多维度（含EIS）电芯一致性分析产业化应用面临的主要问题

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.

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Electrochemical impedance spectroscopy (EIS) is a powerful characterization technique for the in-depth investigation of kinetic/transport parameters detection, reaction mechanism understanding, and degradation effects exploration in lithium-ion battery (LIB) systems. However, due to the lack of standardized criterion/paradigm, severe misinterpretations occur frequently during an EIS measurement. In this paper, the significance of instrumental accuracy is described and the character/principle of selection on the simulation model is illuminated/proposed, showing that an adequate precision device and an appropriate fitting model are a prerequisite for a correct EIS analysis. Moreover, the drawbacks of conventional two-electrode EIS experiments for typical coin-type cells are rigorously pointed out by comparison with the ideal three-electrode configuration, where the real impedance information of the cathode would be masked by the sum of both the anode film resistance response and the unavoidable inductive loop signal. The three-electrode case enables efficient accurate observations on individual electrodes, thus facilitating abundant and useful information acquisition. Consequently, devices with a sufficient accuracy, rational simulation models, and advanced three-electrode cells are distinctly illustrated as standardized criterion/paradigm for EIS characterizations, which are essentially important for electrode and interface modifications in LIBs.

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.

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.

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.

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.

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.

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

Understanding and accurately determining battery cell properties is crucial for assessing battery capabilities. Electrochemical impedance spectroscopy (EIS) is commonly employed to evaluate these properties, typically under controlled laboratory conditions with steady‐state measurements. Traditional steady‐state EIS (SSEIS) requires the battery to be at rest to ensure a linear response. However, real‐world applications, such as electric vehicles (EVs), expose batteries to varying states of charge (SOC) and temperature fluctuations, often occurring simultaneously. This study investigates the impact of SOC and temperature on EIS in terms of battery properties and impedance. Initially, SSEIS results were compared with dynamic EIS (DEIS) outcomes after a full charge under changing temperatures. Subsequently, DEIS was analysed using combined SOC and temperature variations during active charging. The study employed a commercial 450 mAh lithium‐ion (Li‐ion) cobalt oxide (LCO) graphite pouch cell, subject to a 1C constant current (CC)–constant voltage (CCCV) charge for SSEIS and CC charge for DEIS, with SOC ranging from 50% to 100% and cell temperatures from 10 to 35°C. The research developed models to interpolate battery impedance data, demonstrating accurate impedance predictions across operating conditions. Findings revealed significant differences between dynamic data and steady‐state results, with DEIS more accurately reflecting real‐use scenarios where the battery is not at equilibrium and exhibits concentration gradients. These models have potential applications in battery management systems (BMSs) for EVs, enabling health assessments by predicting resistance and capacitance changes, thereby ensuring battery cells’ longevity and optimal performance.

This paper proposes a proof-of-concept for a low-cost compact battery cell monitoring system based on Electrochemical Impedance Spectroscopy (EIS). The architecture exploits a multisine excitation signal, which reduces the time required for EIS measurement in the frequency range of interest, enabling online battery monitoring/diagnosis. A comprehensive CAD simulation of the system is performed in TINA-TI to evaluate its performance. The simulation covers the analysis of the general behaviour of the entire system and the complete signal elaboration flow, from analog signal processing to digital analysis of the impedance data based on Discrete Fourier Transform (DFT). This allowed for the analysis of the non-ideal effects of the components, as well as the error induced by the DFT algorithm. Moreover, thermal-noise limitation on the resolution of the system is assessed to verify its capability of detecting variations down to $\mathbf{1}\ \mathbf{m}\boldsymbol{\Omega}$. Finally, the system is tested in the reconstruction of the impedance spectrum of a battery cell described by an integer-order equivalent circuit model, demonstrating good accuracy. The overall system is realized with low-power off-the-shelf components, representing an innovative solution towards the feasibility of an even more integrated sensor node, which will be designed for implementation at cell level for in-operando sensing.

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

The consistency deterioration of lithium-ion battery packs is a critical factor influencing their performance, safety, and other key characteristics. This study develops a mathematical model for the battery pack based on a thermal resistance network, incorporating the non-uniform distribution of cell-to-cell parameters, the heat generation of individual cells, and the heat transfer mechanisms between them. The research investigates the impact of parameter variations on the consistency of battery packs and explores the coupling relationships between these factors and battery life degradation. The results indicate that initial capacity, Coulomb efficiency, and the capacity fade coefficient are the primary factors affecting battery pack consistency. These factors also contribute to the accelerated aging of the battery pack, implying that poorer consistency leads to faster degradation of the battery’s lifespan. This paper provides theoretical insights into maintaining battery pack consistency and extending its cycle life, while offering valuable guidance for the design of battery systems.

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.

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.

The ability to reliably detect the forthcoming failure of a rechargeable cell without removing it from its normal operating environment remains a significant goal in battery research. In this work we have cycled in the laboratory a previously-aged 3.2 A h, 3.6 V 18650 INR LiNixMnyCo 1−x−yO2 cell for 300 d until failure was apparent, using a current waveform representative of use in an electric vehicle application. Electrochemical impedance spectroscopy (EIS) down to 5 µHz was also performed on the cell as a ‘gold-standard’ measure, at the beginning, end and part way through the cycling. Analysis of voltage and current time series data using both parametric (equivalent circuit model) and non-parametric (wavelet-based analysis) approaches allowed us to successfully reconstruct the EIS data. As the battery aged, impedance gradually increased at frequencies between 10−4 Hz—10−1 Hz. The increase accelerated around 50 d before the battery ultimately failed. The acceleration in rate of change of impedance was detectable while the cycle efficiency remained high, indicating that a user of the cell would be unlikely to detect any change in the cell based on its performance or by common measures of state-of-health. The results imply upcoming failure may be detectable from time series analysis weeks before any noticeable drop in cell performance.

