基于EIS图谱的电芯一致性分析方法研究与实现

Electrochemical impedance spectroscopy (EIS) holds great promise for assessing battery degradation. Nevertheless, many present methods for extracting battery aging features from EIS data are unsuitable for cells that have very different aging behaviors. Another challenge is that the time complexity of machine learning (ML) models based on full-frequency EIS significantly increases. To tackle these issues, this article investigates two feature extraction methods together with suitable regression algorithms. First, manual feature extraction methods are studied that involve feature extraction via feature correlation and consistency analysis, enhancing the robustness of battery capacity estimation within Gaussian process regression (GPR) and support vector machine (SVM) algorithms. On the other hand, automatic feature extraction methods extract latent features using convolutional neural networks (CNNs) as inputs to the model. Best results are achieved with the CNN-based methods that achieve robust capacity estimation with 2.88% accuracy for different battery cells at varying temperatures while remaining insensitive to EIS measurement uncertainty. This is a factor of 2-3 more accurate than the existing published approaches on the same dataset. By combining the advantages of manual and automatic feature extraction approaches, a reduced EIS dataset is used to train a lightweight CNN model, resulting in an inference time of less than 21.48 ms on STM32L476RG, whereas the existing approaches take up to 50 ms for achieving a similar accuracy.

The sorting of a group of battery cells with great consistency is critial for the safe and durable operation of battery pack. The conventional methods of battery sorting is based on the linear potential sweep methods, which are complicated and coarse. In this manuscript, the electrochemical impedance spectroscopy was used to determine the kinetic process and electrochemical reactions on the electrodes. There are 41 lithium iron phosphate battery cells(15 Ah) were investigated, and an equivalent circuit was developed to fitted the measure EIS spectrum. The dispersion of the fitting parameters and their variations with SOC were discussed, in which solution resistance RH, constant phase elements (CPEWarburg-Y0 and CPEct-N) at 100% SOC to identify and selected a group of cells with great consistency. The results showed that the sorted battery bells exhibit great consistency as compared the whole sample group. This manuscript demonstrated a non-destructive and effective method for battery sorting, which are instructive for the field of power battery and energy storage battery.

The exponential growth in electric vehicles causes similar growth in retired batteries, resulting in a need to evaluate these batteries for possible use in other applications. Methods for fast health characterization are still challenging for batteries. So far, electro-chemical impedance spectroscopy as a frequency domain measurement technique has been mainly used for this purpose at the battery cell level. This paper aims to evaluate the feasibility of EIS on the module level, to see whether it allows obtaining the module’s practical electrical equivalent circuit model (ECM) beyond cell-level modeling. To this end, ECM parameters derived from EIS measurements on cells are compared with those from modules made from these cells. The results show a clear correlation between measuring the EIS on the cell level and the module created by connecting the same cells. This provides a first evidence for the usefulness of conducting EIS measurements at module level with an acceptable range of accuracy and consistency with the results obtained from the individual cells.

To ensure accurate battery capacity estimation over the battery life time, it is important to extract those features from battery data sets that give a good indication of battery capacity degradation. Data obtained from electrochemical impedance spectroscopy (EIS) are a promising route for detecting different aging effects. Many present methods for extracting battery aging features from EIS data are unsuitable for cells that have very different aging behaviour, which leads to low robustness in the battery capacity estimation. To improve battery capacity estimation of cells with significantly different aging behaviour, two methods for feature detection and consistency analysis are proposed for finding the high aging-correlated features in EIS data of these cells. A novel feature-consistency coefficient is proposed to assess whether the detected features are suitable for use in capacity determination. Based on the two new features that are found using this approach on a published data set of 8 battery cells with significantly inconsistent aging behavior, a capacity estimation is subsequently carried out using several advanced machine learning (ML) techniques, using Gaussian process regression (GPR) and Support vector machine (SVM) models. It appears that a third ML method based on automatic feature extraction and capacity estimation using convolution neural networks (CNNs) gives the best, most robust capacity estimation result. All methods presented in this paper significantly outperform GPR-based estimations published in the literature.

Fast and accurate measurement of the lithium-ion (Li-ion) battery electrochemical impedance spectroscopy (EIS) is essential for the safe and reliable operation of energy storage systems. However, existing methods for measuring battery EIS are of low efficiency. Hence, this article proposes a high-precision and fast measurement method for Li-ion battery EIS. In terms of the excitation signal, the optimized sinc pulse signal and linear frequency modulation (LFM) pulse signal (chirp) are introduced for EIS measurement of low- and medium-to-high frequency bands, respectively, and the spectrum is calculated by chirp Z transform (CZT). In terms of the measurement device, the method proposes a terminal voltage balancing capacitor to achieve high-precision measurement of the small terminal voltage variations of the battery. Then, the measured EIS is fit into a fractional-order equivalent circuit model (ECM). Finally, simulations and experiments under different working conditions are conducted to inspect the effectiveness of the proposed method on lithium-iron-phosphate (LiFePO4) battery cell. The duration of the proposed excitation signal is shortened to 7.3% of the traditional frequency sweeping method, the sensitivity of the measurement circuit is improved by about three orders of magnitude compared with the traditional method and the normalized root-mean-square error (NRMSE) of the full-frequency band EIS is within 2%, which indicates that the proposed method can achieve fast and high-precision measurement of battery EIS and provides a guidance for further study of battery ECM parameters.

Electrochemical impedance spectroscopy (EIS) is widely used at the laboratory level for monitoring/diagnostics of battery cells, but the design and validation of in situ, online measurement systems based on EIS face challenges due to complex hardware–software interactions and non-idealities. This study aims to develop an integrated co-simulation framework to support the design, debugging, and validation of EIS measurement systems devoted to the online monitoring of battery cells, helping to predict experimental results and identify/correct the non-ideality effects and sources of uncertainty. The proposed framework models both the hardware and software components of an EIS-based system to simulate and analyze the impedance measurement process as a whole. It takes into consideration the effects of physical non-idealities on the hardware–software interactions and how those affect the final impedance estimate, offering a tool to refine designs and interpret test results. For validation purposes, the proposed general framework is applied to a specific EIS-based laboratory prototype, previously designed by the research group. The framework is first used to debug the prototype by uncovering hidden non-idealities, thus refining the measurement system, and then employed as a digital model of the latter for fast development of software algorithms. Finally, the results of the co-simulation framework are compared against a theoretical model, the real prototype, and a benchtop instrument to assess the global accuracy of the framework.

With the increasing demand for electric vehicles (EVs) and energy storage systems, electrochemical impedance spectroscopy (EIS) has emerged as a promising method for battery pack diagnostics. However, existing EIS research has been predominantly limited to single cells, presenting challenges for practical implementation in actual battery pack systems. In real battery packs, structural elements such as bus plates introduce additional impedance artifacts into measurement data. This parasitic impedance becomes more pronounced as the number of parallel-connected cells increases, degrading measurement reliability. This study presents a systematic analysis of bus plate effects on EIS measurements of parallel battery modules and develops a correction algorithm to extract pure module impedance. Standalone bus plate EIS measurements were conducted to establish geometry-based impedance prediction formulas, and correction factors accounting for current distribution and frequency dependence were derived. The algorithm was validated on 2P-4P parallel modules of NCA and LFP batteries, achieving RMSE reduction from 1.18–2.65 mΩ to 0.10–0.17 mΩ, corresponding to an 88–96% error reduction. These results demonstrate that the proposed algorithm effectively improves module-level EIS measurement reliability regardless of battery chemistry and parallel configuration.

The reliability and safety of retired battery packs require detailed characterization to benchmark their performance and safety. Electrochemical impedance spectroscopy (EIS) is a widely recognized powerful technique in this regard. However, EIS instruments have inherent voltage and current limitations when characterizing battery packs, making direct pack-level EIS measurement challenging compared to single-cell analysis. Furthermore, estimating pack-level EIS from the impedance data of single lithium-ion cell is not straightforward. Since the terminals connecting the cells introduce non-negligible impedance, an efficient computational approach is required to compensate for these effects and accurately estimate pack-level EIS from individual cell data. This paper addresses these limitations in pack-level EIS testing and proposes a new computational method for estimating lithium-ion pack-level impedance using cell-level measurements. A series-connected battery module was analyzed, and EIS was computed with and without modeling terminal impedance. The results demonstrate that accounting for terminal effects improves the accuracy of pack-level EIS estimation by 9.8% compared to when they are neglected.

Lithium-ion battery energy storage systems (ESSs) occupy the majority share of cumulative installed capacity of new energy storage. Consistency of an ESS significantly affects its performance and efficiency. Thus, accurate consistency evaluation for ESSs is vital to the operation maintenance management. This article proposes an integrated framework of evaluating the consistency of battery groups and identifying the inconsistent battery packs. First, low-dimensional feature representations are learned from charge–discharge voltage curves by the approximate low-rank representation (ALRR), which can realize the dimension reduction and also preserve the spatial structure relationships in the low-dimensional subspaces. Second, a hypersphere concentration-based consistency evaluation index for battery groups is constructed with the von Mises–Fisher distribution and optimal representations. Third, the inconsistent battery packs with voltage anomalies are rapidly identified by the hypothesis testing of chi-square statistics. Finally, the proposed method is validated based on real datasets of a battery ESS.

Accurate consistency diagnosis of series-connected battery packs is crucial for the safety management of lithium-ion batteries. However, traditional methods for extracting and analyzing consistency indicators often require significant memory and computing resources, posing a challenge for embedded battery management system (BMS). In this regard, this work proposes a practical method for real-time diagnosis of state of charge (SOC) and capacity consistency. First, a low-complexity online identification method is employed to obtain the open-circuit voltage (OCV) of individual cells. These OCVs serve as raw indicators which are then reconstructed to extract consistency parameters. During OCV reconstruction, the extended Kalman filter (EKF) is employed to iteratively find optimal solutions with less memory and computational consumption. Additionally, the mean shift algorithm (MS) is adapted to enhance robustness and reliability under practical conditions, such as partially available data and inaccurate estimations from EKF. The proposed method is validated on a real BMS at varying temperatures of 5 °C, 25 °C, and 45 °C. The diagnosis errors for capacity and SOC are within 3.2% and 1.6%, respectively, at all temperatures, even with partially available data. Resource consumption analysis demonstrates that the proposed method maintains appropriate complexity and reduced storage requirements, making it ideal for embedded BMS applications.

Retired electric vehicle lithium batteries can be used in microgrids to fully utilize the remaining capacity of lithium batteries. Due to differences in service life and usage environment, the state of charge (SOC) and capacity of retired lithium battery packs in microgrids are difficult to be completely consistent. SOC balancing is an important means to extend the lifespan of retired lithium battery (RLB). However, the traditional virtual synchronous generator (VSG) control scheme and the existing VSG-based SOC balancing scheme fail to achieve the SOC balancing of non-equal capacity RLB in VSG-controlled microgrids. Therefore, a VSG control-based SOC balancing scheme for non-equal capacity RLB is proposed. This paper analyzes the influence mechanism of traditional VSG control and RLB capacity difference in SOC balancing. The influence of capacity on SOC balancing can be eliminated by correcting the traditional VSG control parameters and line impedance. Then, a SOC balancing factor is added to the VSG to regulate the output active power of inverter and achieve SOC balancing. The consistency algorithm is introduced in the process of calculating the SOC balancing factor, which realizes the SOC balancing of the non-equal capacity RLB under the premise of fewer communications and guaranteed frequency quality. In addition, a small signal model for VSG is established to analyze the influence of control parameters on system stability. Finally, the simulation and experimental results verify the effectiveness of the proposed scheme. Compared with the traditional SOC balancing scheme based on VSG, the proposed SOC balancing difference is only 0.0023, the frequency deviation is only 0.02Hz, and the energy loss is reduced by 2.13%, which reflects the advancement of the proposed scheme.

This study investigates calendar-aging mechanisms in lithium-ion batteries, focusing on cathode lithiation due to decomposition of conductive salt and SEI growth, by correlating quantified float currents, capacity loss rates, and pulse resistances with changes in electrochemical impedance spectroscopy (EIS) spectra. Seven Samsung 25R cells are aged at different float voltages with periodic EIS measurements at 30°C. Using a pre-characterization cell, the internal processes via EIS are allocated across various states of charge and temperatures and GITT measurements are performed to derive scaling factors. GITT, float currents and capacity loss rate measurements at 30°C enables the separation of SEI growth I_(SEI growth) and cathode lithiation current I_CL based on float current behavior across a temperature range of 5°C to 50°C. The distribution of relaxation times (DRT) method is employed to deconvolute overlapping electrochemical processes. EIS and DRT analyses showed significant changes in cathode charge transfer resistance and diffusion, confirming that cathode lithiation correlates substantially to elevated internal resistance at high cell voltages. The theory of I_(SEI growth) and I_CL is further supported using inductively coupled plasma atomic emission spectroscopy by quantifying elemental inventory changes and linking phosphorus release and lithium consumption to degradation mechanisms.

To prevent battery thermal runaway for electric vehicles (EVs), it is necessary to figure out and apply the connections between temperature consistency in battery pack (TCBP) and driving condition to achieve accurate evaluation and diagnosis for temperature inconsistency. This article designed and conducted the naturalistic driving experiments on EVs, and the long-term and high-frequency vehicular running data was used to explore the connection characteristics between TCBP and driving condition for the first time. The microtrip method was adopted to divide EVs’ running segments, and 24 driving condition parameters (DCPs) are extracted for each segment. The principal component analysis (PCA) and k-means algorithm were used to cluster segments into congested, moderate, and smooth driving condition (SDC). For the three driving conditions, the correlation between DCPs and variation coefficient of probe temperature (VCPT) was obtained by calculating their maximum information coefficient (MIC). The importance and influence pattern of DCPs to VCPT was analyzed using random forest (RF) model and ALEs plot, and their quantitative effect on VCPT was calculated by data statistics. Moreover, key DCPs preferably used for TCBP estimation or prediction modeling were identified. The research results provide important insights for the development of adaptive threshold-based evaluation and diagnosis method for temperature inconsistency in EVs’ battery pack.

No abstract available

Rapid electrochemical diagnostics, like DC pulse sequences or electrochemical impedance spectroscopy (EIS), have shown promise in isolated studies, but not enough effort has been made to validate these methods for real-world use as diagnostic or end-of-first-life screening tools. To address this gap, we have performed electrochemical characterization tests including rate capability, electric vehicle and stationary storage application cycles, and a variety of DC pulse sequences across the entire SOC window at 15, 30, and 45 oC on 79 total cells of 4 different types with varying manufacturer, chemistry, format, size, and design, aged both in real-world use and in lab testing, resulting in a data set with almost 50,000 independent DC pulse measurements from 259 characterization tests. In addition to tracking cell performance metrics like capacity and efficiency, we have also attempted to quantify safety, using analysis of post-charge voltage relaxation data and physical characterization via X-ray CT scanning performed by Glimpse. Straightforward correlation analysis between various measured quantities reveals interesting relationships, for instance, higher variance of LFP-Gr cell capacity at low discharge rates than at high rates, and format-dependent correlations between physical properties like cell volume and electrode thickness expansion with capacity and resistance. This data set is then used to train machine learning models, where we use a rigorous statistical approach to quantify the distribution of predictive performance on many different targets, as well as investigating the influence of the DC pulse sequence design. We show that that near net-zero energy DC pulse sequences can be used to estimate SOC of NMC-Gr lithium-ion batteries with about 1% mean absolute error (MAE), predict discharge capacity with 2-5% mean absolute percent error (MAPE), and sort high (> 90%) from low (< 80%) discharge capacity cells with less than a 0.3% false positive rate. In contrast, we demonstrate near total failure to predict safety and reliability related targets, like the presence of lithium plating detected by post-charge voltage relaxation or excessive gas generation causing physical deformation of the cell casing, highlighting the critical need for other methods to diagnose safety and long-term reliability of lithium-ion battery systems at scale. We also demonstrate that longer, more complex DC pulse sequences inherently contain more information than shorter, simpler DC pulse sequences, resulting in improved prediction accuracy. Finally, we compare the results here with both EIS-to-capacity predictions as well as other literature using DC pulse sequences to predict capacity. Using four open-source EIS-to-capacity data sets, which includes a direct overlap with one of the cell types studied here, we demonstrate no practical difference between the accuracy of models predicting capacity from either EIS or DC pulses. Some of the potential challenges and opportunities for scaling these methods up from cell- to system-level are discussed. This research highlights both the practical utility of rapid DC or AC diagnostic tests for online monitoring or rapid end-of-life screening of commercial lithium-ion batteries, but also the critical need for rapid, scalable, non-destructive methods to diagnose the safety and reliability of batteries, which cannot be done using rapid electrochemical characterization techniques. Figure 1

The shift toward electric mobility has intensified research on battery technologies that optimize cost, efficiency, and environmental impact. Among various lithium-ion chemistries, lithium-iron-phosphate (LFP) cells have emerged as a promising alternative for many applications due to their inherent safety, longer lifespan, and affordability. Initially overshadowed by high-energy-density nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA) batteries, recent developments have positioned LFP cells as a viable solution for mainstream EV applications due to better thermal stability [1]. This study investigates the electrical and thermal behavior of cylindrical battery cells such as the 26650 LFP and NMC cells. The thermal behavior of individual cells (and, by extension, the battery pack) is substantially dependent on the cell’s electrode characteristics, chemistry, impedance, and boundary conditions. To explore the thermal distribution within a battery pack, a statistically representative pack [2] has been designed consisting of 24 cells arranged in a 4-in-series by 6-in-parallel array where each cell is instrumented with anode and cathode temperature probes with potential measurements at each parallel bus node, as diagrammed in Fig. 1(a). The pack design is made from thermally conductive aluminum with a rectangular block structure containing 24 machined pockets to house individual cells with a spacing of 3 mm between the pockets shown in Figure 1(b). The design minimizes heat coupling between cells and ensures high heat conduction in the pack [3]. In addition, the battery fixture contacts are coated with 30 nm of chromium and 300 nm of gold to enhance electrical conductivity and minimize fixture contact resistances. Since heat generation is more pronounced in the cathode during high discharge rates, effective thermal management strategies are essential to maintain performance and prevent overheating. To accurately monitor temperature variations at the electrode level, thermocouples are integrated into the fixture at each electrode. Heat-distributing layers composed of graphite and aluminum plates enhance heat dissipation. The inclusion of a thin graphite sheet facilitates lateral heat distribution across the surface, improving overall thermal regulation. The protocol for electrothermal cell characterization incorporates the Hybrid Pulse Power Characterization (HPPC) [4] test that is widely recognized as a practical and efficient approach for evaluating the operational response of battery packs under real-world operating conditions. The HPPC test is used to determine the resistive characteristics of a battery, quantify its energy efficiency, and evaluate the state-of-charge (SOC)-dependent behavior. The Enhanced Hybrid Pulse Power Characterization (EHPPC) [5] test has emerged as a novel adaptation of the HPPC to optimize the estimation of Equivalent Circuit Model (ECM) parameters by integrating Crate dependence while reducing the overall duration of the testing. The EHPPC test applies short-duration multiple C-rate charge and discharge pulses to simulate actual load conditions encountered in applications. The data obtained from this test has been shown to yield ECM parameters for battery performance assessment as shown in Figure 2(a), demonstrating improved model performance over wide operating temperatures (0 0 C and 45 0 C), implicating the influence of C-rate variations. The electrochemical impedance spectroscopy (EIS) test can demonstrate an improved ECM parameter extraction capability [6]. EIS tests can be used to derive an additional ECM component (a Warburg element) that standard EHPPC or HPPC testing cannot derive. It provides critical insights into the diffusion of ions within the electrode region, which directly influences the current collection efficiency in the collector. An initial EIS test was conducted prior to any EHPPC test. The corresponding Nyquist plot is presented in Figure 2(b). During the module and pack level test conducted by Wildfeuer et al.[7], the tab contact resistance, cell-to-cell connection resistance and temperature gradient all influenced the impedance spectrum. Therefore, determining an accurate equivalent circuit model of the pack from the EIS test is challenging. This study aims to improve the ECM parameters and find the correlation of measurements using the EHPPC and EIS tests. Characterizing the scaled up battery pack and impedance analysis for different levels of SOC (ranging from 0% to 100%) and operating temperatures (0 0 C, 25 0 C, and 45 0 C) with both tests enhances the ECM parameter predictions. This work anticipates improving the understanding of the evolution of resistances with varying C-rates and environmental conditions, enabling more accurate battery ECM and optimizing robust battery management system (BMS) algorithms. Acknowledgements: DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. OPSEC9575. References [1] https://doi.org/10.1109/EVS.2013.6914893. [2] https://doi.org/10.3390/batteries9090437. [3] https://doi.org/10.1016/j.apenergy.2016.07.094. [4] United States Advanced Battery Consortium Battery Test Manual For Electric Vehicles. U.S. Department of Energy, Report INL/EXT-15-34184, rev. 3.1, Oct 2020. [5] https://www.proquest.com/dissertations-theses/enhancing-lithium-ion-battery-equivalent-circuit/docview/2915495858/se-2. [6] https://doi.org/10.1016/j.est.2024.111167. [7] https://doi.org/10.1109/EVER.2019.8813578. Figure 1

Electrochemical impedance spectroscopy (EIS) is one of the most widely used techniques for battery monitoring and characterization. However, EIS measurement is a time-consuming process, since it must be performed after the battery relaxation time interval. In this article, a method for performing fast broadband EIS during battery relaxation by compensating for the effect of the transient is proposed. The approach is based on the local rational method (LRM), which is a nonparametric frequency-domain system identification technique, and eliminates the need for long waiting time before starting the measurement process. The proposed approach is validated by numerical simulations and experiments, proving its capability of compensating the effect of the transient and outperforming other nonparametric techniques, such as the local polynomial method (LPM). In particular, experimental tests performed on a 18650 lithium-ion battery show that the proposed flexible LRM approach is capable of compensating the transient behavior and providing usable EIS estimates immediately after the battery discharge is finished. This behavior is demonstrated using a broadband multisine excitation signal of 20 s duration, spanning a frequency range from 50 mHz to 100 Hz.

This paper presents a novel onboard solution for evaluating and testing end-of-life (EoL) batteries, facilitating their efficient reuse and recycling. The proposed framework leverages machine learning (ML) algorithms to predict the State of Health (SoH) from Electrochemical Impedance Spectroscopy (EIS) data and classify batteries for potential second-life applications. To achieve this, we trained models using EIS and other relevant data obtained from testing conducted by various organizations, ensuring accurate SoH predictions and suitability assessments for reuse or recycling. The ML models were implemented on an STM32 platform, enabling real-time, onboard battery evaluation. Additionally, cloud connectivity can be integrated for comprehensive database management, remote monitoring, and accessible classification results. This research demonstrates the feasibility of using ML for onboard battery evaluation and testing, with the potential to significantly transform battery management and sustainability efforts. It has profound implications for resource optimization, waste reduction, and the development of circular economies within the battery industry.

The widespread adoption of electric transportation is leading to the accumulation of spent lithium-ion batteries requiring sorting prior to recycling or reuse. Effective triage of end-of-life (EoL) batteries necessitates rapid non-destructive diagnostic methods capable of assessing the state of health at the individual cell level. This study proposes an approach to rapid diagnostics based on electrochemical impedance spectroscopy. Using a 10S6P battery pack from an electric scooter operated under sharply continental climate conditions as an example, we demonstrate that equivalent circuit model parameters – including ohmic resistance, charge transfer resistance, and constant phase element exponents – enable the identification of intra-pack degradation heterogeneity and classification of cells according to their suitability for reuse. Analysis revealed two distinct degradation patterns: localized critical discharge attributed to battery management system malfunction, and a gradient of accelerated aging in peripheral cells due to thermocyclic stress. It was established that 80% of the cells in the investigated pack retain characteristics permitting second-life applications, while 10% exhibit signs of irreversible degradation and require immediate recycling. The results confirm the promise of impedance spectroscopy as a tool for high-throughput triage of EoL batteries within the circular economy framework.

No abstract available

Efficiently sorting large-scale retired batteries is essential for their echelon utilization. Speed and accuracy are critical factors that influence the economic viability, recycling potential, and safety of this process. Current methods, which involve extensive testing and multiple procedures, tend to be overly complex and time-consuming. Thus, enhancing cost-effectiveness in the sorting process while maintaining accuracy presents a significant challenge. This paper introduces a method based on Electrochemical Impedance Spectroscopy (EIS), where features that characterize the consistency of retired batteries are extracted from EIS data. A multilayer perceptron (MLP) model is employed to estimate both SOH and direct current internal resistance (DCIR), which are subsequently utilized in the Autoencoder-FINCH (AEFINCH) clustering algorithm for final sorting. The effectiveness of the proposed method has been validated using a self-tested dataset comprising 381 retired lithiumion batteries. In comparison to state-of-the-art sorting methods, the proposed method achieves reductions of up to 22.06% in SOH standard deviation and 16.74% in DCIR standard deviation.

At Forschungszentrum Jülich a set of over 2600 electrochemical impedance spectroscopy (EIS) measurements, conducted on 15 individual solid oxide cell (SOC) stacks was consolidated. The stacks were subjected to varying operational modes and conditions, but consistently for the purpose of degradation research. Material-wise the stacks comprised similar properties utilizing fuel electrode supported cells by the commercial manufacturer Elcogen (Tallinn, Estonia) and the in-house F10-design. 1 The EIS measurements conducted during these experiments were uniformly pre-processed with a specifically designed curation procedure to compensate for recurring instabilities and parasitic inductive impedances. 2 Dominant patterns providing a low-dimensional representation of every EIS measurement could be identified by singular value decomposition of the EIS data set. 3 These information-rich patterns, referred to as eigenspectra, facilitate efficient data handling in terms of data exchange and the training of data-driven models. Furthermore, the eigenspectra can be used as a reconstruction basis U r in which every EIS measurement in the data set corresponds to a unique r -dimensional representation. 3 A straightforward advantage of this property is the usage as a filter to compensate for process-related noise during EIS by calculating the corresponding reconstruction of such an EIS measurement (cf. Fig. 1-A). Process-related noise during EIS can arise from fluctuations in the steam supply, induced by the steam generator, for instance. A less obvious advantage is the identification of a set of tailored frequencies f* from the eigenspectra which allows the reconstruction of complete EIS spectra from sparse measurement of only these selected frequencies f* (cf. Fig. 1-B). 3 The accuracy of such approaches sensitively depends on the rank r of the reconstruction basis , i.e. the number of eigenspectra considered. Common methods from the literature were used to find an optimal value for r . Depending on the measurement conditions, accurate reconstructions can be reported for r = 6...12 . Due to the theoretically separate measurement of the real and imaginary parts of the impedance, the number of frequencies required for the reconstruction of whole EIS spectra from sparse measurements could be reduced to |f*| = r/2 . In addition, the frequencies f* were found to coincide with typical ranges of distribution of relaxation times (DRT) peak-to-process-attribution, supporting the physical relevance of the identified tailored frequencies. To verify the proposed method for application in future experiments it was validated for held-out measurements conducted on an additional SOC stack with similar material properties. Thus, accurate reconstructions of EIS measurements conducted at 700°C in dry hydrogen can be reported for the sparse measurement of only three frequencies. Furthermore, the reconstruction of EIS from sparse measurements was investigated for EIS containing known faults such as temperature and feed gas flow induced drifts during the measurement as well as for measurements exhibiting a degradation of electric contact within a repeating unit of the stack and an external leakage. Though six instead of three tailored frequencies are necessary, accurate reconstructions could still be determined even under the adverse conditions. Despite being underrepresented in the data set, the reconstruction of EIS measurements conducted under load, i.e. with high DC offsets of the excitation current up to 1 Acm -2 , was found to be accurate when employing the identified three to six tailored frequencies f* , depending on the measurement conditions. These findings suggest the potential application of the developed method as a time efficient alternative for conventional EIS measurements during operation of SOC stacks including productive operation. We hypothesize that this should enable simplified EIS measurements during load operation to be carried out at significantly shorter time intervals. This would considerably facilitate data-driven modelling for degradation prediction by using the sparse EIS measurements for regularly updating the prediction model. However, it is important to note that the relevance and accuracy of the reconstructed EIS measurements are contingent on the similarity of the SOC stacks comprised in the underlying data. 3 As a technically favorable and cost-efficient solution the use of a power supply instead of dedicated EIS hardware for the implementation of EIS is proposed. Such an experimental setup has already been investigated simulatively for an alkaline electrolyzer, by utilizing the IGBT-based power converter. 4 In the present study the impedance is determined only at those frequencies f* previously identified as optimal, and the entire spectrum is reconstructed based on the described method. This approach has the potential to significantly minimize the measurement effort and enhance resilience against instationarities in temperatures and gas flows during the measurement. For this purpose, the modulation of suitable excitation signals is tested using the arbitrary waveform generator in a CAENels FAST-Bi-1K5 power supply. Together with the embedded oscilloscope three to six tailored frequencies f* are measured utilizing only the power supply unit and EIS spectra are reconstructed from this sparse measurement. The application of the proposed approach is verified for an SOC stack with in-house F10-design 1 and fuel electrode supported cells by the manufacturer Elcogen. The obtained measurements are compared to validation measurements conducted with a Zennium X potentiostat, power potentiostat PP212 and EL1002 load devices from the manufacturer Zahner Elektrik (Kronach, Germany). To date, the impedances at the tailored frequencies f* from conventionally measured EIS have been utilized for validation purposes, and the resulting reconstruction has then been compared to the conventionally measured EIS. Due to the preconditioning of these EIS measurements and the curation of parasitic inductive impedances, measurement-induced artefacts were not limiting the reconstruction capabilities. However, with the proposed experimental setup, such instabilities and parasitic inductances may now have to be taken into consideration, as the prior curation of such a sparsely measured EIS is not a viable option anymore. Consequently, the determination of the tailored frequencies f* is also carried out for the original EIS data set without prior curation. A preliminary investigation indicates a significant influence on the distribution of those frequencies f* due to curation of the underlying data set. As exemplified in Fig. 2 for a subset of 734 EIS measurements conducted at 700°C in dry H 2 both with and without prior curation, the frequency ranges yielding an optimal reconstruction quality partially deviate. This affects the proposed reconstruction method as the tailored frequencies f* are determined as the mean of the depicted distributions. Therefore, the curated and the original data set are used to determine the tailored frequencies f* and resulting reconstruction qualities for EIS measurements with and without parasitic, inductive impedances and measurement instabilities are compared. In addition, feasible extensions of the data set are proposed to facilitate high reconstruction accuracies in all scenarios. Thus, potential avenues are elaborated for integrating sparse EIS measurements at stack level and during productive operation into existing test benches, thereby ensuring a cost-effective and time-efficient diagnostic method. Acknowledgement The authors would like to thank their colleagues at Forschungszentrum Jülich GmbH for their great support and the Helmholtz Society as well as the the German Federal Ministry of Education and Research for financing these activities as part of the WirLebenSOFC project (03SF0622B) as well as the PHOENIX project (03SF0775A). References [1]: Blum L., Buchkremer H., Gross S., Gubner A., de Haart L.G.J.B., Nabielek H., Quadakkers W.J., Reisgen U., Smith M.J., Steinberger-Wilckens R., Steinbrech R.W., Tietz F., Vinke I.C. Solid oxide fuel cell development at Forschungszentrum Juelich. Fuel Cells 7 (3), 204-210 (2007). [2]: C. Mänken, J. Uecker, D. Schäfer, L.G.J. (Bert) de Haart and R.-A. Eichel. Impact of Electrochemical Impedance Spectroscopy Dataset Curation on Solid Oxide Cell Degradation Assessment. J. Electrochem. Soc. 171 (6), 064503 (2024). [3]: C. Mänken, D. Schäfer and R.-A. Eichel. The exploitation of eigenspectra in electrochemical impedance spectroscopy: Reconstruction of spectra from sparse measurements. J. Power Sources 628 , 235808 (2025) [4]: A. Sanchez-Ruiz and M.T. de Groot. High-power electrolyzer characterization via smart power converters . Int. J. Hydrogen Energy 96 , 1243-1250 (2024). Figure Captions Figure 1: EIS conducted in humidified H 2 at 700°C together with reconstructions using four different ranks r of U r (A). Reconstruction of held-out EIS from sparse measurement at tailored frequencies f* for different measurement conditions (B). (Reproduced from [3], which is licensed under CC BY, © 2025 by the authors) Figure 2: Bootstrap distribution of frequencies identified as optimal for reconstruction of 734 EIS measurements conducted at 700°C in dry H 2 both w/ and w/o prior curation of the EIS dataset. Tailored frequencies f* determined as mean of individual distributions. Figure 1

Lithium Ferro Phosphate (LiFePO4) batteries are a primary choice for electric vehicles and energy storage systems due to their superior thermal stability and extended cycle life compared to conventional batteries. However, their performance is influenced by factors such as electrochemical characteristics, temperature, charge/discharge rates, and usage patterns. This study analyzes the LiFePO4 AMP20m1HD-A cell using electrochemical impedance spectroscopy (EIS) at State of Charge (SOC) levels of 20–90% and discharge rates (C-rate) ranging from 0.5C to 3C under ambient conditions. The battery cell modeling is based on the equivalent circuit model (ECM) approach, which consists of a combination of inductance, ohmic resistance, two pairs of R-CPE components, and a Warburg element to represent charge transfer dynamics and ion diffusion. Parameter fitting of the ECM using the non-linear least squares method yielded an average Root Mean Square Error (RMSE) of 3.87×10−6 Ω, indicating high accuracy. Model validation at 80% SOC (0.5C) and 20% SOC (3C) resulted in an RMSE of 5.74×10−6 Ω, demonstrating consistent impedance representation across operating conditions. The results reveal that SOC and C-rate variations significantly impact electrochemical characteristics, particularly resistance and capacitance. These findings emphasize the importance of accounting for operational variability in Battery Management System (BMS) design to optimize battery performance and longevity. This research provides a foundation for precise, application-oriented modeling to advance LiFePO4-based battery management systems.

Electrochemical Impedance Spectroscopy (EIS) is a widely used technique for analyzing the resistive and diffusive properties of lithium-ion cells. The most used conventional EIS model assumes semi-infinite diffusion occurring only within the electrolyte 1 , which does not accurately reflect the real redox species concentration profile inside a working battery. Furthermore, in such a model, both the reduced species and the oxidized species are assumed to be present within the electrolyte. In this study, we develop a more realistic EIS model that considers a finite-length concentration profile and accounts for diffusion of oxidized species (Li + ion) in the electrolyte and the reduced species (Li) within the electrode. By using Fick’s law with appropriate boundary conditions and initial conditions, we incorporate spatial and temporal variations in redox species concentration across the cell. Thus, our model provides a more accurate representation of mass transfer and impedance behavior in practical lithium-ion cells. Our findings highlight key differences between the two models. In our model, Warburg impedance affects the entire frequency range, whereas in the traditional model, it is primarily significant at low frequencies. Additionally, in our model, low-frequency impedance is influenced by lithium diffusion into the electrode, which is not accounted for in the semi-infinite diffusion model due to its underlying assumptions. We compared our model's results with those obtained from the conventional semi-infinite model using EIS data from an NMC622||Li half-cell at different states of charge (SOC). The comparison revealed that the conventional method tends to overestimate resistance values by 20-40%, particularly charge transfer resistance (R CT ) and solid electrolyte interphase resistance (R SEI ), due to its simplified assumptions. Such huge differences can have huge impacts on the electrode/electrolyte material choices that the Li-ion battery community makes. Our model successfully reduces to the classical semi-infinite model in the high and low-frequency limits, ensuring consistency with well-established EIS principles while improving accuracy under real-world battery conditions. This refined model provides deeper insights into mass transfer and impedance behavior, which are critical for optimizing battery performance, predicting degradation mechanisms, and enhancing the reliability of impedance-based diagnostics. References: Allen J. Bard and Larry R. Faulkner, Electrochemical methods : fundamentals and applications , John Wiley & Sons, Inc.-2nd ed. Acknowledgement: This work was supported by Clemson University’s Virtual Prototyping of Autonomy Enabled Ground Systems (VIPR-GS), under Cooperative Agreement W56HZV-21-2-0001 with the US Army DEVCOM Ground Vehicle Systems Center (GVSC). DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. OPSEC9602 Figure 1

No abstract available

Given the widespread use of Li-ion batteries in consumer electronics, electric vehicles, and energy storage applications, the study of battery monitoring systems (BMS) has received increasing attention [1]–[2]. Battery voltage has been the primary indicator for assessing battery state of charge (SoC) and state of health (SoH) in previous work. However, there are several remaining key challenges in optimizing BMS measurement accuracy and extending its functionalities under electrically and environmentally hostile conditions. Firstly, the discharge curve of Li-ion batteries is flat, so battery voltage detection with minimal total measurement error (TME) becomes an essential prerequisite for high-accuracy SoC evaluation. This requires careful control of all potential error sources in the analog front-end (AFE) circuit to achieve high accuracy over a wide temperature range. In addition, connecting multiple Li-ion cells in series generates high common-mode (CM) DC voltages, making high-precision AFE design even more challenging. High-voltage (HV)-tolerant DMOS transistors can be used at the input to withstand high CM voltages [2], and the high input impedance of DMOS can also suppress the gain error caused by filter resistors (Fig. 1). However, this solution requires a charge pump to generate an even higher voltage (7.5V higher than the top cell voltage) for the amplifier, making the supply rail design even more complicated. Furthermore, voltage measurement alone is not sufficient to promptly assess the SoH of battery cells [3]. By the time voltage or temperature anomalies become apparent, it is too late to avert the consequences of irreversible heating or even explosion. Last but not least, electrochemical impedance spectroscopy (EIS) has been proposed as a new paradigm for evaluating battery SoH [3], but existing EIS techniques in BMS rely on additional equipment and do not provide real-time monitoring capabilities.

Lithium-ion batteries play a crucial role in the energy transition, particularly for electric vehicles and renewable energy storage systems. Accurate estimation of battery electrical parameters is essential to ensure effective management of its state of charge (SoC) and state of health (SoH). The 2RC Electric Equivalent Circuit Model is commonly employed, yet its parameter identification is subject to uncertainties that can impair the accuracy of predictions. In this paper, we propose an artificial intelligence-based approach for the detection and correction of anomalies in the estimation of 2RC parameters. Our method relies on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for parameter clustering, followed by anomaly detection using the Z -score. Corrections are then applied via a decision tree, which enhances the reliability of the identified parameters. The results demonstrate that the proposed method reduces the number of anomalies in 2RC parameters. A test case scenario showed that 17% of parameters identified by a classical modelling approach were anomalies. The proposed method correct them all leading to a more physical behaviour of model simulations therefore improving model robustness. This approach contributes to enhanced battery management and enhances the performance of energy storage systems.

Accurate evaluation of the severity of external short-circuit (ESC) faults in li-ion batteries is critical to ensuring the safety and reliability of battery systems. This study proposes a novel ESC fault assessment method based on electrochemical impedance spectroscopy (EIS) and differential feature extraction from relaxation time distributions. By comparing EIS responses before and after the short circuit, differential curves are constructed, and relevant peak descriptors are extracted to form physically interpretable feature vectors without requiring equivalent circuit modeling. Standardized feature data are further analyzed using principal component analysis (PCA) and K-Means clustering to perform unsupervised classification of fault severity. In addition, a differential evolution algorithm is employed to adaptively optimize the feature weights, enhancing the monotonic correlation between the weighted scores and actual short-circuit durations. The resulting SeverityScore provides an interpretable, mechanism-driven indicator of ESC fault severity. Experimental results demonstrate that the proposed method effectively distinguishes between mild and moderate short-circuit conditions and generalizes well across four independent battery groups. The model, trained on a single group, demonstrates strong robustness by accurately classifying the fault severity for three unseen validation groups. This data-driven framework offers a robust and model-free approach for fault evaluation, providing a promising tool for health monitoring and risk assessment in li-ion batteries.