In this paper, a 226Ah type lithium-ion power battery module is taken as the research object, the temperature differences across various temperature sensor layout positions are studied, and the feasibility of the existing temperature sensor layout is verified. The test results show that the temperature of conductive aluminum bar is lower than the temperature of the large surface of the cell core, and the maximum temperature difference is maintained at 2 °C under the working condition of less than 1/3C charge-discharge ratio. For driving durable super-power conditions, the temperature of conductive aluminum bar is higher than the large surface temperature, and the maximum instantaneous temperature difference is 5.2 °C. The core temperature of the existing temperature sensor layout scheme does not exceed the maximum monitored value. The instantaneous temperature difference under super power conditions will lead to the discharge power of the cell exceeding the actual capacity, but the pre-undervoltage control strategy of the battery management system will not allow the cell overdischarge, the risk of the existing temperature sensor layout scheme of the battery module structure is controllable. The test results can also provide reference for thermal management system design of power battery system.

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs). The internal resistance consistency is essential to the performance and safety of LIB packs. To detect the consistency of the LIB cell efficiently, an approach using the unbalanced current is proposed. First, a simple bridging circuit model with four LIB cells is built based on the first-order Thevenin equivalent circuit. Different bridging components are compared to minimize the impact on the LIB system. The capacitor is chosen as the bridging component for the capacity loss less than $5.5\,\, {}\times {}10^{-8}$ Ah. The characteristics of capacitor current under different inconsistent degrees and initial state of charge (SOC) are investigated when the internal resistance of the cell is inconsistent. The unbalanced current pulse is generated on the bridging capacitor in an inconsistent pack. To accurately localize the cell with inconsistent internal resistance in the LIB pack, an improved bridging circuit is built. The simulation and experimental results indicate that the polarity and amplitude of the bridging capacitor currents could be used to detect and localize the inconsistent cell or region with an average error of 1.33%. The capacity loss is 10−10–10−9 Ah for the LIB pack with eight modules.

Lithium-ion batteries (LIBs) have become the main power source for electric vehicles and energy storage systems. However, inconsistency and internal short-circuit (ISC) faults of LIBs increase operational risks. In severe cases, thermal runaway accidents are triggered, posing a threat to the safety of users' lives and properties. Therefore, this paper presents research on risk warning strategies for inconsistency assessment and fault diagnosis of LIBs. First, in order to solve the problem of random noise in the measured voltages of the battery module, a data noise reduction method based on Savitzky-Golay filter is proposed. After that, the voltage differences between the cells of the battery pack and the median cell is calculated and recorded as the anomaly factor δ. Thus, the inconsistency assessment of the battery pack is realized. The battery pack fault diagnosis algorithm is constructed by using δ and sample entropy to realize early real-time fault diagnosis of battery packs. Finally, battery pack consistency and ISC faults experiments are performed. Experimental results show that the proposed consistency assessment and fault diagnosis method have good effectiveness, robustness and reliability.

Electrochemical impedance spectroscopy (EIS) will support understanding of electrochemical phenomena in emerging energy devices, including all-solid-state Li ion batteries (ASSLiBs), only when the results are analyzed in a consistent way without arbitrariness. This study1 aimed to establish such a robust analysis for ASSLiB cells using a fast lithium ion conductor Li10 +x Ge1+x P2 −x S12 (LGPS)2, by applying distribution-of-relaxation time (DRT) analysis3. The DRT method distinctively visualized impedance changes depending on states of charge (SOCs). Pellet-type cells were prepared with an In–Li anode, LGPS separator, cathode composite comprising LGPS and LiNbO3-coated LiCoO2 powders4. Figure 1a shows the discharge curve that was recorded for the cell at the current density of 13.2 μA cm−2 (1/40 C-rate) and with the cut-off voltage of 1.9–3.6 V vs. Li+/In–Li (2.55–4.25 V vs. Li+/Li). The EIS measurements were performed during discharge process at various SOCs from 100% to 20%. Nyquist diagrams obtained from the EIS data are shown in Figure 1b, where two broad semicircles are observed at low and high frequency sides. This result indicates at least two electrochemical processes exist in the cell. Both semicircles become large in size upon decreasing SOCs from 100 % down to 20%, indicating that the cell has SOC-dependent electrochemical processes. Figure 1c shows DRT transformation of the EIS data, which helps to separate impedance components in detail. Here horizonal and vertical axes respectively represent the measurement frequency and polarization contribution at each frequency. The center frequency of each distinct peak corresponds to time constant [τ =1/(2πf)] for the respective electrochemical process, and thus the number of peaks matches that of electrochemical processes in the cell. Meanwhile, the peak area represents polarization contribution of each process. Consistently in the whole SOC range, the above DRT analysis indicates the semicircle at high frequency side is divided into two impedance contributions and that at low frequency side cannot be separated any more. This result is consistent with the DRT spectra from additionally prepared symmetric cells; in total three DRT peaks were observed with two peaks from interphase and interface between In–Li/LGPS, and one from LiNbO3-coated LiCoO2/LGPS interface. Accordingly, DRT diagram in Figure 1c was interpreted as indicated by arrows for each peak. This study demonstrated that DRT analysis supports a consistent interpretation of EIS data from ASSLiB cells at various SOCs and with different electrode configurations, and thus implied that the method will also help subsequent equivalent circuit model analysis for reproducible parametrization of electrochemical processes. 1. S. Hori et al., “Understanding the impedance spectra of all-solid-state lithium battery cells with sulfide superionic conductors,” J. Power Sources 556, 232450 (2023). 2. N. Kamaya et al., “A lithium superionic conductor,” Nat. Mater. 10, 682–686 (2011). 3. S. Dierickx, A. Weber, E. Ivers-Tiffée, “How the distribution of relaxation times enhances complex equivalent circuit models for fuel cells,” Electrochim. Acta 355, 136764 (2020). 4. N. Ohta et al., “LiNbO3-coated LiCoO2 as cathode material for all solid-state lithium secondary batteries,” Electrochem. Commun. 9, 1486-1490 (2007). Figure 1

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) 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.