This research work focuses on implementing Data Science techniques for Battery Energy Storage Systems (BESSs) according to Health and Charge indicators based on End-of-Life (EOL) criteria. A simple Equivalent Circuit Model (ECM) is implemented to illustrate the behavior of a battery cell in parallel with numerical methods. Density-based spatial clustering of Applications with Noise (DBSCAN), Ordering Points to identify the Clustering Structure (OPTICS), and Local Outlier Factor (LOF) are the Unsupervised techniques implemented for anomaly detection, while Multi-Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) are the Supervised algorithms executed for diagnostics. Similar results are obtained in the outlier analysis, additionally, Supervised algorithms show a high level of performance and provide a basis for determining the capacity of the battery based on the End-of-Life criteria (EOL) of a Battery Energy Storage System (BESS).

This paper presents a deep learning-based framework for the automated extraction and classification of equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data, aiming to enhance lithium-ion battery (LIB) diagnostics. EIS provides detailed insights into internal battery mechanisms, such as charge transfer, diffusion, and double-layer capacitance, by analyzing frequency-dependent impedance responses. Traditional interpretation methods are labour-intensive and limited in scalability. To address this, a one-dimensional convolutional neural network (1D-CNN) is employed to classify EIS spectra into four distinct ECM classes using features derived from real and synthetic datasets. The model architecture incorporates hierarchical convolutional layers, dropout, batch normalization, and global average pooling, achieving a classification accuracy of 95.65% on the test set. The predictions align closely with Nyquist plot characteristics of each ECM, validating the interpretability and robustness of the model.

The equivalent circuit model (ECM) is widely used in lithium-ion battery management systems because of its well-balanced simplicity and accuracy. However, it remains unclear whether the ECM can accurately model the dynamics of parallel-connected battery cells when the individual cells are heterogeneous. To address this issue, an approximate-parameter ECM (AECM) has been proposed to represent the input–output characteristics of parallel batteries with inconsistent capacity and internal resistance in the frequency domain. Additionally, a first-order ECM for parallel individual cells has been established for comparison. The transfer functions of the two models are calculated to analyze how model errors change with increasing inconsistencies among cells in parameters such as individual cell capacity and internal resistance. Finally, electrochemical impedance spectroscopy is used to validate the errors of the AECM under different operating conditions. Theoretical analysis and experimental results indicate that even with a 6% capacity variation in a battery pack with heterogeneous cells connected in parallel, the AECM can still accurately characterize the battery pack dynamics with an error of less than 2% under various conditions, including several representative operation profiles for batteries in electric vehicle and power system applications, demonstrating the broad effectiveness of the proposed AECM.

No abstract available

Summary Existing methods for interpreting Electrochemical Impedance Spectroscopy data involve various models, which face significant challenges in parameterization and physical interpretation and fail to comprehensively reflect the electrochemical behavior within batteries. To address these issues, this study proposes a Temperature-Controlled Second-Order R-CPE Equivalent Circuit Model to capture the non-ideal capacitive characteristics of electrode surfaces. Additionally, the study employs a Copula based Joint Mixture Weibull Model and multi-output Gaussian Process Regression to enhance the precision in capturing the distribution of battery electrochemical parameters and predict SoH curves. Experimental validation shows that the model used in this article has an average RMSE error of 8.5%, and the prediction of the SoH curve after the 100th cycle can achieve an average RMSE error of 9.2%. These findings provide insightful implications for understanding the electrochemical complexities and parameter interdependencies in the battery aging process, offering a robust framework for future research in battery diagnostics.

No abstract available

Recently, lithium-ion batteries have received considerable attention owing the rapid growth of the electric vehicles (EVs). However, batteries face considerable challenges in dynamic simulation that responsible from battery health assessment to battery fault diagnosis, traditional methods deploy experimental procedure that are more expensive, need high computational power and time consuming. To overcome this problem a grey wolf optimizer (GWO) are used in extracting battery parameters of the non-linear ECM model with 45 parameters to create an accurate second order model of the battery, this method used the charge and discharge battery I-V curve to estimate the battery parameters with less computational power. The method effectiveness is tested under different temperatures scenarios $(25^{\circ} \mathrm{C}$ and $\left.35^{\circ} \mathrm{C}\right)$ for both lithium iron phosphate (LFP) and the other on lithium nickel cobalt manganese oxide (NMC).

Grid-connected lithium-ion battery energy storage system (BESS) plays a crucial role in providing grid inertia support. However, existing equivalent circuit models (ECM) cannot accurately represent the battery's impedance in the inertia support working condition (ISWC). Thus, this article proposes a novel negative resistor-based ECM for BESS in ISWC. First, the principle of grid inertia support of BESS is analyzed, and the typical sustaining current profiles in ISWC are obtained. Then, the distinguished battery impedance characteristic in ISWC is experimentally obtained. To capture the special impedance characteristic, a resistor-capacitor (RC) branch with negative resistor is added to the 1-RC model (Thevenin model). Moreover, based on the difference in impedance characteristics between the conventional RC branch and the negative resistor RC branch, this article proposes a parameter separation method based on impedance characteristics to obtain an improved ECM. A comparative analysis reveals that the improved ECM outperforms conventional models, achieving an average relative error below 0.06% compared to experimental results. The improved ECM offers a simple structure, reduced computational burden, and enables rapid analysis of battery state in ISWC. Furthermore, a new simplified negative resistor-based model without altering the conventional RC model's structure is proposed. It is simple in structure with only three parameters, significantly reducing computational costs while maintaining accuracy.

The design of an efficient thermal management system for a lithium-ion battery pack hinges on a deep understanding of the cells’ thermal behavior. This understanding can be gained through theoretical or experimental methods. While the theoretical study of the cells using electrochemical and numerical methods requires expensive computing facilities and time, the Equivalent Circuit Model (ECM) offers a more direct approach. However, upfront experimental cell characterization is needed to determine the ECM parameters. In this study, the behavior of a cell is characterized experimentally, and the results are used to build a second-order equivalent electrical circuit model of the cell. This model is then integrated with the cooling system of the battery pack for effective thermal management. The Equivalent Circuit Model estimates the internal heat generation inside the cell using instantaneous load current, terminal voltage, and temperature data. By extrapolating the heat generation data of a single cell, we can determine the heat generation of the cells in the pack. With the implementation of the ECM in the cooling system, the coolant flow rate can be adjusted to ensure the attainment of a safe operating cell temperature. Our study confirms that 14% of pumping power can be reduced when compared to the conventional constant flow rate cooling system, while still maintaining the temperature of the cells within safe limits.

Li-ion batteries' complex and dynamic electrochemical behavior pose a significant challenge for their diagnostics and prognostics. Electrochemical Impedance Spectroscopy (EIS) is a valuable measurement technique that provides crucial insight into battery behavior. Transforming the EIS impedance data across various frequencies into equivalent electrical circuit elements tailored to the battery's physical and chemical characteristics is valuable for interpreting this data. Distribution of Relaxation Times (DRT) analysis is such an approach and results in a distribution of time constants that effectively characterize the RC networks of the battery's equivalent circuit model (ECM). Modeling the battery with detailed consideration of EIS data and minimizing the information that is lost in this modeling stage is the crucial advantage of the DRT technique. In this work, we present a fully automated procedure for modeling a substantial volume of EIS data using DRT. This involves the automatic selection of prominent peaks that persist throughout the entire battery aging process, effectively enhancing computational efficiency. It furthermore includes data pre-processing steps and frequency range selection, making DRT analysis more efficient and robust by employing the least squares fitting (LSQF) algorithm. The findings in applying our procedure to published EIS spectra of Li-ion battery cells indicate that the presented approach yields a high level of precision in tracking the ECM parameters to analyse the degradation of these cells. Further proof of the quality of our procedure lies in the consistent low dispersion of the various fits, which remain consistent across the entire dataset.

To accurately predict the State of Health (SOH) of lithium-ion batteries under the continuously changing charging and discharging conditions in practical applications, this study proposes a hybrid modeling approach that integrates a Fractional Order Equivalent Circuit Model (F-ECM) with the AutoGluon automatic machine learning framework. By leveraging Electrochemical Impedance Spectroscopy (EIS) to capture battery frequency response characteristics, F-ECM accurately fits EIS data to extract detailed internal state parameters. The integration of AutoGluon automates the machine learning process, enhancing the precision of SOH predictions. Through testing and analysis on real battery datasets, this method has demonstrated superior prediction precision and computational efficiency compared to existing mainstream modeling approaches. Specifically, the hybrid method achieved a Root Mean Square Error (RMSE) of 2.12% and a Mean Absolute Error (MAE) of 1.67%. This study presents a highly accurate, interpretable, and adaptable predictive framework for lithium-ion battery health assessment, offering valuable insights for battery health management system development.

Lithium-ion battery has become a promising energy storage device because of its long life cycle, good stability, high energy density and voltage platform. Electrochemical impedance spectroscopy (EIS) is an electrochemical measurement method with small amplitude sine wave potential as disturbance signal. It is a frequency domain measurement method, which studies the electrode system with the impedance spectrum with a wide frequency range, so it can get more dynamic information and structure information of electrode interface than other conventional methods. Consistency of lithium-ion battery packs is a problem that runs through the whole life cycle of battery packs. With the continuous popularization of electric vehicles, the balanced maintenance of lithium-ion battery packs during after-sales and secondary gradient utilization is particularly important. In this paper, the basic principle of electrochemical impedance spectroscopy (EIS), the interface reaction mechanism between electrolyte and electrode materials, and its application in condition monitoring, anode materials and cathode materials research of lithium ion batteries are described, so as to improve the performance and prolong the life of batteries.

Accurate extraction of equivalent circuit model (ECM) parameters is essential for aging-aware battery management in lithium-ion batteries. Electrochemical impedance spectroscopy (EIS) offers detailed insight into ohmic resistance, charge-transfer kinetics, and diffusion processes, but its onboard application is constrained by unstable measurement conditions. This work introduces a diagnostic window at 100% SOC immediately after the constant-voltage (CV) phase, where interfacial stabilization and kinetic relaxation yield quasi-equilibrium suitable for reproducible impedance measurements. A short post-CV rest is included only as a verification step to confirm minimal voltage drift. Validation was performed on three cells representing pristine, moderately aged, and heavily aged states. Galvanostatic Intermittent Titration, conducted at a low C/25 rate, provided a laboratory benchmark, while EIS was carried out at 0%, 50%, and 100% SOC under controlled rests. Comparative analysis showed strong consistency in ohmic resistance across techniques, while EIS demonstrated superior resolution of charge-transfer and diffusion processes, particularly in aged cells, thereby making it suitable for real-time evaluation. These findings establish the CV-based diagnostic window as a reproducible, diagnostically rich, and onboard-compatible method for ECM parameter tracking and aging diagnostics.

In the long-term operation of a megawatt-scale energy storage plant composed of series-parallel connections, the single batteries will have different degrees of inconsistency problems. To solve this problem, this paper proposes a comprehensive assessment method based on the consistency of batteries in scaled energy storage power stations. According to the consistency indexes such as single battery voltage and voltage polarity difference in series connection between modules, the consistency evaluation criterion of battery monomer was established. Based on the historical data of a battery energy storage system, the consistency evaluation criterion of a single battery is used to analyze the consistency of a single battery of an energy storage power station at different charging and discharging stages. The results show that this method can accurately assess cell consistency. It provides data support for the health status assessment of energy storage plants and reduces the systematic operational risk of energy storage plants.

No abstract available

Digital battery passports are being adopted to provide traceable records of lithium-ion batteries across their lifecycle, credible performance, and durability. However, it requires continuous diagnostics rather than lab-based tests and conditions. This review establishes a materials-informed system that links (i) battery-passport frameworks, (ii) cell-level design, and (iii) online electrochemical impedance spectroscopy (EIS) observables. Therefore, a chemistry-aware indicator set is proposed for passport reporting that relies on capacity and impedance indices, each accompanied by explicit tests. A review of the common and commercial LIBs (LCO, NCA, NMC, LMO, LFP) explains differences and characteristics. In addition, online EIS is reviewed, and different techniques for battery online diagnostics and state estimation are described, with details on how this online analysis is incorporated into the battery passport framework. This review covers the battery passport framework, the materials used in commercial batteries that must be documented and traced, and how these materials evolve throughout the degradation process. It concludes with the state of the art in online battery cell inspection, which enables comparable health reporting, conformity assessment, and second-life grading. Finally, it outlines key implementation priorities related to the reliability and accuracy of battery passport deployment and online battery diagnostics.

Electrochemical impedance spectroscopy (EIS) holds significant potential for evaluating battery degradation. However, EIS readings are not only affected by battery degradation but also by the state of charge (SOC). Traditional models for estimating battery capacity rely on impedances measured at specific SOC points, and thus can suffer from substantial inaccuracies when SOC estimation errors occur. To tackle this challenge, we propose a novel partial-range SOC-insensitive model for precise battery capacity estimation using transformer neural networks complemented by an EIS change pattern model based on the k-nearest neighbors (KNN) algorithm. To the best of our knowledge, this is the first study to develop an EIS-based battery capacity model that considers incorrect SOC scenarios. Test results show that our partial-range SOC-insensitive model can estimate battery capacity with a root-mean-square percentage error of 2.69%, even with a 30% SOC error, within the SOC range of 20% to 50%. Adding the EIS change pattern recognition model further improves the performance of the partial-range SOC-insensitive model, reducing the maximum absolute percentage error from 19% to less than 3% in scenarios involving 50% to 70% SOC error during battery cell testing.

This paper presents a 14-channel voltage measurement unit (VMU) with fully integrated high-pass filters (HPFs) for electrochemical impedance spectroscopy (EIS) systems in automotive battery management applications. Conventional EIS systems require bulky off-chip RC HPFs to achieve the sub-Hz cut-off frequencies necessary for accurate battery characterization, significantly increasing system cost and complexity. The proposed VMU eliminates this limitation through on-chip HPFs with pseudo-resistor implementation featuring adaptive bias control that compensates for process and temperature variations across AEC-Q100 Grade 1 conditions ($-40^{\circ} \mathrm{C}$ to 125° C). The adaptive control system combines process corner detection with temperature-dependent current modulation using proportional-to-absolutetemperature (PTAT) and complementary-to-absolutetemperature (CTAT) voltages with optimized coefficients. This approach reduces pseudo-resistor variation from $\mathbf{1 5 9. 0 ~ G} \boldsymbol{\Omega}$ to $15.8 \mathrm{G} \Omega$ across process corners and from $104.4 \mathrm{G} \Omega$ to $33.2 \mathrm{G} \Omega$ across AEC-Q100 Grade 1 temperature range ($-40{ }^{\circ} \mathrm{C}$ to 125 °C), representing an 83.7 % improvement in stability. Designed in $\mathbf{0. 1 8}-\boldsymbol{\mu}$ m BCD process, each integrated HPF occupies only $\mathbf{0. 3} \mathbf{m m}^{\mathbf{2}}$, achieving a 610-fold area reduction compared to off-chip implementations. Post-layout simulations using real electric vehicle battery data demonstrate root-mean-square errors of $32.7 \boldsymbol{\mu} \boldsymbol{\Omega}$ in magnitude and $\mathbf{0. 1 7}{ }^{\circ}$ in phase across $\mathbf{1}-\mathbf{H z}$ to $\mathbf{1}-\mathbf{k H z}$ frequency range, validating the system's capability for precise battery diagnostics in integrated automotive applications.

No abstract available

Electrochemical Impedance Spectroscopy (EIS) is a widely used non-destructive diagnostic technique for evaluating the state-of-health (SOH) of lithium-ion batteries. EIS data measured across various frequency ranges reflect distinct internal battery mechanisms, including film interface processes, charge transfer reactions, and diffusion phenomena, each characterized by a combination of resistive, capacitive, and phase shift properties. However, previous EIS-based artificial intelligence (AI) models for SOH estimation exhibit significant limitations. First, these models do not clearly differentiate the battery's internal reaction mechanisms across different frequency ranges. Second, they lack interpretability, which complicates the understanding of how specific changes in EIS data directly influence battery SOH. Third, due to the limited memory resources inherent in embedded battery management systems (BMS), the extensive EIS data available cannot be effectively utilized. To address these limitations, this study proposes a universal frequency-band segmentation method based on Nyquist and Bode phase plots, independent of battery form factors and chemical compositions. Specifically, the low-frequency region is defined using the distinct Warburg line in the Nyquist plot, clearly representing diffusion mechanisms. The mid- and high-frequency bands are separated based on transition points between resistive and capacitive characteristics observed in the Bode phase plot. These transition points occur because the charge transfer and film interface mechanisms repeatedly alternate between resistive and capacitive behaviors, depending on the frequency. In cases where these transition points are ambiguous, the frequencies are fixed based on samples from the same cell and state-of-charge (SOC) showing the most significant degradation, as they clearly exhibit distinct resistive and capacitive behaviors. For each defined frequency band, we applied a Convolution Block Attention Module (CBAM) to automatically identify key impedance data channels (real, imaginary, and magnitude) and essential frequency points that are highly associated with SOH degradation. Specifically, the Channel Attention module selects the impedance channel that is most correlated with SOH, and subsequently, the Spatial Attention module identifies the top-K critical frequency points within the selected channel based on attention weights. A lightweight SOH estimation model was developed using only the selected features, resulting in an extremely compact model size of approximately 0.006 MB, ideal for embedded BMS applications requiring real-time diagnostics. Experimental validation was conducted on two battery systems with different form factors and chemistries. For the 18650 NMC811 cylindrical cell, the proposed method reduced the required EIS measurement time by approximately 63.6% to 77.8% compared to conventional methods. The model achieved a mean absolute error (MAE) of 1.23% across the entire SOH range (0–100%) when using the top-5 selected frequency points, and 1.68% when using the top-3 frequency points. For the Coin LCO cell, the EIS measurement time was similarly reduced by approximately 67.6% to 77.7%, with outstanding estimation performance, indicated by MAE values of 0.63% and 0.88% for top-5 and top-3 frequency point selections, respectively. The developed model demonstrates broad applicability across various battery systems with diverse form factors and chemistries. Unlike conventional AI models that operate as black boxes, our approach offers high interpretability by explicitly clarifying the relationship between specific battery degradation mechanisms and SOH decline. Consequently, this research makes a significant contribution to practical industrial applications by enabling interpretable and reliable battery diagnostics, thereby supporting effective battery health management strategies.

Online diagnosis of lithium-ion battery in electrical vehicles is of paramount importance to assess battery performance and degradation, thus improving the operation efficiency and preventing faults. This work investigates the feasibility of implementing electrochemical impedance spectroscopy (EIS) for the online monitoring of battery cell state parameters, such as the the state-of-health (SoH) and the cycling ageing, in the framework of common industrial procedures for automotive applications. SoH and ageing cell are assessed by typical laboratory tests in automotive grade environment, together with EIS measurements performed both at specific check points and online, during the charging/discharging tests involved in the procedure. The battery impedance measurements are conducted by means of a custom-designed EIS-based sensing system, implementing a multi-band multisine excitation strategy for fast measurement time (as required by online EIS) while preserving good accuracy. Compared with automotive laboratory instrumentation, the online multi-band multisine EIS demonstrated to be suitable for the real-time monitoring of both the battery impedance evolution and the ageing trend.

No abstract available

Lithium-ion batteries inevitably experience aging and performance degradation during operation, which significantly impacts the lifespan and reliability of energy storage systems. As such, accurately predicting the remaining useful life (RUL) is of critical importance. Compared to time-domain features, electrochemical impedance spectroscopy (EIS) can provide more comprehensive information on battery states. However, acquiring full-spectrum EIS data is often hindered by lengthy measurement times and high noise levels. To tackle this problem, this study introduces a feature extraction method for EIS data based on frequency-band selection, integrated with machine learning algorithms for RUL prediction. Pearson correlation analysis is employed to identify frequency bands of the impedance spectrum that are highly correlated with battery aging. Specifically, a continuous five-point frequency band with high correlation coefficients is selected, and the imaginary components of impedance within this range are extracted as input features for the following model. A Bayesian-optimized support vector regression model is constructed for validation. Comparative analysis with other feature extraction methods demonstrates that the proposed approach maintains high prediction accuracy while achieving good generalization across different battery cells. The prediction results show a reduction in root mean square error and mean absolute error by 66.5% and 71.3%, respectively, compared to models using full-spectrum EIS features.