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.

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.

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.

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ABSTRACT Cell inconsistency fault, a critical failure mode in electric vehicle (EV) power battery systems, poses significant threats to the safe operation of EVs. To this end, relying on the voltage data of power battery cells collected by the vehicle manufacturer’s monitoring platform, this paper proposes a fault identification method for power battery cell inconsistency based on the Relative Skewness Density Ratio Outlier Factor (RSDROF). The method is developed by introducing skewness features and adaptively selecting the k-value based on the Local Outlier Factor (LOF) algorithm. First, the natural neighbor search algorithm is adopted to adaptively select the k-value. Second, the relative skewness density ratio outlier factor algorithm is introduced, and the outlier factor of each cell is calculated by combining it with the sliding time window mechanism. Finally, the concept of factor difference is introduced, and potential abnormal cells are identified in advance based on the fault threshold determined by statistical methods. The results show that, compared with the method based on the LOF algorithm, this proposed method can not only accurately identify all potential abnormal cells in vehicles with the “poor battery cell consistency” alarm but also yields more reliable results. In addition, compared with the vehicle enterprise monitoring platform, this method issues an alarm up to 21 days earlier, indicating that the research results have significant practical application value.

This paper proposes a dual-mode AFE system that supports both voltage and electrochemical impedance spectroscopy (EIS) monitoring for multiple cell Li-ion batteries. The proposed current-balanced IA adapts the common mode voltage of the selected cell by taking the same voltage from the same cell, enabling multiple cell monitoring with minimum current. The system also includes a DC-servo loop cancelling the DC component from the AC voltage excited by a current generator. To the best of the author’s knowledge, it is the first BMS AFE system supporting multiple cell EIS monitoring, providing better SoC/SoH estimation and safety for Li-ion batteries.

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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.

This paper deals with the qualification testing to design a battery pack for a fully electric racing motorcycle. To obtain the best performance from the cells and considering the point that in this case the battery pack will be used in special racing condition, finding the best configuration and cells sequence to be connected together in that’s configuration is very important to make the battery pack. To avoid impedance mismatching inside the modules and the final pack, electrochemical impedance spectroscopy, capacity test, and impulse current test has been used to select appropriate cells to be connected in strings and make a uniform and high-performance battery pack with the best configuration based on the categorized cells. Experimental results obtained from the cell to cell variation and also impedance measurement for each module prove the uniformity in impedance.

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.

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

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.

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This study endeavors to assess the impedance responses exhibited by 18650 Graphite||LiCoO 2 (C 6||LCO) cells under conditions of over‐discharge and overcharge. The impedance measurements are conducted at a potential of 4.20 V, followed by subjecting the cells to over‐discharge and overcharge scenarios. It is observed that the impedance magnitude experiences augmentation in both instances. Upon reaching a potential of 2.70 V, the electrochemical attributes of the cells revert to their normal state post over‐discharge. However, the impedance characteristics persist even upon exceeding a potential of 4.70 V. These observations imply that overcharge induces enduring alterations in impedance behavior,

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Given the increasing use of lithium-ion batteries, which is driven in particular by electromobility, the characterization of cells in production and application plays a decisive role in quality assurance. The detection of defects particularly motivates the optimization and development of innovative characterization methods, with simultaneous testing of multiple cells in the context of multi-cell setups having been researched to economize on the number of cell test channels required. In this work, an experimental study is presented demonstrating the influence of a defect type in one cell on five remaining interconnected cells in eight combinatorially varied topologies using galvanostatic electrochemical impedance spectroscopy. The results show that regularities related to the interconnection position are revealed when considering the change in the specific resistance ZIM,min at the transition from the charge transfer to the diffusion region between the reference and fault measurements, allowing it to function as a defect identifier in the present scenario. These results and the extensive measurement data provided can serve as a basis for the evaluation and design of multi-cell setups used for simultaneous impedance-based lithium-ion cell characterizations.

Electrochemical impedance spectroscopy (EIS) is a widely used electroanalytical technique. A consequence of small amplitude modulation of the electrochemical interface is the linearization of inherently nonlinear processes, easing analysis, but giving up some mechanistic discriminating power. An example of this diminished discriminating power is degeneracy among linearized physics-based models [1] and even equivalent circuits [2]. The discriminating power of EIS is further degraded in situations where a two-electrode cell configuration is required, in which case, the total cell impedance is the sum of two half-cell impedances, plus an ohmic drop of the electrolyte. The summative nature of two-electrode EIS (a positive parity signal) makes the assignment of physicochemical processes to one electrode or the other extremely challenging without additional information. We have shown that second harmonic nonlinear electrochemical impedance spectroscopy (NLEIS), a natural extension of EIS achieved with somewhat larger modulations, can break model degeneracy and provide more information than EIS alone. [3,4] An added feature of second harmonic NLEIS acquired in a two-electrode cell is that the signal arises from the difference between each half-cell response (a negative parity signal). The complementary parity between EIS and second harmonic NLEIS, when analyzed with a common physics-based model, makes discriminating half-cell processes from two-electrode measurements feasible. To pave the foundation of NLEIS analysis, the first (EIS) and second harmonic (NLEIS) impedance responses of a simple electrochemical interface are considered, building from well-known Helmholtz double layer, Butler–Volmer kinetics, and solid state Fickian diffusion (to align with experimental lithium insertion chemistry). The linearized portion of this model produces a classic Randles circuit with Warburg impedance, whereas the second harmonic reveals a dependence on charge transfer symmetry and the second derivative of the open circuit voltage with insertion charge. We then adopted and extended Paasch’s macro-homogeneous porous electrode theory [5] to describe the linear and nonlinear impedance responses of a porous electrode. Half-cell models are then combined into whole-cell models to describe lithium-ion battery (LIB) systems. Experimentally, EIS and second harmonic NLEIS obtained with 1.5 Ah Samsung 18650 NMC|C cells are analyzed. Figure 1 demonstrates that our more sophisticated extended Paasch model can accurately fit the positive parity EIS and negative parity NLEIS data with a total of 15 meaningful physicochemical parameters (11 for the linear response and 4 additional for the nonlinear response). A data analysis pipeline is built based on these models to analyze a dataset that composed of 48 commercial LIBs cycled under four different aging conditions, and evaluated at 10%, 30%, and 50% state of charge (SOC). The co-evolution of EIS and NLEIS parameters from our analysis provides several insights, with perhaps the most interesting being the simultaneous increase in charge transfer resistance on the positive electrode and the breaking of charge transfer symmetry at the same time (for low SOC). These results demonstrate that the coupling of EIS and NLEIS can advance electrochemical impedance analysis with small changes from traditional EIS. Open-source software is described that leverages impedance.py to enable the easy implementation of EIS and NLEIS data analysis. References [1] J.R. Wilson, D.T. Schwartz, S.B. Adler, Nonlinear electrochemical impedance spectroscopy for solid oxide fuel cell cathode materials, Electrochimica Acta. 51 (2006) 1389–1402. https://doi.org/10.1016/j.electacta.2005.02.109. [2] S. Fletcher, Tables of degenerate electrical networks for use in the equivalent‐circuit analysis of electrochemical systems, J. Electrochem. Soc. 141 (1994) 1823–1826. https://doi.org/10.1149/1.2055011. [3] M.D. Murbach, D.T. Schwartz, Extending newman’s pseudo-two-dimensional lithium-ion battery impedance simulation approach to include the nonlinear harmonic response, J. Electrochem. Soc. 164 (2017) E3311–E3320. https://doi.org/10.1149/2.0301711jes. [4] M.D. Murbach, V.W. Hu, D.T. Schwartz, Nonlinear electrochemical impedance spectroscopy of lithium-ion batteries: Experimental approach, analysis, and initial findings, J. Electrochem. Soc. 165 (2018) A2758–A2765. https://doi.org/10.1149/2.0711811jes. [5] G. Paasch, K. Micka, P. Gersdorf, Theory of the electrochemical impedance of macrohomogeneous porous electrodes, Electrochimica Acta. 38 (1993) 2653–2662. https://doi.org/10.1016/0013-4686(93)85083-B. Figure 1

Lithium ion-battery (LIB) technology, featuring upstanding energy and power density, satisfying lifetime, high round-trip efficiency and very fast dynamics in a reasonably economic package, rapidly became the undisputed ruler of portable power and it is now the main driver of the electrification of transportation sector. However, despite the fully commercial development, understanding and predicting degradation of such devices is still a great challenge for the scientific and technical community, especially when dealing with real-life-operation induced aging. The reusability of such devices in a circular economy perspective is a hot topic to the sector to improve sustainability, but requires understanding “how” batteries are faded rather than only “how much” they are, to enable a physically consistent and second-life-related estimation of residual lifetime. In the present activity, a detailed analysis of high-power LFP (lithium iron phosphate) cells samples operated on IVECO hybrid buses are performed to assess their possible reusability, in the frame of a joint research cooperation. A large batch of cells with different ages (from 9 years old to brand new) and different positions inside the modules are investigated. State-of-art electrochemical diagnostics are performed embedded in a multi-measurement optimized protocol (full discharge, pulse test, electrochemical impedance spectroscopy) developed after a sensitivity optimization for a model-based improved parameter identification [1], as visible in Figure 1. Residual performances are analyzed and compared with limited on-board data collected from vehicles BMS and analyzed by means of an appositely-improved physical modelling platform. Both capacity and power capabilities have sensibly decreased over the cells lifetime (see Figure 2 for the full discharges at 0.1C, 25°C) with a clear pattern attributable to cells position beneath the modules, prompting the importance of a homogeneous thermal management during operation. Power-based state of health of the samples is higher than a capacity-based SoH, foreseeing a possible reuse of the samples in a power-intensive second-life. Residual performance interestingly feature a high consistency with data recently published in [2], relative to similar system despite operated in a completely different geographic area; this strengthens the generalizability of the results. Aged cells diagnostics were interpreted with a previously developed Newman P2D physical model provided with heath exchange, to identify the degradation mechanisms through the EIS-based parameter identification procedure [1] involving both thermodynamic and kinetic aspects. Interpretation of thermodynamic aging modes has been conducted on all the tested cell samples, based on 0.1C thermodynamic analysis and the differential voltage. Incremental capacity curves of aged cells have been reproduced with a PSO-based (particle-swarm-optimization) identification of three ageing parameters: loss of lithium inventory (LLI), mainly corresponding to solid electrolyte interphase (SEI) growth, and loss of active materials at negative (LAMn) and positive (LAMp) electrode, indicating loss of actives sites for the electrochemical reactions. Despite the severely low residual capacity, the model reproduces satisfyingly the features of the aged cells. Interestingly, a linear trend could be identified as common beneath all the samples involved in the analysis, indicating common aging mechanism appearing with different magnitude. As visible in Figure 3, the trend points out a two-steps aging path: (I) a first pathway mainly leading to LLI close to 20%, followed by (II) a second pathway with both LLI and LAMn occurring at the same time. In the literature, similar observations have been already performed by some authors in previous works, with a lower extent of degradation [3,4]. They agree in this interpretation: the first stage is usually associated to the growth of the SEI layer, while the second stage is usually associated to the onset of lithium plating (due to a dense SEI which inhibits the lithium intercalation into graphite). Confirmation of such interpretation is obtained by means of model based interpretation on non-equilibrium measurements, permitting estimation of physical parameters value and enabling identification of aging mechanisms, together with ex-situ measurements based on electrochemical and morpho chemical analyses. Additionally, several consistencies between on-line measurements, such as EIS, and equilibrium capacity loss of cells have been identified, theoretically discussed and tested under further accelerated aging tests, enabling possible strategies of SoH implementation based on fast and informative electrochemical measurements. References [1]Rabissi et al., doi.org/10.1016/j.est.2022.106435 [2] K. Ramirez-Meyers et al., doi.org/10.1016/j.est.2022.106472 [3] E. Sarasketa-Zabala et al., doi.org/10.1021/jp510071d [4] X.G. Yang et al., https://doi.org/10.1016/j.jpowsour.2017.05.110 Figure 1