No abstract available

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance for improving the utilization efficiency and safety performance of batteries. Since the time-domain voltage and current data are difficult to reflect the changes in the characteristics of lithium-ion batteries, this paper proposes a RUL prediction method for lithium-ion batteries based on the integration of time convolution network (TCN) and long-term and short-term memory model (LSTM) based on electrochemical impedance spectroscopy(EIS). Bayes optimization algorithm based on tree structure is used to select the super parameters of the model. The TCN network is used to automatically extract the aging characteristics of lithium-ion batteries from the impedance data. The LSTM network is used to capture the long-term and short-term dependencies in the time series of lithium-ion batteries, and the final life prediction is carried out through the full connection layer. The experimental results show that the proposed method based on TPE-TCN-LSTM model can not only accurately grasp the overall degradation trend of the battery, but also quickly capture and respond to the fluctuations, so as to effectively improve the prediction performance.

The gradual depletion of fossil fuel reserves has recently accelerated the transition from traditional internal combustion vehicles to new energy vehicles. Among various energy storage technologies, lithium-ion batteries have gained widespread adoption in NEVs due to their high energy density, negligible memory effect, and low self-discharge rate. However, their performance inevitably deteriorates over time during operation, and in extreme cases, this degradation can lead to thermal runaway. To address these concerns, this paper provides a concise overview of the internal ageing mechanisms of lithiumion batteries and explores the application of electrochemical impedance spectroscopy (EIS) for ageing analysis. Finally, the study reviews and critically evaluates EIS-based approaches for state-of-health (SOH) estimation, while also highlighting the key challenges and future directions in this field.

Lithium-ion batteries (LIBs) play a critical role in electric vehicles (EVs) and hybrid electric vehicles (HEVs) and degradation of LIBs influences lifetime, reliability, safety and dependability. The ability to assess and quantify degradation enables assessment of LIB’s true state of health. This paper investigates LIB degradation using a pseudo two-dimensional (P2D) model, particularly focusing on the changes to Electrochemical Impedance spectroscopy (EIS) results due to degradation. Three key degradation mechanism are considered and the impact of State-of-Charge (SoC) and temperature on EIS results are discussed. This paper also identifies the need for a more realistic approach to assess degradation. Simulations are conducted considering four repetitive standard drive cycles (viz., HTDDT, HWFET, US06 and OCTBC) for a vehicle travel distance of 150,000 km for each case. The cycle counting method is used to convert partial SoC variations during a drive cycle to an equivalent full cycle count which is then used within the degradation model to modify the parameters to represent the P2D model. This study demonstrates a robust process for analyzing degradation dynamics. The methodology presented here can guide future researchers with experimental data, enabling validation and refinement of model parameters to advance LIB degradation analysis and improve battery life predictions under operational scenarios.

Electrochemical impedance spectroscopy (EIS) is one of the most powerful techniques for diagnosing various failures and degradation in electrochemical devices. To date, online converter-based EIS has been demonstrated on only one port at a time—either the input or the output—using tailored control loops to preserve linearity, stability, and causality for that specific device under test. This work presents a single-perturbation scheme that excites one converter to perform simultaneous EIS (S-EIS) on both its input and output ports, enabling characterization of two distinct electrochemical devices in a hybrid system within a single acquisition. Implementing this approach will expand the online application of EIS and optimize the time it takes to perform a comprehensive system measurement. Simulations confirmed by hardware data verify the feasibility of the proposed S-EIS method. The calculated equivalent-circuit parameters deviate by less than 15% of their reference values, demonstrating encouraging performance even at this preliminary conceptual phase.

The challenges of climate change urge a rapid transition of the global energy supply from fossil to renewable feedstocks, including the electrification of numerous areas of societal life. The associated increase in demand for electrical energy with simultaneously intermittent sources such as solar and wind requires enormous electrochemical storage capacities. This explains the immense research interest in advanced electrical storage media, such as lithium ion batteries (LIBs). A popular approach for developing better LIBs is to improve the properties of cathode, anode and electrolyte materials by doping, i.e., by the introduction of small quantities of foreign atoms into a material. For doped battery cathode materials, variable concentrations and combinations of more than 60 metals qualify as potential dopants. A search space of this size cannot efficiently by investigated by conventional experimental approaches. Therefore, significant efforts are made in chemical research to overcome the bottleneck of human capability by the transfer of common synthesis and characterization methods to high-throughput and automated workflows. Our lab has shown in numerous projects that semi-automated high-throughput methods allow for the time-, resource- and cost-efficient synthesis and characterization of various battery materials. Among the previously developed methods are solid-state and sol-gel synthesis, powder X-ray diffractometry and cyclovoltammetry performed on 64 samples in parallel.[1] The portfolio of these methods is now extended by adding high-throughput electrochemical impedance spectroscopy (EIS) and potentiostatic intermittent titration technique (PITT). These methods provide valuable complementary insights into electrochemical reaction kinetics and diffusion behavior of electrode materials. We demonstrate these techniques for LiCoPO4 (LCP) cathodes to identify how the high-throughput EIS and PITT methods contribute to the identification of suitable dopant combinations for materials with advanced properties. LCP is an interesting model substance since the cathodes operate at voltages of 4.8 V vs. Li, which allows considerably higher energy densities. At the same time, challenges like the electronic and lithium ion transport properties as well as the poor cycle stability are relatively well known and continue to be prohibitive for commercial use.[2] Herein, EIS and PITT were, to our knowledge, systematically applied to battery materials in high throughput for the first time. The promising results in the LCP system are expected to be easily transferable to other battery materials. Thus, these methods represent another building block for the efficient development of advanced battery materials that is hoped to pave the transition to renewable energies. References: [1] E. McCalla, J. Phys. Chem. C 2024, 128, 16831, DOI: 10.1021/acs.jpcc.4c04870. [2] A. Jonderian, S. Jia, G. Yoon, V. T. Cozea, N. Z. Galabi, S. B. Ma, E. McCalla, Adv. Energy Mater. 2022, 12, 2201704, DOI: 10.1002/aenm.202201704.

Reducing system capital costs to levels below $100/kWh is considered an important milestone for flow batteries to achieve commercial success. Aqueous organic flow batteries (AOFBs) offer several promising advantages over vanadium flow batteries (VFBs), including improved safety, greater tunability, and, most importantly, lower electrolyte costs.1 Whilst reducing electrolyte costs is advantageous, it also emphasises the relative importance of reducing stack costs as a critical factor for achieving commercial competitiveness. To date, most research on AOFBs has primarily focused on improving molecule solubility and stability, leaving cell design relatively underexplored. As a result, despite a decade of development, AOFB cells have yet to match the power densities and energy efficiencies that VFBs routinely achieve, largely due to AOFBs’ higher area-specific resistance.2 Addressing this high area-specific resistance is therefore essential for AOFBs to lower both stack and total system costs, paving the way for their commercial success. Dissection of polarisation losses remains a crucial tool for guiding cell engineering efforts. Using electrochemical impedance spectroscopy (EIS) with distribution of relaxation times (DRT) analysis as our primary tool, we previously measured the relative contributions of ohmic, activation, and concentration losses to overall cell polarisation for a range of chemistries.3 Building on this foundation, we have now developed an improved DRT model of flow cell systems. Additionally, we demonstrate how EIS and DRT can be used to monitor changes in these features over time, providing insights into the system’s state of health. Furthermore, we have investigated different electrode materials and structures. By employing ultra-long carbon nanotubes (CNTs) in non-woven mats as flow battery electrodes, we have demonstrated their use and the potential performance benefits of this material for the first time. Using CNT mats as a model system, we explore how these benefits can be translated to more commonly employed electrode materials. This work aims to develop the understanding of the relative contributions of flow cell polarisation losses across different chemistries. By doing so, we seek to pinpoint opportunities for cost-effective interventions. (1) Zhang, L.; Feng, R.; Wang, W.; Yu, G. Emerging Chemistries and Molecular Designs for Flow Batteries. Nat Rev Chem 2022, 6 (8), 524–543. https://doi.org/10.1038/s41570-022-00394-6. (2) Amini, K.; Shocron, A. N.; Suss, M. E.; Aziz, M. J. Pathways to High-Power-Density Redox Flow Batteries. ACS Energy Lett. 2023, 8 (8), 3526–3535. https://doi.org/10.1021/acsenergylett.3c01043. (3) Saunders, E.; Grey, C. P.; Volder, M. F. L. D. Deconvoluting Surface Modification Effects on Flow Battery Electrode Performance by Impedance Spectroscopy. Meet. Abstr. 2024, MA2024-01 (3), 571. https://doi.org/10.1149/MA2024-013571mtgabs.

Electrochemical Impedance Spectroscopy (EIS) is a powerful non-destructive detection method, which has been widely used in lithium-ion battery state estimation studies in recent years. Electrochemical impedance spectroscopy is usually resolved by establishing a reasonable Equivalent Circuit Model (ECM), which is crucial for lithium battery state estimation, and the method of identifying the parameters of the equivalent circuit model also directly affects the accuracy and speed of the final estimation. In this paper, firstly, a large amount of impedance spectrum data is obtained by conducting cycle aging experiments on lithium batteries and electrochemical impedance spectrum tests under different cycle times. Subsequently, an equivalent circuit model based on the internal reaction process of the lithium battery was selected, and an algorithm based on the Levenberg-Marquardt nonlinear least-squares method was used to identify the parameters of the equivalent circuit, the reliability of the model was verified by fitting, and the charge transfer resistance was extracted as a characteristic parameter. It is observed from the identification results that the fitting is significant in the range of SOC=60% to SOC=90%, so the State of Health (SOH) estimation model considering State of Charge (SOC) is adopted and the SOH estimation is successfully implemented in this SOC range. The results showed that the SOH estimation was within 3% error for the vast majority of the data.

Interpreting Electrochemical Impedance Spectroscopy (EIS) data is challenging due to the complex chemical system in batteries and the lack of control over variables. The purpose of this paper is to examine how an individual ageing issue affects EIS. In this paper, the relationship between battery water loss and EIS change is investigated through a controllable experiment. In this experiment, a lead-acid battery is destructed and placed in an air-conditioned room, and the EIS is measured every three days, ensuring that the battery’s degeneration is only due to water loss. Through the equivalent circuit model, the change of EIS is analyzed. The results show that the water loss has a different effect on the parameters in the equivalent circuit model (ECM): the internal resistance increases all the time, while the double-layer capacity drops rapidly at a certain amount of water loss. Meanwhile the turning point of parameter change can be used as the basis for judging the complete water loss of the battery.

We introduce a partial-differential-equation model for rechargeable lithium-metal battery (LMB) cells whose parameter values are fully identifiable from cell-level experiments. From this model, we formulate a computationally tractable transfer-function (TF) model for use within optimization loops. A strategy is proposed for regressing the TF model to cell electrochemical impedance spectroscopy (EIS) measurements to estimate parameter values. We validate the regression using a synthetic dataset before application to a single-layer LMB pouch cell. The voltage RMSE between the fully identified model's predictions and laboratory measurements is about 4 mV for a GITT profile. We provide MATLAB code to simulate the model in COMSOL, compute cell impedance from the TF model, and perform model regression.

More than 20 years ago, electrical impedance spectroscopy (EIS) was proposed as a potential characterization method for flow cytometry. As the setup is comparably simple and the method is label-free, EIS has attracted considerable interest from the research community as a potential alternative to standard optical methods, such as fluorescence-activated cell sorting (FACS). However, until today, FACS remains by and large the laboratory standard with highly developed capabilities and broad use in research and clinical settings. Nevertheless, can EIS still provide a complement or alternative to FACS in specific applications? In this Perspective, we will give an overview of the current state of the art of EIS in terms of technologies and capabilities. We will then describe recent advances in EIS-based flow cytometry, compare the performance to that of FACS methods, and discuss potential prospects of EIS in flow cytometry.

Dielectrophoretic force has been used to manipulate and control the biological microparticles. This technique has been used for various purposes such the cell trapping, cell sorting and separating the different biological particles suspended in a buffer solution. The strength of dielectrophoretic force depends on different features related to the cell dielectric features and geometry of the electrodes. Label-free and non-invasive techniques are considered promising in the analysis of the cell. The impedance spectroscopy is matching with the advantages of these techniques. In this paper, we study the influence of the modification of the geometry of the electrodes as the source of dielectrophoresis force on impedance spectroscopy techniques. The main target is to enhance the sensitivity of the system to differentiate between the cells with allow conductivity.

In this article, a new fault diagnosis framework for proton exchange membrane fuel cells (PEMFCs) based on electrochemical impedance spectroscopy (EIS) and the deep learning method is proposed. Specifically, this work employs the PEMFC knowledge to drive the training process of the deep learning network, which makes it possible to improve fault diagnosis performance by deep learning algorithms with a limited scale of actual measured EIS data. A pretraining network is developed to predict the equivalent circuit model (ECM) parameters, which could reduce the time consumption of ECM parameter identification. Besides, considering that the ECM parameters are susceptible to significant changes due to nonfault operation, a fine-tuning network is designed to generate robust diagnosis features, which could support the proposed framework working in different environments. Moreover, the complex neural networks are adopted in the proposed framework to extract features from EIS data, which is composed of complex impedances. Finally, a new evaluation metric PScore is proposed to assess the performance of the diagnosis framework from the perspective of practical applications. The experiments are performed to demonstrate the effectiveness of each component in the framework, and the proposed algorithm has significant improvements in fault diagnosis performance and computational efficiency over traditional algorithms.

Dye‐sensitized solar cells (DSSC) have received significant interest in the photovoltaic technology because of their eco‐friendly nature, affordability and flexibility. Here, this work presents a DSSC of the configuration; FTO/WO3/N719 Dye/GO/C with performance metrics – open‐circuit voltage (Voc) of 1.1055 V, short‐circuit current density (Jsc) of 22.23 mA cm−2, a fill factor (FF) of 84.65%, and a remarkable power conversion efficiency (PCE) of 20.80%. The study utilizes a wide frequency range of 10−3 to 1010 Hz to examine charge transport dynamics and evaluate the electrochemical performance of the model cell. Impedance spectroscopy investigates both complex electrical impedance (Z*) and electric modulus (M*) to provide critical insights into ionic transport, charge recombination, ion migration and diffusion mechanisms within the cell. A model equivalent circuit is developed and theoretically validated by fitting experimental alternating current (AC) data to theoretical predictions, allowing the extraction of characteristic time constants for various processes. The results highlight that efficient ion conduction and rapid electron diffusion are essential for optimizing charge collection and minimizing recombination losses. Further, the study emphasizes the critical role of both series and shunt resistances across low‐ and high‐frequency domains, establishing a strong correlation between time constant behavior and overall device efficiency.

This article proposes an improved online electrochemical impedance spectroscopy (EIS) measurement strategy for fuel cell electric vehicle (FCEV) applications. A two-phase interleaved boost converter (IBC) is designed to regulate the output power of a proton exchange membrane fuel cell (PEMFC). Then, the alternating disturbance signals are injected into the fuel cell via the controller of the direct current to direct current (dc/dc) converter without any additional equipment. An injection strategy based on bandwidth distinguishment is proposed to determine the frequency and injection position of disturbance signals. Furthermore, a closed-loop control strategy is introduced to stabilize the response signal’s amplitude at the desired value. The perturbations are effectively regulated to satisfy the signal-to-noise ratio requirement of sensors. Hence, a low-cost high-accuracy online EIS measurement solution is realized. The effectiveness of the approach is validated with a 1-kW converter prototype. The experimental results indicate that the proposed approach can achieve impedance detection with a relatively high degree of accuracy. When testing the setting varies, such as relative humidity fluctuation, the observed spectroscopy clearly demonstrates the noticeable variation in the internal electrochemical reaction.

A versatile, centrifuge-coupled lab-on-a-compact disk (ccLoCD) hardware is developed for high through-put quantification of cell volume fractions (<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>) by electrical impedance spectroscopy (EIS). The eight-electrode sensor hardware consists of 32-bit ARM microcontroller, impedance converter, and two high-speed analog multiplexer (MUX) modules on a disk mounted on a motor. It was evaluated by quantifying red blood cells (RBCs) of; 1. chicken (<inline-formula> <tex-math notation="LaTeX">$\phi _{\mathrm {chic}}^{\mathrm {RBC}}$ </tex-math></inline-formula>) mixed with pork RBC with centrifugation and 2. pork RBC (<inline-formula> <tex-math notation="LaTeX">$\phi _{\mathrm {por}}^{\mathrm {RBC}}$ </tex-math></inline-formula>) as they sediment into the sensor in saline (S), 50% plasma (50-P) and whole blood (WB) for assay of effects of protein concentration (<inline-formula> <tex-math notation="LaTeX">$\text {Conc}^{\mathrm {p}}$ </tex-math></inline-formula>) on erythrocyte sedimentation rates (ESRs). To check quantification accuracy (QA), <inline-formula> <tex-math notation="LaTeX">$\phi _{\mathrm {por}}^{\mathrm {RBC}}$ </tex-math></inline-formula> (blood hematocrit) in cardiopulmonary bypass (CPB) was measured in comparison to <inline-formula> <tex-math notation="LaTeX">$\phi _{\mathrm {por}}^{\mathrm {RBC}}$ </tex-math></inline-formula> from standard microcentrifuge (MC) method at increasing plasma volumes (<inline-formula> <tex-math notation="LaTeX">$v_{\mathrm {p}}$ </tex-math></inline-formula>). Blood impedance, measured between 10 and 100 kHz, was analyzed by distribution of relaxation times (DRTs) and regression modeling to quantify <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> by correlating (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) amplitude maxima (<inline-formula> <tex-math notation="LaTeX">$\gamma _{\max }$ </tex-math></inline-formula>) to <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>. From results at characteristic relaxation time <inline-formula> <tex-math notation="LaTeX">$\tau _{\mathrm {c}} = 1\times 10^{-5}$ </tex-math></inline-formula> s, differences in <inline-formula> <tex-math notation="LaTeX">$\gamma _{\max }$ </tex-math></inline-formula> before and after centrifugation were statistically significant (<inline-formula> <tex-math notation="LaTeX">$p \lt 0.05$ </tex-math></inline-formula>) with centrifugation increasing QA thus raising <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> from 0.0242 to 0.989. In 15 min, differences in <inline-formula> <tex-math notation="LaTeX">$\phi _{\mathrm {por}}^{\mathrm {RBC}}$ </tex-math></inline-formula> for RBC sedimentation in S, 50%-P, and WB were also statistically significant (<inline-formula> <tex-math notation="LaTeX">$p \lt 0.05$ </tex-math></inline-formula>) showing capacity of hardware to capture <inline-formula> <tex-math notation="LaTeX">$\text {Conc}^{\mathrm {p}}$ </tex-math></inline-formula> effects. In QA analysis, hardware-based <inline-formula> <tex-math notation="LaTeX">$\phi _{\mathrm {por}}^{\mathrm {RBC}}$ </tex-math></inline-formula> had low 0.73% error relative to MC-based <inline-formula> <tex-math notation="LaTeX">$\phi _{\mathrm {por}}^{\mathrm {RBC}}$ </tex-math></inline-formula>. The <inline-formula> <tex-math notation="LaTeX">$\phi _{\mathrm {por}}^{\mathrm {RBC}}$ </tex-math></inline-formula> strongly correlated with <inline-formula> <tex-math notation="LaTeX">$v_{\mathrm {p}}$ </tex-math></inline-formula> in flow (<inline-formula> <tex-math notation="LaTeX">$R^{2} =0.9937$ </tex-math></inline-formula>) which shows hardware’s potential for low cost measurements of cell <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>.