As Battery Management Systems (BMSs) continue to expand in both scale and capacity, conventional state-of-charge (SOC) estimation methods—such as Coulomb counting and model-based observers—face increasing challenges in meeting the requirements for cell-level precision, scalability, and adaptability under aging and operating variability. To address these limitations, this study integrates a Deep Neural Network (DNN)–based estimation framework into a node-level BMS architecture, enabling edge-side computation at each individual battery cell. The proposed architecture adopts a decentralized node-level structure with distributed parameter synchronization, in which each BMS node independently performs SOC estimation using shared model parameters. Global battery characteristics are learned through offline training and subsequently synchronized to all nodes, ensuring estimation consistency across large battery arrays while avoiding centralized online computation. This design enhances system scalability and deployment flexibility, particularly in high-voltage battery strings with isolated measurement requirements. The proposed DNN framework consists of two identical functional modules: an offline training module and a real-time estimation module. The training module operates on high-performance computing platforms—such as in-vehicle microcontrollers during idle periods or charging-station servers—using historical charge–discharge data to extract and update battery characteristic parameters. These parameters are then transferred to the real-time estimation chip for adaptive SOC inference. The decentralized BMS node chip integrates preprocessing circuits, a momentum-based optimizer, a first-derivative sigmoid unit, and a weight update module. The design is implemented using the TSMC 40 nm CMOS process and verified on a Xilinx Virtex-5 FPGA. Experimental results using real BMW i3 battery data demonstrate a Root Mean Square Error (RMSE) of 1.853%, with an estimation error range of [4.324%, −4.346%].

Utilizing retired batteries in energy storage systems (ESSs) poses significant challenges due to their inconsistency and safety issues. The implementation of dynamic reconfigurable battery networks (DRBNs) is promising in maintaining the reliability and safety of battery energy storage systems (BESSs). Recently, large-scale BESSs based on DRBN have been deployed with the use of retired batteries, but the operational performance of these systems in real-world working conditions still remains uncertain. To bridge the research gaps, a case analysis is conducted based on the operational data from a real-world large-scale retired BESS. First, the framework of the retired BESS and the structure of the DRBN are illustrated in detail. Then, the core principle of DRBN is comprehensively demonstrated in terms of energy digitization and time-oriented battery management mode. Finally, the operational results indicate that the ESS can effectively improve the voltage and capacity consistency of battery modules and realize efficient thermal management to improve systematic safety of the retired BESS.

Electrochemical impedance spectroscopy (EIS) is a nonintrusive detection technique that is extensively utilized in battery state estimation and fault diagnosis. This paper addresses the challenge of online impedance measurement for large‐capacity cells by employing reconfigurable battery modules to perform such measurements. Given that existing EIS measurement signal forms and design methods are not directly applicable to the impedance measurement of large‐capacity cells and have poor measurement results, this study proposes a time‐optimized multisine group pulse‐width modulation (TO‐MSGPWM). Under the premise of meeting the excitation signal limitations required for online impedance measurement of large‐capacity cells, this approach aims to retain as many advantages of traditional multisine pulse‐width modulation (MSPWM) signals as possible while reducing the total signal duration. A commercial reconfigurable battery module prototype consisting of 16,280 Ah lithium‐ion cells is constructed to validate the feasibility of the proposed excitation signal. The experimental results demonstrate the superiority of the proposed excitation signals compared to traditional MSPWM and Discrete Interval Binary Sequence (DIBS) signals.

The equalizer can greatly improve the consistency of the series-connected battery string, which has been widely used in the field of electric vehicles. However, the existing equalization topology suffers from the disadvantages of slow equalization, large size, and complex control. Therefore, a compact equalization topology is proposed based on flyback conversion. Distinguishing from the traditional flyback conversion topology, the proposed equalizer divides the series-connected battery cells into odd and even groups. The neighboring battery cells share a pair of mosfets, which greatly reduces the number of switches and costs. Moreover, the proposed equalizer has both equalization and charge equalization modes, which can be flexibly applied in different situations. An experimental prototype is designed for 12-cell large-capacity batteries. The experimental results show that the peak equalization current reaches 10 A, which is five times higher than the traditional active equalization topology. The voltage difference is less than 5 mV after equalization. Compared with the existing equalization topologies, the proposed equalizer has a smaller size, faster equalization speed, and optional equalization modes, which can be applied to large-scale battery pack scenarios.

The accurate prediction of lithium-ion battery capacity is crucial for the safe and efficient operation of battery systems. Although data-driven approaches have demonstrated effectiveness in lifetime prediction, the acquisition of lifecycle data for long-life lithium batteries remains a significant challenge, limiting prediction accuracy. Additionally, the varying degradation trends under different operating conditions further hinder the generalizability of existing methods. To address these challenges, we propose a Multi-feature Transfer Learning Framework (MF-TLF) for predicting battery capacity in small-sample scenarios across diverse operating conditions (different temperatures and C-rates). First, we introduce a multi-feature analysis method to extract comprehensive features that characterize battery aging. Second, we develop a transfer learning-based data-driven framework, which leverages pre-trained models trained on large datasets to achieve a strong prediction performance in data-scarce scenarios. Finally, the proposed method is validated using both experimental and open-access datasets. When trained on a small sample dataset, the predicted RMSE error consistently stays within 0.05 Ah. The experimental results highlight the effectiveness of MF-TLF in achieving high prediction accuracy, even with limited data.

Abstract In recent years, battery energy storage has garnered increasing attention in the frequency regulation field due to its rapid and precise output characteristics. The focus of this paper is on the control strategy for battery energy storage that is involved in primary frequency regulation and addresses the coordination control issues of different storage units when implemented on a large scale. A control method is proposed that considers the consistency of the State of Charge (SOC) in battery energy storage, which is involved in primary frequency regulation. The control of the battery energy storage system is divided into upper-level and lower-level control. The upper-level control uses model predictive control to obtain the total frequency regulation power of the energy storage station. The lower-level control allocates the corresponding power based on the capacity of each battery energy storage unit. It introduces a proportional integral adjustment term related to the average SOC to achieve SOC consistency control for each unit. Simulation results demonstrate that, regardless of whether the capacities of various storage units are identical, the proposed method achieves good frequency regulation performance, restores SOC to the ideal range, and ensures SOC consistency among different storage units. This approach aids the efficient and safe operation of battery energy storage systems.

With the rise of distributed power grids, efficient energy storage systems are essential to support the integration of renewable energy. Battery capacity prediction is crucial to optimize energy storage performance, extend battery life, and reduce operating costs. However, the inherent complexity of battery systems and the scarcity of high-quality data pose significant challenges to achieving accurate predictions.Traditional machine learning models, including Feedforward Neural Network (FNN), rely heavily on large datasets to achieve high accuracy. These models often fail to generalize effectively when data is limited. To address this limitation, we propose an informed machine learning model with sparse nonlinear dynamic identification (SINDy) equations as prior knowledge. This method uses cycle times as pseudo-time features and does not require strictly consistent time interval measurements, which can simplify the process of establishing battery capacity dynamic equations using SINDy. Then, the physical model obtained by SINDy is incorporated into FNN training as prior knowledge to ensure the model's prediction accuracy even when data is scarce. Experiments show that the proposed informed machine learning model has higher prediction accuracy for battery capacity with less training data.

The large-scale integration of energy storage in renewable energy systems faces several challenges, including incomplete policies, unclear business models, and suboptimal project economics. The gradual reduction of subsidies for wind and solar energy has further increased the financial pressures on enterprises, making energy storage configurations with low returns cost-prohibitive and lengthening investment payback periods. This paper focuses on the “Renewable Energy + Energy Storage” collaborative model, specifically addressing the multi-objective optimization of large-capacity energy storage systems. A comparative study of energy storage system topologies is conducted, considering multi-dimensional performance factors such as economics, reliability, and flexibility, resulting in the development of an optimal topology scheme. Furthermore, the paper explores multi-level state-of-charge (SOC) balancing control strategies, taking into account the charging and discharging characteristics of batteries. The proposed optimization strategies are validated through simulations.

Accurate prediction of lithium-ion battery capacity degradation and remaining useful life (RUL) is crucial for battery health management and the safe operation of equipment. However, the diversity of battery types and variations in usage environments pose challenges to data-driven predictive models. Traditional machine learning models often exhibit poor performance in terms of prediction and generalization capabilities. This paper introduces a time-series large model: ANVMD-Llama. The model employs Adaptive Noise Variational Mode Decomposition (ANVMD) to process battery aging data for RUL prediction. Initially, the adaptive noise variational mode decomposition optimizes the tokenization scheme of Lag-Llama, decomposing battery degradation data into multiscale modal components with distinct features to characterize degradation trends and fluctuation properties, aiding the model in understanding fluctuation patterns. Subsequently, ANVMD-Llama is pre-trained on a large dataset of diverse lithium-ion battery degradation data to learn capacity degradation patterns. The model is then fine-tuned using a small amount of data to update the top-level modules, achieving more accurate predictions. Finally, the experimental results demonstrate that the proposed model achieves accurate RUL prediction and exhibits strong transfer capability.