Epithelial tissues form barriers to the flow of ions, nutrients, waste products, bacteria, and viruses. The conventional electrophysiology measurement of transepithelial resistance (TEER/TER) can quantify epithelial barrier integrity, but does not capture all the electrical behavior of the tissue or provide insight into membrane-specific properties. Electrochemical impedance spectroscopy, in addition to measurement of TER, enables measurement of transepithelial capacitance (TEC) and a ratio of electrical time constants for the tissue, which we term the membrane ratio. This protocol describes how to perform galvanostatic electrochemical impedance spectroscopy on epithelia using commercially available cell culture inserts and chambers, detailing the apparatus, electrical signal, fitting technique, and error quantification. The measurement can be performed in under 1 min on commercially available cell culture inserts and electrophysiology chambers using instrumentation capable of galvanostatic sinusoidal signal processing (4 μA amplitude, 2 Hz to 50 kHz). All fits to the model have less than 10 Ω mean absolute error, revealing repeatable values distinct for each cell type. On representative retinal pigment (n = 3) and bronchiolar epithelial samples (n = 4), TER measurements were 500–667 Ω·cm2 and 955–1,034 Ω·cm2 (within the expected range), TEC measurements were 3.65–4.10 μF/cm2 and 1.07–1.10 μF/cm2, and membrane ratio measurements were 18–22 and 1.9–2.2, respectively. Key features • This protocol requires preexisting experience with culturing epithelial cells (such as Caco-2, RPE, and 16HBE) for a successful outcome. • Builds upon methods by Lewallen et al. [1] and Linz et al. [2], integrating commercial chambers and providing a quantitative estimate of error. • Provides code to run measurement, process data, and report error; requires access to MATLAB software, but no coding experience is necessary. • Allows for repeated measurements on the same sample. Graphical overview Electrochemical impedance spectroscopy measurement involves sending a galvanostatic signal through the electrophysiology chamber and across the epithelial cell monolayer (left) and results in complex impedance data at each frequency. This data is then fit to an electrical circuit model to output transepithelial resistance (TER), transepithelial capacitance (TEC), and membrane ratio (α) (right).

Single-cell microfluidic impedance spectroscopy is widely used to characterise single cells, but the intrinsic electrical properties are rarely determined owing to the limited number of data points across a wide frequency bandwidth. To address this shortcoming, we have developed a system with an extended frequency range (to 550 MHz) that measures the impedance spectrum of single nucleated cells at high throughput. The system was evaluated using HL60 cells treated with glutaraldehyde or cytochalasin D, and THP-1 cells differentiated into macrophages. The impedance data was fitted to the double-shell model to obtain cell membrane capacitance and cytoplasm conductivity. It is shown that reducing the conductivity of the suspension media significantly enhances the dielectric relaxations of the cell membrane, allowing small differences between control and chemically modified cells to be discriminated.

Electrical impedance spectroscopy (EIS) offers a noninvasive mean to probe the physiological and morphological dynamics of microbial populations. Here, we present an impedance-based circuit modeling approach to characterize and monitor the volumetric and electrical behavior of Saccharomyces cerevisiae across its growth cycle. By modeling the yeast suspension as a dielectric system and fitting its frequency-dependent impedance to a modified Randles circuit, we extract discrete electrical parameters—capacitance, resistance, and diffusion elements—that reflect cellular properties and concentration. We show that suspension capacitance scales linearly with optical density (OD) and correlates with estimated yeast cell volume, enabling direct quantification of biomass without requiring optical dilution. This relationship holds across dilution series and growth kinetics experiments, where temporal increases in capacitance mirror population expansion. Furthermore, metabolic activity within the media is captured through shifts in background capacitance, providing insights into nutrient consumption and media composition. Our method offers a scalable, label-free platform for real-time yeast monitoring, with broad implications for bioprocess optimization, synthetic biology, and intelligent bioreactor design.

Bioprinting produces personalized, cell-laden constructs for tissue regeneration through the additive layering of bio-ink, an injectable hydrogel infused with cells. Currently, bioprinted constructs are assessed for quality by measuring cellular properties post-production using destructive techniques, necessitating the creation of multiple constructs and increasing the production costs of bioprinting. To reduce this burden, cell properties in bio-ink can be monitored in real-time during printing. We incorporated dielectric impedance spectroscopy (DIS) onto a syringe for real-time measurement of primary chondrocytes suspended in phosphate buffered saline (PBS) using impedance (|Z|) and phase angle (θ) from 0.1 to 25 000 kHz. Cell concentration and viability ranged from 0.1 × 106 cells ml−1 to 125 × 106 cells ml−1 and from 0%to 94%, respectively. Samples with constant or with changing cell concentration were exposed to various flow conditions from 0.5 to 4 ml min−1. The background PBS signal was subtracted from the sample, allowing for comparisons across devices and providing insight into the dielectric properties of the cells, and was labeled as |Zcells| and θcells. |Zcells| shared a linear correlation with cell concentration and viability. Flow rate had minimal effect on our results, and |Zcells| responded on the order of seconds as cell concentration was altered over time. Notably, sensitivity to cell concentration and viability were dependent on frequency and were highest for |Zcells| when θcells was minimized. Cell concentration and viability showed an additive effect on |Zcells| that was modeled across multiple frequencies, and deconvolution of these signals could result in real-time predictions of cell properties in the future. Overall, DIS was found to be a suitable technique for real-time sensing of cell concentration and viability during bioprinting.

Objective: This study aims to enhance the identification of cancer cell types using electrical impedance spectroscopy (EIS) by introducing a novel analysis method, ECM-PCA, which integrates an equivalent circuit model with principal component analysis. Methods: The ECM-PCA method addresses the limitations of conventional PCA and kernel PCA (kPCA) in handling non-linear and frequency-dependent data. Impedance data of four cancer cell types (DLD-1, T.Tn, U138, and U87) were acquired across a frequency range of 0.1 MHz to 300 MHz. The ECM-PCA method was applied to analyze the frequency-dependent impedance behaviour and compare its clustering performance with PCA and kPCA. Results: ECM-PCA demonstrated clustering performance comparable to kPCA while capturing the frequency-dependent features of impedance spectra, which kPCA lacks. The phase angle component as the ECM-PCA input achieved the highest Calinski-Harabasz (CH) score of 935, and the method achieved an identification accuracy of 93.6% in the PC1 and PC2 plane. Conclusion: ECM-PCA improves the accuracy and interpretability of cancer cell type identification based on electrical impedance data. Significance: This study highlights the potential of ECM-PCA in advancing cancer diagnostics through enhanced analysis of impedance spectra.

No abstract available

Cellular spheroids are considered a popular option for modeling healthy and diseased tissues in vitro and as injectable therapies. The formation and culture of spheroids can make use of different three-dimensional (3D) culture platforms, but the spheroids’ analysis often has to rely on endpoint assays. In this study, we propose a microfluidic bioreactor to culture and nondestructively monitor human mesenchymal stem cell (hMSC) spheroids over time using non-Faradaic electr(ochem)ical impedance spectroscopy (EIS). For this, an array of porous microwells thermoformed from ion track-etched thin films and a pair of sensing electrodes from transparent indium tin oxide are integrated into the flow and culture chamber of the bioreactor. To measure the spheroid’s electrical properties, the electrodes are connected to a frequency response analyzer (FRA), with a multiplexer in between to enable the operation of more than one bioreactor at the FRA at the same time. We find differences between the complex resistance/impedance and/or capacitance data of a reference condition without cells, a two-dimensional (2D) hMSC culture, hMSC spheroids, and hybrid spheroids aggregated from hMSCs and titanium or hydroxyapatite microparticles. We also found differences between different culture durations. These results suggest that our device can sense the presence and spatial arrangement of cells and micro(sized) biomaterials as a function of time.

Electrochemical impedance spectroscopy (EIS) is a characterization technique for the analysis of electrochemical systems. Interest in EIS as an in-situ diagnostic tool for proton-exchange-membrane fuel cell (PEMFC) devices requires the development of rapid methods of data acquisition, processing, and interpretation. In this work, methods to analyze the impedance response of PEMFCs were developed to extract physical properties that described cell kinetics and transport. A frequency-domain continuum transport model was developed that accounted for reactant concentrations, temperature, and fluid pressures that was capable of simulating the observed real resistances in experimental measurements. The impedance model allowed for the evaluation of kinetic and transport resistances that were consistent with polarization data, which can be used to obtain kinetic and transport information from an impedance measurement at a fixed steady-state potential. Extracted electrokinetic parameters agreed with those extracted from polarization data. Estimation of the limiting low-frequency impedance was necessary to analyze experimental spectra, which was achieved using an arbitrary Voigt-element model and an equivalent circuit model inspired by Havriliak-Negami relaxation. The cell model was also used to simulate transfer functions between reactant oxygen concentration and current density that was shown to agree with experimental observations in literature that probed the pressure dependence of the oxygen-reduction reaction. Figure 1

Electrochemical pressure impedance spectroscopy (EPIS) is a fuel cell diagnostic technique based on pressure alternating frequency response analysis. This technique has similarities to electrochemical impedance spectroscopy except it implements mechanical perturbations via pressure oscillations rather than voltage or current oscillations to achieve an electrochemical impedance response. This study examines pressure-induced electrochemical resistances while independently varying fuel cell inlet operating conditions, such as cathode relative humidity, gas stoichiometry at the cathode, and fuel cell temperature. Results indicated that EPIS resistances increased with decreasing cathode relative humidity, decreasing cathode stoichiometry, and increasing cell temperature. Variation in transport resistance with cathode stoichiometry was attributed to decreased oxygen partial pressures measured at the cathode outlet. Alternatively, variations in resistance with relative humidities were correlated to changes in humidity ratios throughout the cathode. Resistance changes with cell temperature were a function of both phenomena. Mechanical responses were analyzed by measuring inlet pressure response relative to outlet pressure perturbations. Mechanically based variations observed during cathode stoichiometry tests indicated additional down-channel variations from changes in gas flow. In comparison, insignificant variations in mechanical response were observed during relative humidity and temperature tests, suggesting purely through-plane responses attributed to electrochemical resistances within the membrane electrode assembly.

The dielectric properties of biological culture media play a critical role in accurately modeling and optimizing in vitro electrical stimulation systems. This study presents the dielectric characterization of a skeletal muscle cell differentiation medium using electrochemical impedance spectroscopy (EIS) with a custom‐designed three‐electrode stainless steel setup. Impedance measurements were conducted across a frequency range of 0.01 Hz to 10 kHz and fitted to a Randles equivalent circuit, yielding excellent agreement with experimental data ( χ 2  = 0.0833). Analysis focused on the frequency range of interest for stimulation applications (1–100 Hz), where results demonstrated marked frequency‐dependent behavior in conductivity (ranging from 0.07 to 4.7 S/m) and relative permittivity ( ε ′ exceeding 2 × 10 9 at 1 Hz), indicative of α ‐dispersion and interfacial polarization effects. The medium's high ionic conductivity and dielectric response were comparable to those observed in standard saline and physiological solutions, reinforcing its suitability for bioelectrical applications. While the use of stainless‐steel electrodes facilitated low‐cost fabrication and stable measurements, minor variability in the impedance signal may reflect surface redox activity. Future work will involve integrating live‐cell systems, refining electrode materials, and extending equivalent circuit models to capture diffusion‐related phenomena for real‐time monitoring in organ‐on‐a‐chip platforms.

Electrochemical flow cell systems enable continuous, real-time monitoring with enhanced reproducibility, improved mass transport, and reduced manual intervention. Their reliability depends on the stability of the reference electrode, which we investigated using electrochemical impedance spectroscopy (EIS) in static and flow cell systems. Two flow cell configurations were analyzed: a High-Performance Liquid Chromatography (HPLC) electrochemical flow cell and a flow cell for screen-printed electrodes (SPE). While the SPE flow cell exhibited impedance behavior similar to a static cell, the HPLC flow cell showed elevated reference electrode impedance due to a ceramic frit. Comparative analysis with the static cell reference electrode confirmed the frit material’s influence on impedance, emphasizing the need for optimized reference electrode design to ensure accuracy in dynamic systems. Having resolved the reference electrode frit issue, we leveraged the optimized system to develop a βeta Cyclodextrin biosensor for non-redox EIS-based cortisol detection in PBS. Since cortisol is electrochemically inactive, a tailored sensing strategy was necessary. Transitioning from static to automated flow cells improved sensitivity and reproducibility, allowing for precise real-time monitoring. The goal is to establish a robust, long-term automated detection system, highlighting the biosensor’s clinical potential for real-time health monitoring.

The purpose of this research is investigating the effect of eutectic lamellae modification through heat-treatments on the electrochemical performance of Eutectic aluminum-copper (E-Al82.70Cu17.30 at%) alloy. The alloy works as anode in less expensive aluminum chloride (AlCl3) aqueous electrolyte using Symmetric Cell Electrochemical Impedance Spectroscopy (SCEIS). The alloy was produced using an arc furnace followed by heat-treatments at different conditions. X-ray diffraction (XRD), optical microscope (OM), were used to characterize the alloys followed by SCEIS testing. The as-cast E-Al82.70Cu17.30 showed some inhomogeneities in the lamellae microstructure, while heat-treating the as-cast E-Al82.70Cu17.30 produced homogenized sponge-like eutectic microstructure. Two heat-treatment conditions were employed, that is 1. Heating the as cast alloy at 535 °C and soaking for one hour, and 2. Heating at 535 °C and soaking for five hours. Electrochemical Impedance spectroscopy measurements were performed on these three (3) samples. The Nyquist plot of as-cast E-Al82.70Cu17.30, 1 and 5 hours annealling heat-treated (1HT and 5HT respectively) displayed characteristic diameters in high, medium, and also low frequency range. After fitting using complex non-linear least squares method, 1HT condition has bulk resistance (RI) and charge transfer resistance (RCT) of ~9 Ω and ~566 Ω respectively, as compared to As-cast condition, which has RI of ~ 14 Ω and RCT of ~ 695 Ω, whiles 5HT condition has RI of ~26 Ω and RCT of ~724 Ω.

Perovskite solar cells (PSCs) are promising photovoltaic technologies, yet their performance is critically influenced by the relative permittivity (εr) of the active layer, which governs charge carrier dynamics. This study employs SCAPS-1D simulations coupled with complex impedance and modulus spectroscopy to systematically investigate the impact of varying the εr of the MAPbI3 layer from 4 to 12. We find that while the open-circuit voltage (Voc~1.05 V) and short-circuit current density (Jsc~25 mA cm−2) remain stable, the FF and efficiency η (%) decline from 78% to 70% and 20% to 17%, respectively, with increasing εr. Impedance analysis deconvoluted this trend, revealing a decrease in recombination time (τ1) and a peak in ionic transport time (τ2) at εr = 7. The optimal performance of 18.86% was achieved at a lower εr, demonstrating that minimizing recombination losses through permittivity engineering is crucial for advancing PSC efficiency.

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Organ-on-chip (OOC) systems that recapitulate microenvironmental features like coculture, fluid shear stress, and extracellular matrix are useful for modeling biological barriers. OOC barrier integrity measurements are often done by trans-endothelial/epithelial electrical resistance (TEER) measurement, but this approach is confounded by nonuniform current distribution and interference from biomaterials typical to such systems. We addressed this gap by incorporating gold leaf porous membrane electrical cell-substrate impedance sensing (PM-ECIS) electrodes (diameters of 250, 500, or 750 μm) into a biocompatible tape-based barrier-on-chip (BOC) platform. PM-ECIS measurements were robust to fluid shear (5 dyn/cm2) in cell-free devices, yet highly sensitive to flow-induced changes in an endothelial barrier model. Perfusion (0.06 dyn/cm2) corresponded to significant decreases in impedance at 40 kHz (p < 0.01 for 750, 500 μm electrodes) and resistance at 4 kHz (p < 0.05 for all electrode sizes) relative to static control, with minimum values reached 6.5-9.5 h after flow induction. We also demonstrated that PM-ECIS is robust to the presence of hydrogel, and unlike chopstick TEER, has the measurement sensitivity to detect human brain microvascular endothelial monolayers in a hydrogel coculture model. The sensitive, noninvasive, real-time measurements of barrier function in microfluidic PM-ECIS setups makes it well-suited for OOC applications that include features like 3D coculture, biomaterials, and shear stress.

Inorganic–organic hybrid lead halide perovskites are promising materials for photovoltaic applications but face challenges related to toxicity and stability. To address these issues, research has focused on “perovskite-inspired” alternatives that retain the advantages of lead-based perovskites. Bismuth (Bi3⁺)-based materials, with electronic structures resembling lead (Pb2⁺), have emerged as potential light absorbers, despite suboptimal efficiencies. This study explores CsBiSI₂ as an absorber layer in quaternary chalcohalide perovskite solar cells using SCAPS-1D simulations. Results highlight the critical influence of donor doping density on efficiency and impedance variations, surpassing the impact of acceptor density. Interface defect density significantly affects charge carrier dynamics, particularly at the electron transport layer and perovskite interface. Electrical and impedance analyses underscore these dynamics, offering insights into optimizing material interfaces for stability and efficiency. This research advances the development of environmentally friendly, stable, and efficient inorganic solar cells, paving the way for improved photovoltaic technologies.

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Cell sorting holds broad applications in fields such as early cancer diagnosis, cell differentiation studies, drug screening, and single-cell sequencing. However, achieving high-throughput and high-purity in label-free single-cell sorting is challenging. To overcome this issue, we propose a label-free, high-throughput, and high-accuracy impedance-activated cell sorting system based on impedance detection and dual membrane pumps. Leveraging the low-latency characteristics of FPGA, the system facilitates real-time dual-frequency single-cell impedance detection with high-throughput (5 × 104 cells per s) for HeLa, MDA-MB-231, and Jurkat cells. Furthermore, the system accomplishes low-latency (less than 0.3 ms), label-free, high-throughput (1000 particles per s) and high-accuracy (almost 99%) single-particle sorting using FPGA-based high-precision sort-timing prediction. In experiments with Jurkat and MDA-MB-231 cells, the system achieved a throughput of up to 1000 cells per s, maintaining a pre-sorting purity of 28.57% and increasing post-sorting purity to 97.09%. These findings indicate that our system holds significant potential for applications in label-free, high-throughput cell sorting.

The development of modern technologies requires a balanced approach that considers both environmental sustainability and economic feasibility. The large‐scale adoption of silicon‐based solar cells remains limited due to high production costs and environmental challenges. Herein, we present a chalcogenide‐based solar cell of the configuration, FTO/SnO 2 /SrZrS 3 /Cu‐MOF/C, made of affordable materials. The cell reports a theoretical power conversion efficiency of 32.28%. The simulation was carried out using SCAPS‐1D device simulator, whereas the optical parameters were determined using the density functional theory (DFT). The device exhibits an open‐circuit voltage ( V oc ) of 1.10 V, a short‐circuit current density ( J sc ) of 35.89 mA/cm 2 , and a fill factor (FF) of 82.20%. The absorption coefficient ( α ) of the perovskite compound, SrZrS 3 , was found to increase with low‐energy absorption, reaching its first peak at 13.74 eV with a magnitude of 2.44 × 10⁵ cm −1 . Further, the relative permittivity of the materials used in this work was measured using impedance spectroscopy analysis. It was found that high permittivity improves the cell's tolerance to various non‐idealities, particularly in the early stages of device development when the structure is optimized.

In the face of accelerating climate change, the electrification of the mobile sector has become a pressing necessity. Modes of transportation such as rail, air, and marine now demand battery systems with significantly higher energy and power capabilities compared to conventional passenger vehicles. For instance, regional aircraft and construction machines require pack-level energy densities exceeding 330 Wh/kg and 300 Wh/L, respectively, for viable operation. These demanding technical requirements necessitate exploration beyond traditional liquid-electrolyte lithium-ion batteries. Solid-state batteries have garnered attention as a promising alternative, offering potential energy densities three to four times higher than conventional designs, along with improved safety and extended cycle life. However, their large-scale implementation remains hindered by challenges in manufacturing solid electrolytes capable of achieving sufficient contact with electrodes, comparable to the wetted interfaces in liquid-electrolyte systems. Semi-solid batteries have emerged as a compelling transitional technology, combining the advantages of solid and liquid systems. These batteries employ a gel-like matrix stabilized by less than 10% liquid electrolyte to address interfacial contact limitations, providing improved safety and stability. Commercially available in large-format pouch cells, semi-solid batteries show great promise. However, their performance and longevity are highly temperature-dependent, with steep thermal gradients often developing between the cell's surface and core during fast charge-discharge cycles. Accurate assessment of capacity fade and power degradation over time is crucial, necessitating robust multiphysics models that couple electrochemical and thermal dynamics. Parameterization of such models is challenging due to limited data transparency from cell manufacturers and restrictions on accessing internal cell structures. To address similar challenges, electro-thermal impedance spectroscopy (ETIS) has been developed as a cost-effective, non-destructive diagnostic technique. Building upon the widely used Electrochemical Impedance Spectroscopy (EIS) methodology, ETIS enables characterization of thermal behavior without reliance on electrochemical coupling. By applying a sufficiently high excitation current to induce detectable surface temperature changes, ETIS facilitates rapid estimation of thermal impedance and entropy-related parameters, offering insights into battery degradation and material structural changes. This study presents the application of ETIS to characterize the thermal behavior of a 106 Ah-rated, semi-solid commercial pouch cell. A sinusoidal current pulse with an RMS amplitude of 30 A is applied to induce internal heat generation, and surface temperature changes are monitored using K-type thermocouples placed at five strategic locations on the cell. Measurements are conducted in a thermally controlled chamber to isolate ambient temperature effects, across a low-frequency range from 50 mHz to 0.01 mHz. The results include thermal parameters derived from this analysis and the development of an electrothermal equivalent circuit model. These findings contribute to advancing our understanding of semi-solid battery thermal management and degradation mechanisms, paving the way for optimized designs and improved operational safety.

Conventional approaches for bacterial cell analysis are hindered by lengthy processing times and tedious protocols that rely on gene amplification and cell culture. Impedance spectroscopy has emerged as a promising tool for efficient real-time bacterial monitoring, owing to its simple, label-free nature and cost-effectiveness. However, its limited practical applications in real-world scenarios pose a significant challenge. In this review, we provide a comprehensive study of impedance spectroscopy and its practical utilization in bacterial system measurements. We begin by outlining the fundamentals of impedance theory and modeling, specific to bacterial systems. We then offer insights into various strategies for bacterial cell detection and discuss the role of impedance spectroscopy in antimicrobial susceptibility testing (AST) and single-cell analysis. Additionally, we explore key aspects of impedance system design, including the influence of electrodes, media, and cell enrichment techniques on the sensitivity, specificity, detection speed, concentration accuracy, and cost-effectiveness of current impedance biosensors. By combining different biosensor design parameters, impedance theory, and detection principles, we propose that impedance applications can be expanded to point-of-care diagnostics, enhancing their practical utility. This Perspective focuses exclusively on ideally polarizable (fully capacitive) electrodes, excluding any consideration of charge transfer resulting from Faradaic reactions.

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Blood, a vital tissue comprising blood cells within the plasma matrix, plays a crucial role in transporting oxygen, nutrients, and functional components throughout the body. Insufficient blood levels can lead to disorders or life-threatening conditions, necessitating blood transfusions for those experiencing deficiencies. Blood banks store blood products to meet transfusion needs, emphasizing safety, donor health, patient conditions, cross-matching accuracy, and storage quality. Examining stored blood indicates an individual's physiological response to environmental changes, with quantitative and qualitative changes visible through whole blood cell count and impedance parameters. Electrical impedance spectroscopy, which measures these biological properties, shows that although blood cell count remains stable over 35 days, impedance characteristics change significantly. Analysis of the Nyquist Zriil plot reveals a consistent decrease in Zriil values, indicating reduced extracellular resistance (Res) over time. These impedance changes reflect alterations in blood morphology, providing crucial insights into the quality of stored blood. In conclusion, electrical impedance spectroscopy effectively monitors stored blood quality, detecting significant changes in extracellular resistance over extended storage periods. These findings underscore the importance of regular monitoring and proper management of stored blood to ensure its safety and effectiveness for transfusions.

This paper presents a DAC-Based transmitter for serial link system. To balance speed and accuracy, the 7b DAC i s divided into 4 binary and 3 thermometer. To improve output impedance matching and reduce return loss, the CML driver is use d in the DAC unit cell. In addition, the quarter-rate 4:1MUX is used in the last stage of multiplexer, which is based on a 1UI pulse generator, reduces the design complexity of the clock circuit. T he DAC-Based TX implements serial 28Gb/s PAM4 output on 65 nm CMOS technology with differential output of 617 mVpp. At t he Nyquist frequency of 6.89GHz, SNDR=38.15dB, SNR=42.01d B, SFDR=40.44dBc, THD=-40.44dB. It consumes 152mW at 1.2 V supply voltage.

Learning the underlying dynamics of single cells from snapshot data has gained increasing attention in scientific and machine learning research. The destructive measurement technique and cell proliferation/death result in unpaired and unbalanced data between snapshots, making the learning of the underlying dynamics challenging. In this paper, we propose joint Velocity-Growth Flow Matching (VGFM), a novel paradigm that jointly learns state transition and mass growth of single-cell populations via flow matching. VGFM builds an ideal single-cell dynamics containing velocity of state and growth of mass, driven by a presented two-period dynamic understanding of the static semi-relaxed optimal transport, a mathematical tool that seeks the coupling between unpaired and unbalanced data. To enable practical usage, we approximate the ideal dynamics using neural networks, forming our joint velocity and growth matching framework. A distribution fitting loss is also employed in VGFM to further improve the fitting performance for snapshot data. Extensive experimental results on both synthetic and real datasets demonstrate that VGFM can capture the underlying biological dynamics accounting for mass and state variations over time, outperforming existing approaches for single-cell dynamics modeling.

Accurate measurement of the M-ary phase shift keying (MPSK) signal determined by finite parameters per unit of time is significant in engineering fields, such as digital microwave and satellite communication. In this article, we propose a sub-Nyquist sampling system with two channels for measuring characteristic parameters of MPSK signals based on the finite rate of innovation (FRI) sampling theory. The carrier frequency parameter measurement process is modeled as a convex optimization problem, solvable using the parameter matching method on the estimated grid. The minimum harmonic frequency of the carrier is determined to build the exact grid, and the carrier frequency is estimated through a greedy search based on the given cost function. Even when the truth value of the carrier frequency is unknown, the discontinuity location parameters can be measured with high precision using the spectral estimation method on the Fourier coefficients of the MPSK signal. We propose an upper bound on the estimation error of this separable measurement method for signals with one segment. The amplitude and phase parameters can be measured by solving a least square problem. Our proposed measurement system, with lower hardware complexity, can accurately measure the MPSK signal with $K$ segments by sampling only $2K$ + 5 points in a period, at a much lower sampling rate than the carrier frequency. Numerical simulations and hardware experiments demonstrate the superior performance of our state-of-the-art measurement method.

The linear frequency modulated pulse stream (LFMPS) is a finite rate of innovation (FRI) signal that depends on a finite number of parameters within a unit of time, and precise measurement of these parameters holds great significance in the engineering field. However, traditional LFMPS sensing methods face challenges such as complex sampling structures, high sampling rates, and poor parameter estimation accuracy. This article proposes a universal pulse stream sampling structure based on FRI sampling theory to address these issues. The structure utilizes the periodicity of LFMPS to locate pulses from the sub-Nyquist samples of the first signal period. Then, window functions are constructed to separate the LFM pulse in the second period and perceive the related parameter through the parameter matching method. We analyze the effect of the LFM waveform on pulse positioning in the first period and demonstrate the feasibility of estimating the chirp-rate (CR) factor using the parameter matching method. Numerical simulation results show that our proposed algorithm achieves accurate LFM signal measurement at lower sampling rates and has stronger noise immunity during parameter measurement compared to classical sensing approaches.

During the development of complicated multicellular organisms, the robust formation of specific cell-cell connections (cell matching) is required for the generation of precise tissue structures. Mismatches or misconnections can lead to various diseases. Diverse mechanical cues, including differential adhesion and temporally varying cell contractility, are involved in regulating the process of cell-cell recognition and contact formation. Cells often start the process of cell matching through contact via filopodia protrusions, mediated by specific adhesion interactions at the cell surface. These adhesion interactions give rise to differential mechanical signals that can be further perceived by the cells. In conjunction with contractions generated by the actomyosin networks within the cells, this differentially coded adhesion information can be translated to reposition and sort cells. Here, we review the role of these different cell matching components and suggest how these mechanical factors cooperate with each other to facilitate specificity in cell-cell contact formation.

Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availability https://github.com/ratschlab/scim

The development of biomaterials that reconcile print fidelity with cellular functionality remains a major challenge in extrusion‐based 3D bioprinting. Here, a viscoelastic hydrogel featuring a small‐molecule‐mediated crosslinking dynamic network, enabling precise tuning of viscoelastic properties to mimic the mechanical properties of diverse tissues is introduced. The hydrogel's unique combination of high viscosity and rapid shear‐thinning characteristics reduced extrusion‐induced cell damage while maintaining structural integrity. Meanwhile, the hydrogel mimicking the viscoelasticity of bone marrow significantly promotes the proliferation, spreading, migration and stemness maintenance of bone marrow‐derived mesenchymal stem cell (BMSC) in 3D culture via an integrin/p‐FAK/Lamin/YAP signaling pathway, with an enhanced bone regeneration efficacy both in vitro and in vivo. The molecular mechanisms underlying viscoelastic hydrogel‐mediated osteogenic differentiation are also uncovered, revealing a novel phenomenon of nuclear co‐localization and interaction between Wnt1 and YAP. Moreover, designed viscoelastic hydrogels enable the establishment of disease models by replicating the mechanical parameters of pathological matrices relevant to colon cancer, pulmonary fibrosis, and liver cancer. Overall, this work establishes a unique strategy for bioink design, merging regenerative medicine and disease modeling by integrating tunable viscoelasticity with biological functions, offering broad translational potential for future 3D bioprinting.

SUMMARY This study introduces a pattern-matching method to enhance the efficiency and accuracy of physical verification of cell libraries. The pattern-matching method swiftly compares layouts of all I/O units within a specific area, identifying significantly different I/O units. Utilizing random sampling or full permutation can improve the efficiency of verification of I/O cell libraries. All permutations within an 11-unit I/O unit library can produce 39,916,800 I/O units (11!), far exceeding the capacity of current IC layout software. However, the proposed algorithm generates the layout file within 1 second and significantly reduces the DRC verification time from infinite duration to 63 seconds executing 415 DRC rules. This approach effectively improves the potential to detect layer density errors in I/O libraries. While conventional processes detect layer density and DRC issues only when adjacent I/O cells are placed due to layout size and machine constraints, in this work, the proposed algorithm selectively generates multiple distinct combinations of I/O cells for verification, crucial for improving the accuracy of physical design.

Specific cell adhesion is essential for functional biointerfaces, especially in cancer diagnosis. However, the role of surface nanotopography in this process remains unclear. Herein, we reveal the critical role of surface nanotopography by measuring adhesion forces utilizing fluidic force microscopy (FluidFM). The antibody-coated nanospiky surface exhibits cell adhesion force 1 to 2 orders of magnitude higher than those of the flat, nanospiky, and antibody-coated flat surfaces. This amplified effect is related to a time-dependent reversal, with adhesion force on nanospiky surfaces initially weaker than that on flat surfaces but eventually surpassing it. Mathematical simulations further demonstrate that micro-nanostructured surfaces maximize contact points, enabling multiscale, multipoint cell-substrate interactions, consistent with experimental results. From thermodynamic and kinetic perspectives, we propose a multiscale, multipoint recognition model based on the synergistic effect of topographical matching and molecular recognition. Our findings provide valuable clues for biointerface design in cancer diagnosis, drug screening, and tissue engineering.

Cell-free massive MIMO has emerged as a key technology that is envisioned to play a central role in future wireless networks. It leverages the benefits of massive MIMO by utilizing a large number of access points (APs) distributed over a large coverage area to concurrently serve multiple user equipment (UEs). In its canonical form, each user is served by all the APs which is impractical. It is therefore important to carefully select groups of APs that will participate in serving each UE. In this work, we propose a method to form efficient UE-AP association clusters using matching theory, by modeling the problem as a many-to-many matching with externalities. We consider that the UEs exhibit partially altruistic behavior and therefore select APs in an empathetic way, aiming to improve their neighbor’s rate in addition to their own. Simulation results show that we can improve the system sum spectral efficiency, outage probability and the average energy efficiency compared to existing methods.

The turbocharging of hydrogen fuel cell systems (FCSs) has recently become a prominent research area, aiming to improve FCS efficiency to help decarbonise the energy and transport sectors. This work compares the performance of an electrically assisted variable-geometry turbocharger (VGT) with a fixed-geometry turbocharger (FGT) by optimising both the sizing of the components and their operating points, ensuring both designs are compared at their respective peak performance. A MATLAB-Simulink reduced-order model is used first to identify the most efficient components that match the fuel cell air path requirements. Maps representing the compressor and turbines are then evaluated in a 1D flow model to optimise cathode pressure and stoichiometry operating targets for net system efficiency, using an accelerated genetic algorithm (A-GA). Good agreement was observed between the two models’ trends with a less than 10.5% difference between their normalised e-motor power across all operating points. Under optimised conditions, the VGT showed a less than 0.25% increase in fuel cell system efficiency compared to the use of an FGT. However, a sensitivity study demonstrates significantly lower sensitivity when operating at non-ideal flows and pressures for the VGT when compared to the FGT, suggesting that VGTs may provide a higher level of tolerance under variable environmental conditions such as ambient temperature, humidity, and transient loading. Overall, it is concluded that the efficiency benefits of VGT are marginal, and therefore not necessarily significant enough to justify the additional cost and complexity that they introduce.

Viscoelasticity is now recognised as a key parameter in modulating cell behaviour. Tailoring time-dependent materials to elicit specific cellular responses is, however, a challenge because of the intricate relationship between the substrate relaxation time (τrel) and the cell sensing time-window which depends on the time required for the formation of focal adhesions (τb) and the duration of their lifetime (τL). Here, we introduce a novel design approach to guide cell behaviour based on the cell-perceived Deborah number, De = τrel/τL, arguing that for De > 1 and De < 1, substrates promote cell differentiation because stable adhesions and sustained tension drive mechanotransduction and lineage-specific differentiation on the basis of substrate stiffness. Instead, cell stemness is maintained in the De ∼1, whereby excessive mechanical signalling is prevented as cells balance adhesion stability and plasticity. The design workflow consists in modelling substrate τrel, enabling the selection of the optimal gel formulation according to the cell-perceived De. The workflow was applied to agarose gels with different dextran concentrations in the liquid phase, which act as modulators of mechanical time-dependent properties. To predict the relaxation times for these gels, we developed an in-silico model which integrates their structural and transport properties. Our results show that the gels have an almost constant equilibrium elastic modulus, while their τrel decreases with increasing dextran concentration in the liquid phase. Considering adipose-derived mesenchymal stem cells (ADSCs) and their characteristics sensing times, we defined dextran concentrations to mimic the different De conditions in the agarose gels. Experimental cell investigations confirmed the validity of the design approach: ADSC differentiation, highlighted by YAP nuclear translocation, was promoted in the case of De < 1 and De > 1, respectively eliciting adipogenic and osteogenic lineages. On the other hand, cells maintained their stemness when De ∼1. This study provides novel insights on the interplay between hydrogel viscoelasticity and cellular behaviour and paves the way for precision design of viscoelastic biomaterials for in-vitro studies and regenerative medicine.

Tandem solar cells are a promising technology for improving energy conversion efficiency by stacking multiple cells with different bandgaps. This study focuses on a two-terminal silicon/silicon (Si/Si) tandem solar cell consisting of a tunnel oxide passivated contact top cell and a bifacial silicon bottom cell. A key aspect of this research is the albedo effect, which refers to the reflection of Sunlight from surroundings. This reflected light enhances the absorption in bifacial solar cells, thereby increasing their efficiency. We systematically investigated how varying the bottom cell size (ranging from 4/4 cm to 4/1 cm) and different albedo intensities affect the overall performance. Experimental results reveal that a 4/1 cm tandem configuration achieves 29.67% efficiency under 0.4 Suns of albedo, demonstrating a significant boost compared to standard configurations. The findings highlight the potential of area matching and albedo optimization as key strategies for enhancing tandem solar cell efficiency. Future research will focus on further design improvements, optimizing material properties, and scaling this approach for real-world applications in photovoltaic systems.

Lymphocyte migration plays a crucial role in the progression of autoimmune and inflammatory diseases, and the inhibition of autoreactive immune cells is an attractive therapeutic strategy. Pepitem is an endogenous modulator of lymphocyte migration. In this study, we implemented a structural scaffold matching approach to engineer of stabilized pepitem-based probes. Prioritizing the native helix–loop–helix structure of pepitem, protein structure databases were mined to identify the structurally closest peptide scaffold. Leveraging this strategy, we developed VhTI-pep 2, inhibiting CD3+ T-lymphocyte migration in vitro with a comparable potency (EC50 = 10.6 ± 16.5 nM) to pepitem (EC50 = 6.0 ± 6.4 nM). Its potency was further extended to T-cell subsets derived from multiple sclerosis patients and highly disease-driving memory and Th1 cell populations. Our approach will guide the design of stabilized peptide probes and future therapeutics, overcoming the challenges associated with flexible and linear peptides.

Cryogenic electron microscopy can be used to determine the structure of biomolecules in their native cellular environment, provided that sufficiently thin slices of cells can be prepared. However, compared to single-particle reconstruction, significant challenges remain, including lower throughput in data acquisition and difficulties in identifying targets within the dense cellular milieu. An emerging approach to overcome these challenges is 2D template matching (2DTM), which employs a brute-force cross-correlation search between untilted exposures of cellular samples and all possible projections of a template to detect targets. From the resulting detections, high- resolution reconstructions can be generated using standard single-particle approaches. A key concern in this approach is template bias, which can dominate the reconstruction. In practice, regions of biological interest can be removed from the template to obtain bias-free reconstructions of these regions. Since data collection does not require tilting, we can routinely acquire more than 10,000 exposures per day. Another advantage of 2DTM is that it lends itself to montaged imaging approaches, thereby maximizing the use of cell slices, whose preparation is time consuming. In this talk, I will present our current best practices for 2DTM data collection and processing, along with recent work on benchmarking sample damage in cryogenic sections and characterizing translational states in the pathogenic fungus Candida albicans.

Activity detection plays an important role in grantfree random access, a promising approach for handling a large number of users in use cases like massive machine type communication (mMTC). Existing activity detection algorithms cover various scenarios but overlook wideband distributed antenna systems, a practical configuration for next-generation wireless networks. When following conventional activity detection methods, sparse Bayesian learning (SBL) could be an option in this case. However, SBL-based methods for wideband systems lack the consistency of maximum likelihood estimation (MLE), resulting in unsatisfactory detection performance. This paper proposes a novel distributed activity detection framework for wideband cell-free multiple-input and multiple-output (MIMO). Specifically, we provide a novel uplink channel model for activity detection in wideband cell-free MIMO, accounting for asynchronous reception. Additionally, we present possible SBLbased methods, identifying their limitations, which motivates the development of a new approach for activity detection. Next, we propose a covariance-matching distributed activity detection framework that matches the sample covariance matrix to the estimated covariance matrix. Simulation results demonstrate the effectiveness of the proposed distributed algorithm.

Defect detection for photovoltaic (PV) cell images is a challenging task due to the small size of the defect features and the complexity of the background characteristics. Modern detectors rely mostly on proxy learning objectives for prediction and on manual post-processing components. One-to-one set matching is a critical design for DEtection TRansformer (DETR) in order to provide end-to-end capability, so that does not need a hand-crafted Efficient Non-Maximum Suppression NMS. In order to detect PV cell defects faster and better, a technology called the PV cell Defects DEtection Transformer (PD-DETR) is proposed. To address the issue of slow convergence caused by DETR’s direct translation of image feature mapping into target detection results, we created a hybrid feature module. To achieve a balance between performance and computation, the image features are passed through a scoring network and dilated convolution, respectively, to obtain the foreground fine feature and contour high-frequency feature. The two features are then adaptively intercepted and fused. The capacity of the model to detect small-scale defects under complex background conditions is improved by the addition of high-frequency information. Furthermore, too few positive queries will be assigned to the defect target via one-to-one set matching, which will result in sparse supervision of the encoder and impair the decoder’s ability of attention learning. Consequently, we enhanced the detection effect by combining the original DETR with the one-to-many matching branch. Specifically, two Faster RCNN detection heads were added during training. To maintain the end-to-end benefits of DETR, inference is still performed using the original one-to-one set matching. Our model implements 64.7% AP on the PVEL-AD dataset.