With the surge in new energy installation capacity, the coordination challenges between power generation, grid, and load, along with the risks of generationload imbalance, are becoming increasingly prominent. To address this challenge, both the installation scale and individual capacity of Battery Energy Storage Systems (BESS) are growing rapidly. Compared to traditional architectures, high-voltage transformer-less BESS offers advantages including larger single-unit capacity, fewer parallel devices, simplified control strategies, and faster dynamic response, making it particularly suitable for constructing hundred-megawatt-scale large storage systems. However, in highvoltage transformer-less systems, issues such as heterogeneity of massive battery clusters, temperature control, operational diversity, and control time-effectiveness pose higher requirements for cross-industry collaboration and researchers' information integration capabilities. In recent years, the rapid development of artificial intelligence, especially Large Language Models (LLMs), has brought new opportunities to solve these problems. LLMs have made significant progress in natural language processing and demonstrated strong potential for industry applications. In light of this, this paper proposes the development of a “Grid-level Large-capacity High-voltage Transformer-less Battery Energy Storage Large Model” for energy storage system scenarios. This model uses LLMs as its core engine, integrating key capabilities such as battery cluster topology modeling, operational state estimation, health monitoring, and control strategy optimization. It adopts a hybrid architecture design, combining the advantages of local vector retrieval and cloud-based large language models, providing examples for the application of large models in gridlevel large-capacity high-voltage transformer-less battery energy storage systems.

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 inconsistency of battery voltages in all-electric ships is a significant issue for electric vehicle battery systems, leading to numerous safety concerns during vessel operation. Therefore, timely fault diagnosis and accurate fault prediction are crucial for the safe operation of ships. This study examines the fault alarm system of marine battery management systems in conjunction with the unique operating conditions of ships, focusing on the system’s latency. To facilitate prompt fault detection, a fault diagnosis method based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed, utilizing the voltage data of battery clusters. Results indicate that the DBSCAN clustering algorithm demonstrates superior effectiveness and accuracy in identifying irregular battery clusters. Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. Experimental findings suggest that this model can effectively predict consistency faults and over-/under-voltage conditions based on battery cluster voltage values and corresponding fault thresholds.

Battery energy storage systems can mitigate power fluctuations and enhance system reliability; however, cell-to-cell inconsistencies and aging in large-capacity battery packs can lead to imbalance. To address the limitations of passive balancing, which suffers from high energy loss and low efficiency, this work proposes a high-current active balancing system based on a single-input multiple-output (SIMO) topology. The system enables energy transfer through a full-bridge converter and transformer, supporting series discharge and selective charging of lithium iron phosphate (LFP) cells. To optimize system performance, a small-signal model was established, and corresponding control strategies were designed: the primary-side inverter employs quasi-open-loop control, while the secondary-side charging modules use a voltage–current dual-loop control. The effectiveness of the model and control strategies was validated via QSPICE simulations. Furthermore, a hybrid active–passive balancing strategy based on a voltage-difference threshold was proposed, allowing for real-time dynamic adjustment of the operating mode according to individual cell voltages. Experimental results on a large-capacity LFP battery demonstrate that the system achieves fast balancing with high accuracy, maintaining cell voltage differences within 30 mV. This provides a practical and effective solution for maintaining cell consistency in electric vehicles and grid-scale energy storage systems.

Addressing the challenges of cell inconsistency in battery systems, a key factor affecting performance, dynamic reconfigurable batteries have emerged as a promising solution in recent research, ensuring better cell balancing and optimal energy use. However, existing designs often face tradeoffs between control complexity, switching flexibility, and operational reliability, limiting their practicality for large-scale applications. To overcome these limitations, this study proposes a novel balancing strategy based on a modified modular architecture with flexible module-level switching. This design relies on a dual-layer control framework that simultaneously manages inter- and intra-module state of charge (SOC) equalization with reduced coordination overhead. A genetic algorithm-based approach is used for optimal switching control, enhanced by a feedforward neural network to predict SOC deviation, enabling efficient and reliable real-time balancing. Experimental validation on a lab-scale prototype and hardware-in-the-loop simulations demonstrates the system's ability to improve battery capacity utilization and extend operating time by approximately 17% and 18%, respectively, compared with conventional system. Further testing on a large-scale pack with 320 cells demonstrates up to 40 min of additional autonomy over a fixed architecture, underlining the practical potential of the proposed method for real-world applications.

in an effort to solve the large fluctuation of renewable energy power generation output, which brings many challenges to power system operation, Battery Energy Storage Systems (BESS) are more and more widespread in power systems. This paper proposes an energy management strategy for shared energy storage power plants. First, the shared energy storage power plants are divided into different PCS unit groups, which trade according to different electricity prices. Secondly, the charging and discharging priorities of PCS unit groups are ranked according to the electricity price to maximize the benefits. At the same time, considering the consistency of system SOC (state of charge), the system has higher stability, and more average battery SOC is conducive to better scheduling of BESS. The trade model of a 100MW shared energy storage power station is built in this paper to verify the effectiveness of this method.