We propose a novel matching-based pilot assignment scheme to reduce the pilot contamination effect in cell-free massive multiple-input-multiple-output (MIMO) systems with low computational complexity. We use a many-to-many matching scheme to solve the AP selection problem so that the maximum number of UEs in a UE group equals the number of orthogonal pilots. Furthermore, orthogonal pilots are assigned to each UE group in descending order of the common serving AP ratio of UE groups. Simulation results demonstrate that the proposed algorithm achieves near-optimum performance and outperforms the existing pilot assignment schemes in terms of 95%-likely per-user throughput and computational complexity, especially in scenarios with a limited number of pilots.

No abstract available

The canonical setup is the primary approach adopted in cell-free multiple-input multiple-output (MIMO) networks, in which all access points (APs) jointly serve every user equipment (UE). This approach is not scalable in terms of computational complexity and fronthaul signaling becoming impractical in large networks. This work adopts a user-centric approach, a scalable alternative in which only a set of preferred APs jointly serve a UE. Forming the optimal cluster of APs for each UE is a challenging task, especially, when it needs to be dynamically adjusted to meet the quality of service (QoS) requirements of the UE. This complexity is even exacerbated when considering the constrained fronthaul capacity of the UE and the AP. We solve this problem with a novel many-to-many matching game. More specifically, we devise an early acceptance matching algorithm, which immediately admits or rejects UEs based on their requests and available radio resources. The proposed solution significantly reduces the fronthaul signaling while satisfying the maximum of UEs in terms of requested QoS compared to state-of-the-art approaches.

This study evaluates the dosimetric impact of tumor matching (TM) and bone matching (BM) in carbon ion radiotherapy for locally advanced non-small cell lung cancer. Forty patients diagnosed with locally advanced non-small cell lung cancer were included in this study. TM and BM techniques were employed for recalculation based on re-evaluation computed tomography (CT) images of the patients, resulting in the generation of dose distributions: Plan-T and Plan-B, respectively. These distributions were compared with the original dose distribution, Plan-O. The percentage of the internal gross tumor volume (iGTV) receiving a prescription dose greater than 95% (V95%) was evaluated using dose-volume parameters. Statistical analysis was performed using a paired signed-rank sum test. Additionally, the study investigated the influence of tumor displacement, volume changes, and rotational errors on target dose coverage. The median iGTV V95% values for the Plan-O, Plan-T, and Plan-B groups were 100%, 99.93%, and 99.60%, respectively, with statistically significant differences observed. TM demonstrated improved target dose coverage compared to BM. Moreover, TM exhibited better target coverage in case of larger tumor displacement. TM’s increased adjustability in rotation directions compared to BM significantly influenced dosimetric outcomes, rendering it more tolerant to variations in tumor morphology. TM exhibited superior target dose coverage compared to BM, particularly in cases of larger tumor displacement. TM also demonstrated better tolerance to variations in tumor morphology.

The stress status of a soil pressure cell placed in soil is very different from its stress state in a uniform fluid medium. The use of the calibration coefficient provided by the soil pressure cell manufacturer will produce a large error. In order to improve the measurement accuracy of the interface-type earth pressure cell placed in soil, this paper focuses on a single-membrane resistive earth pressure cell installed on the surface of a structure, analyzing the influence of loading and unloading cycles, the thickness and particle size of the sand filling, and the depth of the earth pressure cell inserted in the structure on the calibration curve and matching error, which were analyzed through calibration tests. The results show that when the sand filling thickness is less than D (D is the diameter of the earth pressure cell), the calibration curve is unstable in relation to the increase in the number of loading and unloading cycles, which will cause the sand calibration coefficient used for stress conversion to not be used normally. When the sand filling thickness in the calibration bucket increases from 0.285D to 5D, the absolute value of the matching error first decreases and then increases, such that the optimal sand filling thickness is 3D. The output of the earth pressure cell increases with the decrease in sand particle size under the same load, and there is a significant difference between the theoretical calculation value and the experimental value of the matching error; aiming at this difference, an empirical formula is derived to reflect the ratio of the diameter of the induction diaphragm of the earth pressure cell to the maximum particle size of the sand filling. When the depth of the earth pressure cell inserted in the structure is “0”, the sensing surface is flush with the structure and the absolute value of the matching error is the smallest. Changes in the horizontal placement of the soil pressure cell in the calibration bucket result in significant differences in both the output and hysteresis of the calibration curve. To improve the measurement accuracy of soil pressure cells in scaled tests for applications such as in the retaining walls of excavation pits, tunnel outer surfaces, pile tops, pile ends, and soil pressure measurements in soil, calibration of the soil pressure cells is required before testing. Due to the considerable difference in the stress states of the soil pressure cell between granular media and uniform fluid media, calibration in soil is essential. During in-soil calibration, factors such as cyclic loading and unloading, soil compression, sand thickness and particle size, and the placement of the soil pressure cell all affect the calibration results. This paper primarily investigates the influence of these factors on the calibration curve and matching error. This study found that, as the sand thickness increases, the matching error decreases initially and then increases.

Histopathological imaging is vital for cancer research and clinical practice, with multiplexed Immunofluorescence (MxIF) and Hematoxylin and Eosin (H&E) providing complementary insights. However, aligning different stains at the cell level remains a challenge due to modality differences. In this paper, we present a novel framework for multimodal image alignment using cell segmentation outcomes. By treating cells as point sets, we apply Coherent Point Drift (CPD) for initial alignment and refine it with Graph Matching (GM). Evaluated on ovarian cancer tissue microarrays (TMAs), our method achieves high alignment accuracy, enabling integration of cell-level features across modalities and generating virtual H&E images from MxIF data for enhanced clinical interpretation.

Article Study on Performance Simulation Matching of One-Dimensional Hydrogen Storage and Supply System for Hydrogen Fuel Cell Vehicles Qi Liu 1,2, * , Biao Xiong 1,3, Yuxuan Liu 1,3, Chuanyu Zhang 1,3, Shuo Yuan 1,2, and Wenshang Ma 1,3 1 College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China 2 Research Institute of Hunan University in Chongqing, Chongqing 401120, China 3 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China * Correspondence: author: hnuliuqi@hnu.edu.cn Received: 1 July 2024; Accepted: 12 September 2024; Published: 27 September 2024 Abstract: With the improvement of environmental protection requirements, hydrogen fuel cell vehicles are considered one of the most potential and promising new energy vehicles because of their advantages, such as pollution-free emission, long cruising range, and short hydrogenation time. However, there are still unresolved problems between the storage and supply of hydrogen and the power demand during the operation of hydrogen fuel cell vehicles. In this study, a hydrogen fuel cell vehicle is taken as the research object, and a one-dimensional model is built according to the basic performance parameters so as to explore the operation law of the power performance demand of the hydrogen fuel cell vehicle, simulate the power demand in the actual operation process, summarize the influence of different parameters on the power economic performance of the vehicle, and put forward optimization strategies to improve the power, durability, and fuel economy of the vehicle.

Cognitive radio (CR) is one of the best option for enhancing spectrum usage in the future generation of cellular-based networks. An essential stage of cognitive radio is the spectrum sensing process, which detects unused spectrum holes in order to use all available frequencies of the radio spectrum. The sampling rate over a multi-GHz bandwidth is the main challenge in the spectrum sensing architecture design. More recently, to be able to use a low sampling rate, Compressed Sensing (CS) technology has been used. In this paper, an enhanced spectrum sensing architecture for reconstructing the original signal is presented. The architecture is based on a modified Orthogonal Matching Pursuit (OMP) algorithm. The proposed architecture is implemented with 1024 samples, a measurement vector with a size of 256 and a sparsity of 36. The proposed architecture can be executed on 132 MHz clock frequency, using Xilinx Virtex 6 FPGA, with a speed up of 32% in comparison with previously published work.

INTRODUCTION: During the operation of large photovoltaic power stations, they are often shielded by dust and bird droppings, which greatly reduce the power generation and even cause fires. Analysis of PV cell occlusion image recognition accuracy based on sub-pixel matching. OBJECTIVES: In order to find the location of the pv cells, we use the method of subpixel image matching. Improve recognition accuracy. METHODS: When the power plant is running normally, taken the original image for photovoltaic power station as the original sample, and then using the subpixel gradient matching algorithm, to match the original image and find out that the minimum matching values. RESULTS: If the calculation results is greater than a specified threshold, When the calculated result is greater than the specified threshold, the power station is considered abnormal. CONCLUSION: The experimental process shows that this method can better judge the operating status of photovoltaic power station, and can find out the location of mismatched photovoltaic cells more accurately, and the calculation accuracy reaches sub-pixel level.

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In this letter, we consider beam selection in cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To address the beam selection of all access points in the network for cell-free operation, a beam selection problem that maximizes the minimum of spectral efficiencies of all user equipments is formulated. We propose an extended deferred acceptance algorithm for beam selection based on the matching theory. Additionally, we propose two intuitive beam selection algorithms. System-level simulation results demonstrate the advantage of the proposed matching theory-based beam selection method in terms of spectral efficiency for cell-free mmWave massive MIMO systems.

Carbon-based hole transport layer-free perovskite solar cells (PSCs) based on methylammonium lead triiodide (MAPbI3 ) have become one of the research focus due to low cost, easy preparation, and good optoelectronic properties. However, instability of perovskite under vacancy defects and stress-strain makes it difficult to achieve high-efficiency and stable power output. Here, a soft-structured long-chain 2D pentanamine iodide (abbreviated as "PI") is used to improve perovskite quality and interfacial mechanical compatibility. PI containing CH3 (CH2 )4 NH3 + and I- ions not only passivate defects at grain boundaries, but also effectively alleviate residual stress during high temperature annealing via decreasing Young's modulus of perovskite film. Most importantly, PI effectively increases matching degree of Young's modulus between MAPbI3 (47.1 GPa) and carbon (6.7 GPa), and strengthens adhesive fracture energy (Gc ) between perovskite and carbon, which is helpful for outward release of nascent interfacial stress generated under service conditions. Consequently, photoelectric conversion efficiency (PCE) of optimal device is enhanced from 10.85% to 13.76% and operational stability is also significantly improved. 83.1% output is maintained after aging for 720 h at room temperature and 25-60% relative humidity (RH). This strategy of regulation from chemistry and physics provides a strategy for efficient and stable carbon-based PSCs.

Photocurrent matching in conventional monolithic tandem solar cells is achieved by choosing semiconductors with complementary absorption spectra and by carefully adjusting the optical properties of the complete top and bottom stacks. However, for thin film photovoltaic technologies at the module level, another design variable significantly alleviates the task of photocurrent matching, namely the cell width, whose modification can be readily realized by the adjustment of the module layout. Herein, this concept is demonstrated at the experimental level for the first time for a 2T‐mechanically stacked perovskite (FAPbBr3)/organic (PM6:Y6:PCBM) tandem mini‐module, an unprecedented approach for these emergent photovoltaic technologies fabricated in an independent manner. An excellent I sc matching is achieved by tuning the cell widths of the perovskite and organic modules to 7.22 mm (PCE PVKT‐mod = 6.69%) and 3.19 mm (PCE OPV‐mod = 12.46%), respectively, leading to a champion efficiency of 14.94% for the tandem module interconnected in series with an aperture area of 20.25 cm2. Rather than demonstrating high efficiencies at the level of small lab cells, this successful experimental proof‐of‐concept at the module level proves to be particularly useful to couple devices with non‐complementary semiconductors, either in series or in parallel electrical connection, hence overcoming the limitations imposed by the monolithic structure.

Development and regeneration in biological tissues are fundamentally affected by stem‐cell‐fate commitment. Bioelectricity is heterogeneous between different tissues and crucially regulates cell behaviors, including cell differentiation. However, the effects of heterogeneous bioelectricity on stem‐cell differentiation remain poorly understood. Herein, it is shown that providing stem cells with electrical stimulation matching the endogenous membrane potentials of cells derived from different tissues (osteogenic‐related: −55.05 ± 4.22 mV, neurogenic‐related: −84.8 ± 7.48 mV) can induce their osteogenic or neurogenic lineage commitment. Molecular dynamics simulations indicated that the osteogenic‐related surface potential favors the adsorption of fibronectin, while the neurogenic‐related surface potential enhances the adsorption of FGF‐2. These different protein adsorptions trigger either downstream Wnt or Erk signaling, which direct stem‐cell differentiation. Surface‐potential‐mediated lineage‐specification of stem cells using bioelectrical intensity has enormous potential application value in tissue regenerative therapy.

Cell-free massive MIMO is a promising technology to meet the requirements of future wireless networks. It uses multiple access points (APs) spread over a large coverage area to serve multiple User Equipment (UEs) simultaneously. In order to make it practical and ensure good performance, it is important to carefully determine which APs should serve particular users. In this work, we propose a method to efficiently select these clusters using matching theory - by modeling the problem as a many-to-many matching with externalities. We consider both the UEs and APs to be altruistic, and therefore select AP clusters with the goal of improving the network sum spectral efficiency, rather than their own rate. Simulation results show that we can improve the outage probability and the average energy efficiency compared to existing methods.

A multi-objective optimization based parameter configuration method is proposed to study the parameter matching problem of a hybrid power system for fuel cell vehicles, which consists of a fuel cell, a battery, and a supercapacitor. A multi-objective optimization function that the evaluation criteria are the life cycle cost of the hybrid system and the daily performance degradation rate of the fuel cell is established, and the NSGA-II algorithm is used to design the parameter optimization process. Specific operating conditions are used for simulation verification. The results indicate that the scheme with the minimum degradation rate has a 23.5% lower daily fuel cell degradation rate than the one with the minimum cost, and a reasonable configuration can reduce the degradation rate of the fuel cell without causing a sharp increase in cost.

With the promotion of carbon reduction target in new energy vehicles, fuel cell hybrid vehicles driven by proton exchange membrane fuel cells (PEMFC) as the main power source are developing rapidly. In this paper, a novel power allocation rule, named pattern-matching control strategy is proposed for the hybrid power system of a fuel cell vehicle (FCV) with PEMFC and auxiliary lithium battery. To investigate the impacts of the designed strategy on the FCV, both the operation characteristics and economic performance of the power system are analyzed under NEDC (New European Driving Cycle) condition. The results show that comparing to the normally used power-following control strategy, the terminal SOC of the lithium battery in the FCV by using the proposed pattern-matching control strategy is 5.36% lower, which indicates a higher utilization of regenerative braking power and reduces the power requirement of the fuel cell. The hydrogen consumption of the vehicle with the proposed strategy is 3.73% less than that of the normal one and the overall energy efficiency increases by 3.88%.

One of the essential challenges of tandem solar cells is designing and adjusting the current-matched tandem structures with high efficiency and stability. Nitride-based wide band gap semiconductors, owing to their high stability and high resistance against the cosmic rays, are appropriate elements to apply as the top cell of tandem solar cells. On the other hand, the organic-inorganic hybrid perovskites are emerging materials with exclusive electronic properties such as tunable band gap, low cost, simple manufacturing process, and efficient charge transport properties, making them capable candidates to be used in tandem layered structures. The aim of this paper is to adjust and optimize the performance of a two-terminal tandem solar cell consisting of InxGa1-xN as the top cell and a FAPbIyBr3-y as the bottom cell. We have studied the effect of different practical parameters such as the indium molar of the top cell, iodine molar of the bottom cell, the thickness of each layer, threading dislocation density of InxGa1-xN, and surface texturing effect on the performance of the two-terminal tandem structure. Because of the prominence of current matching problem in two-terminal tandem structures, we have determined the optimum situation for maximum light harvesting along with the minimum value of current matching factor. In the optimum situation, the current matching factor of 0.15 mA/cm2 leads to the power conversion efficiency of 25.17% for the device.

Mobile edge computing (MEC) enables computing services at the network edge closer to mobile users (MUs) to reduce network transmission latency and energy consumption. Deploying edge computing servers in small base stations (SBSs), operators make profit by offering MUs with computing services, while MUs purchase services to solve their own computation tasks quickly and energy-efficiently. In this context, it is of particular importance to optimize computing resource allocation and computing service pricing in each SBS, subject to its limited computing and communication resources. To address this issue, we formulate an optimization problem of computing resource management and trading in small-cell networks and tackle this problem using a two-tier matching. Specifically, the first tier targets at the association algorithm between MUs and SBSs to achieve maximum social welfare, and the second tier focuses on the collaboration algorithm among SBSs to make efficient usage of limited computing resources. We further show that the two proposed algorithms contribute to stable matchings and achieve weak Pareto optimality. In particular, we verify that the first algorithm arrives at a competitive equilibrium. Simulation results demonstrate that our proposed algorithms can achieve a better network social welfare than baseline algorithms while retaining a close-optimal performance.

Conventional forklifts face serious issues, with internal combustion engine models causing indoor pollution, and lead-acid battery variants having slow charging and reduced power output as the charge diminishes. To address these drawbacks, this paper introduces a 2.5-tonne fuel cell forklift designed for Hong Kong's bustling logistics, warehousing, and transportation needs. It presents the development of dynamic simulation and cycle condition models, incorporating life cycle cost and average efficiency functions. Simulations reveal that selecting a 50-cell stack (rated at 11.8 kW) is the most cost-effective option, reducing hydrogen consumption by 2.3% using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimisation. Cycle conditions do not alter the stack's optimal working voltage. However, the stack's voltage is influenced by stack and hydrogen prices, requiring an optimal design based on Hong Kong's actual costs. This study provides a theoretical foundation for future fuel cell forklift design through techno-economic analysis.

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ABSTRACT With the increased use of collaborative robots, a new production model of the human-robot collaborative hybrid assembly cell (HRCHAC) is becoming a new trend in customised production. Collaborative assembly between workers and robots in assembly cells can significantly increase productivity and improve the well-being of workers once the distribution of tasks and resources is optimised. This paper proposes a new integrated task allocation model to better utilise human-robot collaboration to increase productivity and improve worker well-being. The developed model enables the skills of both workers and robots to be fully utilised while ensuring economic efficiency and the effective protection of workers’ physiological and psychological health. First, the product assembly process is decomposed into several assembly tasks, and the characteristics of each task are analysed. Second, a bi-objective mixed-integer planning model is developed with the objectives of minimising unit product assembly time and maximising total task matching. The ergonomics-related objectives are considered in terms of both the physiological and psychological fatigue of the worker, and relevant constraints are established. An improved NSGA-II algorithm is developed to determine the final task allocation scheme. Finally, the proposed method is applied to a real industrial case to verify the effectiveness of the approach.

Aiming at the problems of insufficient accuracy caused by empirical value-dependent ohmic internal resistance ($\mathbf{R}$) in traditional equivalent circuit battery voltage simulation models and the lack of physical interpretability in pure data-driven models, this paper proposes an equivalent circuit modeling method based on data-driven parameter optimization. First, electrical parameter data of lithium iron phosphate batteries across the full State of Charge (SOC) range were collected through multi-round controlled charge-discharge experiments. The Sequential Least Squares Programming (SLSQP) monotonic constraint algorithm was introduced, and a high-precision SOC-Open Circuit Voltage (SOC-OCV) relationship model was constructed by combining 11th-order polynomial regression. The Mean Squared Error (MSE) was controlled at $3.588 \times 10^{-6}$, which solves the nonmonotonic fluctuation problem of traditional SOC-OCV models and provides reliable basic input for equivalent circuit modeling. Second, abandoning the traditional empirical value method, a linear regression model was built based on experimental data using the data-driven idea to accurately solve $\mathbf{R}$ of the equivalent circuit, reducing the identification error of R to the $0.00001 \Omega$ level. Finally, a complete equivalent circuit voltage simulation model was constructed based on the optimized SOC-OCV model and the data-driven solved R, and its performance was compared with the traditional empirical parameter equivalent circuit model and the pure data-driven model (XGBoost). Experimental results show that the proportion of data points with voltage simulation error $\leqslant$ 0.03 of the proposed model reaches $58 \%$, which is 23 percentage points higher than that of the traditional empirical parameter equivalent circuit model. While maintaining physical interpretability, its accuracy is better than that of the pure datadriven model (with an error reduction of $15 \%$), which can provide an efficient and reliable technical solution for voltage simulation of user-side energy storage systems.

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The survey delf into the intricate dynamics of battery behavior nether change temperature conditions, use angstrom comprehensive examination 2RC equivalent circuit battery model. by analyze the relationship between temperature, state of charge (SOC), and voltage feature, the research shed light on the fundamental factor influence battery performance. This probe not lone light the impact of temperature on electrochemical chemical reaction inside the battery merely besides underscore the necessity for effective thermal management scheme to optimize performance and prolong battery life. furthermore, the survey research the deduction of temperature variation on battery thermal profile, identify potential hot spot and hazard that May compromise functionality and safety. The findings supply valuable penetration for engineer and research worker to develop advance thermal management system and compensation algorithm, thereby enhance the dependability and efficiency of battery system, particularly inch demand urban transportation system environment.

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The fuel cell propulsion system is one of the potential development directions for future green aviation. Due to the slow dynamic response of fuel cells, they need to be used together with lithium-ion batteries to form a hybrid power system. The implementation of most functions of lithium-ion batteries requires the State of Charge (SOC) as the basis, and accurate estimation of SOC is of great significance for the stable operation of the battery. Taking into account the requirements for modeling accuracy and complexity, this paper presents an improved second-order RC Equivalent Circuit Model (ECM) that incorporates hysteresis effects. The model parameters were identified using data obtained from Hybrid Pulse Power Characteristic (HPPC) tests. Recognizing the limitations of the Extended Kalman Filter (EKF) algorithm in terms of its interference rejection capabilities, this paper introduces a noise correction matrix via the windowing method to develop an Adaptive Extended Kalman Filter (AEKF). A simulation model was constructed to validate the SOC estimation performance of the proposed algorithm under various operating conditions. The simulation results demonstrate that the AEKF exhibits superior convergence and interference rejection capabilities, with SOC estimation error of less than 1%, meeting the required precision standards.

The global transition toward low-consumption energy systems, reinforced by energy regulations and international climate agreements, highlights the need for accurate modeling of electrical system components. This paper presents a methodology for integrating Equivalent Circuit Modeling (ECM) of battery storage systems into the Optimal Power Flow (OPF) estimation within a Mixed-Integer Nonlinear Programming (MINLP) framework on MATLAB. A second-order ECM is used to represent battery internal losses and dynamic voltage behavior more realistically than idealized models. A case study based on a grid-connected Photovoltaic $(\text{PV})$ system is conducted to evaluate the impact of incorporating real battery behavior into OPF and Locational Average Marginal Price (LAMP). Results show that the inclusion of ECM-based battery efficiency leads to more accurate marginal price calculation considering operational decisions of battery degradation and lifecycle cost.

Voltage fault diagnosis is critical for detecting and identifying the lithium (Li)-ion battery failure. This article proposes a voltage fault diagnosis algorithm based on an equivalent circuit model-informed neural network (ECMINN) method for Li-ion batteries, which aims to learn the voltage fault observer by embedding the equivalent circuit model (ECM) into neural network structures. This method directly embeds the deterministic mechanism part in the ECM and designs the uncertain part into neural networks, which takes advantage of the high precision of the physical model and the strong nonlinear processing ability of the neural network to improve the fault diagnosis effect. In this method, the voltage prediction module and residual-based parameter calibration module are developed to describe state space equations and automatically learn the state parameters and model parameters, respectively. Moreover, a fault score specifically designed by combining the identified model parameters is presented, which is proved to be effectively in detecting different fault types. Finally, experiments on two public datasets are conducted, by which the proposed ECMINN method is demonstrated to be effective and accurate in predicting voltage data and detecting faults. Comparison with three other state-of-the-art methods also reveals the advantages of the proposed ECMINN method.

To monitor and analyze the state of energy storage batteries, two methods for estimating the state of charge (SoC) are proposed: (1) circuit model-based SoC estimation and (2) machine learning-based SoC estimation. For the circuit model-based method, an equivalent circuit for energy storage battery is modeled using resistors and capacitors, and then an extended Kalman filter is utilized to optimize the battery condition estimation. For machine learning-based method, long short-term memory (LSTM) with historical battery data is used to model battery condition estimation. Both methods show good estimation results. However, LSTM exhibits lower error rates in terms of root mean square error (RMSE). Therefore, for energy storage batteries whose performance cannot be tested, historical data-driven LSTM can more accurately estimate the SoC of the battery.

The subject of the article is the study and comparison of two approaches to modelling the battery discharge of a CubeSat satellite: analytical using equivalent circuit and machine learning. The article aims to make a reasoned choice of the approach to modelling the battery discharge of a CubeSat satellite. Modelling the battery discharge of a satellite will enable the prediction of the consequences of disconnecting the autonomous power system and ensure the fault tolerance of equipment in orbit. Therefore, the selected study is relevant and promising. This study focuses on the analysis of CubeSat satellite data, based explicitly on orbital data samples of the power system, which include data available at the time of the article publication. The dataset contains data on the voltage, current, and temperature of the battery and solar panels attached to the five sides of the satellite. In this context, two approaches are considered: analytical modelling based on physical laws and machine learning, which uses empirical data to create a predictive model. Results: A comparative analysis of the modeling results reveals that the equivalent circuit approach has the advantage of transparency, as it identifies possible parameters that facilitate understanding of the relationships. However, the model is less flexible to environmental changes or non-standard satellite behavior. The machine learning model demonstrated more accurate results, as it can account for complex dependencies and adapt to actual conditions, even when they deviate from theoretical assumptions.

Due to the highly nonlinear, dynamic, and slowly time-varying nature of lithium-ion batteries (LIBs) during operation, achieving accurate and real-time parameters online identification in first-order RC equivalent circuit models (ECMs) remains a significant challenge, including low accuracy and poor real-time performance. This paper establishes a fractional-order chaotic system for first-order RC-ECM based on a charge-controlled memristor. The system exhibits chaotic behavior when parameters are tuned. Then, based on the principle of the state observer, an identification observer is designed for each unknown parameter of the first-order RC-ECM, achieving online identification of these unknown parameters of the first-order RC-ECM of LIB. The proposed method addresses key limitations of traditional parameter identification techniques, which often rely on large sample datasets and are sensitive to variations in ambient temperature, road conditions, load states, and battery chemistry. Experimental validation was conducted under the HPPC, DST, and UDDS conditions. Using the actual terminal voltage of a single cell as a reference, the identified first-order RC-ECM parameters enabled accurate prediction of the online terminal voltage. Comparative results demonstrate that the proposed state observer achieves significantly higher accuracy than the forgetting factor recursive least squares (FFRLS) algorithm and Kalman filter (KF) algorithm, while offering superior real-time performance, robustness, and faster convergence.

The factors affecting the battery performance are taken into account, and an accurate equivalent circuit model of the battery model is developed for calculating the parameters of SOC, SOH, etc., in the simulation of the energy storage system along with battery management system. The experimental data were obtained by setting up experiments under different operating conditions, and the data were processed using multiple linear regression methods. Then, the model were determined by an offline identification method, and a battery model based on the multidimensional look-up table method was established. This model can be interpolated according to different operating condition data to adapt to changes in battery performance. Finally, the accuracy of the model and its SOC and SOH calculation strategies are verified by designing simulations and experimental cases.

The paper presents NeuroECM, an equivalent circuit model (ECM) for Li-ion batteries that integrates a Long Short-Term Memory (LSTM) network with Multi-Layer Perceptron (MLP) networks to enhance battery state estimation. The LSTM processes historical current sequences to estimate State-of-charge (SOC), while the MLP networks leverage automatic differentiation to learn battery model parameters. Unlike traditional Mean Squared Error (MSE) based optimization, a weighted smooth Mean Absolute Error (wMAE) loss function is introduced, which prioritizes early-stage accuracy to mitigate long-term error accumulation. Additionally, we enforce physical constraints through a boundary loss to ensure realistic voltage predictions. The proposed NeuroECM framework is validated using 18650 Li-ion battery experimental data, demonstrating highly accurate SOC estimation with an MAE of 0.03963 and terminal voltage prediction with an MAE of 0.04592. The results show that NeuroECM offers high accuracy while ensuring interpretability, making it a promising methodology for realtime battery management.

Accurate diagnosis of current sensor faults is a critical requirement for improving the reliability and safety of lithium-ion battery systems, especially in electric vehicles and renewable energy applications. This paper proposes a comprehensive diagnostic framework that combines enhanced equivalent circuit modeling, adaptive filtering, and artificial intelligence techniques. A five-phase permanent magnet synchronous motor (PMSM) Simulink model is developed to collect operational data, which is pre-processed using an Adaptive Smooth Variable Structure Filter with a time-varying boundary layer (ASVSF-VBL) to suppress noise measurement. State estimation is carried out using a hybrid Kalman Filter–Cuckoo Algorithm (KFCA), while a CNN-LSTM architecture captures spatial and temporal features for effective fault identification. Open-circuit transistor faults are monitored through a pulse-width modulation voltage source inverter, and model parameters are optimized using a Self-adaptive Bonobo Optimizer combined with Least Mean Squares (SaBO-LMS). Simulation studies demonstrate that the proposed method achieves a fault diagnosis accuracy of 94%, significantly outperforming VICO (70%) and RLS-UKFJEM (65%). It also delivers smoother current tracking, higher voltage stability up to 550 V, improved state of charge estimation, and reduced computational complexity. These results confirm the suitability of the proposed approach for real-time intelligent battery management systems.

The RC network in the equivalent circuit model (ECM) can accurately describe the transient of lithium batteries. However, it has many combinations of optimal parameter values, which makes it difficult for the identification results of each parameter to converge to a clear quantitative value and to independently reflect engineering value. This article proposes a method of quantifying the impedance values of multiple RC networks in ECM into a single value impedance, which is suitable for offline rapid identification of battery parameters with strict measurement time limitations. It uses a window-based algorithm to simultaneously observe the parameters and states of the 2RC-ECM model over a continuous period of time. In the subsequent result quantification stage, it extracts the equivalent impedance of the entire transient network based on the excitation current and voltage observations of the 2RC network. In this way, it bypasses the separate quantification of the four circuit parameters of the 2RC network, thereby achieving stable identification of open circuit voltage, ohmic resistance, and polarization resistance simultaneously. Comparative tests using multiple methods show that it can accurately distinguish between ohmic resistance and polarization resistance, and the parameter identification results are stable, reliable, and have good adaptability to working conditions.

An accurate battery model is crucial for a Battery Management System (BMS). However, considering that complex chemical reactions inside the battery, the multi-timescale effect of the battery creates difficulties in designing accurate parameter identification algorithms. This paper proposes a novel two-time-scale method to the parameters identification which described transfer and diffusion process of lithium-ions battery. In this model, a second-order equivalent circuit model (ECM) by the short timescale and long timescale is built by Variable Recursive Least Squares (VRLS), while the State of Charge (SOC) is estimated by an improved Extended Kalman filtering (EKF) model in the short timescale, which employs a forgetting factor. The validation result of this model can track the terminal voltage more accurately with a root mean square error (RMSE) of 0.018V, which is significantly better than that of the conventional ECM by 0.06V. In the future, the parameters related to diffusion process in this model can be used for the State of Health (SOH) estimation on battery aging.

Both electric and hybrid vehicles (HEVs and EVs) employ high-voltage battery packs, so a robust and dependable battery management system (BMS) is necessary to ensure a vehicle's safe and dependable operation. For proper functioning of the BMS, battery state estimation is a key challenge. This paper presents an equivalent circuit and a data driven model, evaluating and contrasting their appropriateness in terms of accuracy as compared to an experimental battery. Furthermore, the state of charge (SOC) of battery is estimated, first with the help of the 2RC equivalent circuit model (ECM), then with the help of an artificial neural network (ANN). A pulsed discharge test is carried out and the SOC and voltage values are noted. The parameters of the 2RC model are tuned for improving the accuracy. The model with different parameters is compared with an experimental battery. With the data driven model, a better accuracy is achieved in comparison with experimental data. With the ANN the mean square error (MSE) of 0.8715% is achieved, whereas with the ECM an MSE of 2.87% is achieved.

The subject of the article is the study and comparison of two approaches to modelling the battery discharge of a CubeSat satellite: analytical using equivalent circuit and machine learning. The article aims to make a reasoned choice of the approach to modelling the battery discharge of a CubeSat satellite. Modelling the battery discharge of a satellite will enable the prediction of the consequences of disconnecting the autonomous power system and ensure the fault tolerance of equipment in orbit. Therefore, the selected study is relevant and promising. This study focuses on the analysis of CubeSat satellite data, based explicitly on orbital data samples of the power system, which include data available at the time of the article’s publication. The dataset contains data on the voltage (mV), current (mA), and temperature (Celsius) of the battery and solar panels attached to the five sides of the satellite. In this context, two approaches are considered: analytical modelling based on physical laws and machine learning, which uses empirical data to create a predictive model. Results: A comparative analysis of the modeling results reveals that the equivalent circuit approach has the advantage of transparency, as it identifies possible parameters that facilitate understanding of the relationships. However, the model is less flexible to environmental changes or non-standard satellite behavior. The machine learning model demonstrated more accurate results, as it can account for complex dependencies and adapt to actual conditions, even when they deviate from theoretical assumptions. However, the model requires prior training on a large amount of data and is less well understood in terms of physical laws. General conclusions. The equivalent circuit approach provides high accuracy and reliability under known conditions, but it is limited when external parameters change. The machine learning approach demonstrates better overall accuracy and stability, especially under variable or unpredictable conditions, but requires a large amount of high-quality data and more complex interpretation. Thus, the most effective approach may be a hybrid one, where the analytical model serves as the basis and machine learning is used as a tool for refining or compensating for inaccuracies.

Accurate state of charge (SOC) estimation is essential for the safety, performance, and longevity of lithium-ion batteries. Physics-based models such as equivalent circuit models (ECMs) are computationally efficient but struggle under nonlinear and time-varying conditions, whereas purely data-driven approaches often lack interpretability. This study proposes a hybrid framework that integrates a physics-informed neural network (PINN) with a first-order Thevenin ECM for dynamic SOC estimation using only terminal voltage and current inputs. The method incorporates physically derived parameters including open-circuit voltage (OCV), polarization resistance, and capacitance identified through pulse testing. An eighth-order OCV–SOC polynomial regression optimized with a genetic algorithm (GA) enables nonlinear mapping, while the Newton–Raphson (NR) method is applied for final SOC estimation. Experimental validation was performed on 18 Ah lithium iron phosphate (LFP) cells over 300 charge–discharge cycles at 20 °C, extended up to 2000 cycles under 1C/2C rates with cut-off voltages of 3.7 V and 2.7 V. Comparative analysis with extended kalman filters (EKF) and standard neural networks (NN) demonstrates the superiority of the proposed method, achieving a root mean squared error (RMSE) of 0.103, mean absolute percentage error (MAPE) of 0.702%, and coefficient of determination (R²) of 0.998. By embedding physical constraints into the learning process, the PINN enhances accuracy, robustness, and generalizability, while reducing estimation uncertainty, thereby offering a scalable and interpretable solution for real-time battery management systems (BMS) in electric vehicles (EVs) and battery energy storage systems (BESS).

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Lithium-ion capacitors (LICs) are promising energy storage devices because they feature the high energy density of lithium-ion batteries and the high power density of supercapacitors. However, the mismatch of electrochemical reaction kinetics between the anode and cathode in LICs makes exploring anode materials with fast ion diffusion and electron transfer channels an urgent task. Herein, the two-dimensional (2D) Ti_3C_2 MXene with controllable terminal groups was introduced into 1D carbon nanofibers to form a 3D conductive network by the electrospinning strategy. In such Ti_3C_2 MXene and carbon matrix composites (named KTi-400@CNFs), the 2D nanosheet structure endows Ti_3C_2 MXene with more active sites for Li^+ ion storage, and the carbon framework is favorable to the conductivity of the composites. Impressively, Ti-O-C bonds are formed at the interface between Ti_3C_2 MXene and the carbon framework. Such chemical bonding in the composites builds a bridge for rapid electron transportation and quick ion diffusion in the longitudinal direction from layer to layer. As a result, the optimized KTi-400@CNFs composites maintain a good capacity of 235 mA h g^−1 for 500 cycles at a current density of 5 A g^−1. The LIC consisting of the KTi-400@CNFs//AC configuration achieves high energy density (114.3 W h kg^−1) and high power density (12.8 kW kg^−1). This paper provides guidance for designing 2D materials and the KTi-400@CNFs composites with such a unique structure and superior electrochemical performance have great potential in the next-generation energy storage fields. 锂离子电容器(LICs)是一种很有前途的储能装置, 因为它们同时具有锂离子电池的高能量密度和超级电容器的高功率密度的特点. 然而, 由于锂离子电容器中阳极和阴极之间电化学反应动力学的不匹配, 使得探索具有快速离子扩散和电子转移通道的阳极材料面临挑战. 在此, 通过静电纺丝策略将具有可控末端基团的二维Ti_3C_2 MXene引入一维碳纳米纤维中, 形成三维导电网络. 在这种Ti_3C_2 MXene和碳基复合材料(称为KTi-400@CNFs)中, 二维纳米片结构赋予了Ti_3C_2 MXene更多Li^+存储活性位点, 而碳骨架则有利于提高复合材料的导电性. 更值得一提的是, 在Ti_3C_2 MXene和碳骨架的界面上形成了Ti-O-C键. 复合材料中的这种化学键为电子的快速传输和离子在层与层之间纵向的快速扩散建立了桥梁. 因此, 优化后的KTi-400@CNFs复合材料在电流密度为5 A g^−1的情况下, 500次循环后仍保持235 mA h g^−1的良好容量. 由KTi-400@CNFs//AC 组成的锂离子电容器实现了高能量密度(114.3 W h kg^−1)和高功率密度(12.8 kW kg^−1). KTi-400@CNFs的这种独特结构和优异的电化学性能为二维材料制备提供了参考.

电动汽车动力电池过度充电容易导致电池加速老化和严重的安全事故。因此, 准确预测车辆充电时间对充电安全防护意义重大。由于电池组结构复杂, 充电方式多样, 传统方法因缺乏充电模式识别而预测精度不高。本文应用数据驱动和机器学习理论, 提出一种新的基于充电模式识别的充电时间预测方法。首先, 基于动态加权密度峰值聚类(DWDPC)和随机森林融合的智能算法对车辆充电模式进行分类; 然后, 采用改进的简化粒子群优化算法(ISPSO)和强跟踪滤波器(STF), 对LSTM神经网络进行优化, 构建了一种高性能的充电时间预测方法; 最后, 通过实际工程数据对所提出的ISPSO-LSTM-STF方法进行了验证。实验结果表明, 该方法能够有效区分充电模式, 提高了充电时间预测精度, 具有实际工程意义。 Overcharging is an important safety issue in the charging process of electric vehicle power batteries, and can easily lead to accelerated battery aging and serious safety accidents. It is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various charging modes, the traditional charging time prediction method often encounters modeling difficulties and low accuracy. In response to the above problems, data drivers and machine learning theories are applied. On the basis of fully considering the different electric vehicle battery management system (BMS) charging modes, a charging time prediction method with charging mode recognition is proposed. First, an intelligent algorithm based on dynamic weighted density peak clustering (DWDPC) and random forest fusion is proposed to classify vehicle charging modes. Then, on the basis of an improved simplified particle swarm optimization (ISPSO) algorithm, a high-performance charging time prediction method is constructed by fully integrating long short-term memory (LSTM) and a strong tracking filter. Finally, the data run by the actual engineering system are verified for the proposed charging time prediction algorithm. Experimental results show that the new method can effectively distinguish the charging modes of different vehicles, identify the charging characteristics of different electric vehicles, and achieve high prediction accuracy.