With a particular focus on capacity degradation modeling, this paper offers a ground-breaking examination of the use of machine learning approaches for the precise prediction of lithium-ion battery life cycles. Because lithium-ion batteries are essential to many technological applications, it's critical to comprehend and anticipate their life cycles in order to maximize performance and guarantee sustainable energy solutions. The study starts with an extensive examination of the literature, assessing current approaches critically and setting the stage for the introduction of models based on machine learning. The process entails the methodical collecting of data across a range of operational settings, environmental variables, and charging-discharging cycles. Thorough preprocessing guarantees the dataset's consistency and quality for further machine learning model training. Predictive models are created using a variety of machine learning algorithms, including regression models, support vector machines, and deep neural networks. In order to improve prediction accuracy, the paper focuses on the reasoning behind model selection, parameter tuning, and the incorporation of ensemble approaches. In order to uncover important elements influencing the life cycles of lithium-ion batteries and provide important insights into degradation mechanisms, feature selection approaches are used. Using cross-validation techniques and real-world lithium-ion battery datasets, the built machine learning models go through rigorous evaluation and validation processes to determine their robustness, capacity for generalization, and performance metrics. Comparing machine learning-based predictions with conventional models, the results are presented and discussed, offering insights into the interpretability of the models and the identification of important affecting elements. In order to promote proactive maintenance and optimize battery usage, predictive models are integrated into real-time monitoring systems. The consequences for battery management systems are examined. The paper continues by discussing the challenges that come with using machine learning to estimate the life cycle of batteries and outlining possible directions for further research and development, such as scalability, interpretability, and the incorporation of emerging technologies. This research contributes to the ongoing efforts to increase the reliability and sustainability of lithium-ion battery technologies by highlighting the potential impact of machine learning on energy storage system optimization.

Electric vehicles (EVs) have created a revolution in sustainable transportation. The number of EV users has increased significantly within a short period globally. EVs running largely on the battery source require large-capacity battery packs to handle the range anxiety. The primary lifetime of such batteries in EV applications is said to end when their capacity drops to 80% of their initial capacity. This is termed as the end of-life of these batteries. These batteries can still be utilized for secondary applications based on their remaining capacity. Batteries undergo many degradations throughout their lifecycle which affects their capacity. This paper carries out a detailed study on the major degradation factors like solid electrolyte interphase and lithium plating which results in loss of lithium inventory. These affect the capacity of the battery in the long run. Remaining useful capacity must be accurately estimated to identify if the cells are useful for the next phase or must be recycled. Many estimation techniques are available with attention rising towards data derivational methods due to their accuracy and their sensitivity towards battery degradation which thereby makes it easy to track them. Incremental capacity analysis is one such method which is discussed in detail in this paper. The method starts from the initial stage of data extraction and extends to the training set of the models. This method is greatly beneficial as it can reveal the deviations in battery behavior with the help of the valley peak locations and alterations in the slope. The quantitative insights make it an advantageous technique in the field of battery health monitoring and diagnostics. These are discussed in detail and validated by experimental analysis and results. This paper also discusses the market prospects, developments, various ageing mechanisms in batteries, applications, comparison with other estimation techniques and challenges related to secondary life applications. The complete analysis of the estimation method along with the detailed steps also aims to serve as a foundation for the upcoming developments and research in this field.

In recent years, battery degradation has become a critical concern in various industries, including electric vehicles, renewable energy systems, and portable electronics. To address this issue, data-driven techniques have emerged as a promising approach for lithium-ion battery (LIB) degradation analysis and estimation. This paper focuses on the application of incremental capacity curves (ICCs) in battery degradation analysis using data-driven techniques. The incremental capacity curve is a powerful tool that provides valuable insights into the capacity degradation of a battery. By analyzing the changes in the ICC over time, it is possible to identify and quantify battery degradation phenomena such as capacity fade, impedance growth, and aging effects. However, manually analyzing ICCs can be time-consuming and subjective, leading to potential errors and inconsistencies. To overcome these challenges, hyperparameter-tuned Long Short-Term Memory (LSTM) techniques are employed to automate the analysis of ICCs and extract meaningful degradation information. These techniques leverage statistical models to process large volumes of ICC data and identify degradation patterns. By training these models on historical data, they can accurately predict battery degradation and estimate the remaining useful life (RUL) of a battery. Further, to enhance the performance of estimation of RUL of the battery. A hyperparameter-tuned LSTM technique has been proposed. The proposed technique has been compared with well-known techniques (i.e. Fully Connected Neural Network (FNN), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)). The results depict that the proposed robust LSTM technique outperforms well in terms of computational cost and speed. To demonstrate the efficiency of the proposed technique, error analysis has been carried out. The simulation and experimental results depict that the proposed hyperparameter-tuned LSTM model results in very low error indices such as RMSE, MEA, and MAPE as 0.0246, 0.0159 and 1.03 compared with models such as FNN, ANN and CNN. The proposed hyperparameter-tuned LSTM technique depicts a lesser error. By leveraging machine learning and statistical models. The results of this study contribute to the advancement of battery management systems and the optimization of battery usage in various applications.

ABSTRACT Electrochemical models of lithium-ion battery cells, such as the variants of the pseudo two-dimensional model proposed by Doyle, Fuller and Newman, find applications in cell design, diagnosis, and advanced model-based control, as these models allow to reproduce, over a wide range of experimental conditions, the current–voltage characteristics and the internal state of the cell. An implementation of this model and the most commonly employed analysis techniques, including current pulse trains, constant-current constant-voltage cycles, electrochemical impedance spectroscopy (EIS), and dynamic EIS, are presented. These models are organized into a novel Modelica library named LiIonCellP2D, whose structure is discussed. A published model, developed by other authors, of a commercial lithium-ion cell manufactured by Kokam is used to illustrate the features of LiIonCellP2D, and the simulation results are compared with those obtained using the DandeLiion online simulator. LiIonCellP2D was developed and tested using Dymola version 2024x.

Ensuring manufacturing consistency in lithium-ion batteries is critical for reliable performance, safety, and longevity. This study examines the variability in initial pouch cell characteristics, including voltage, current, charge capacity, and discharge capacity, across 192 samples from 24 batches. Statistical analysis reveals that voltage remains relatively stable (mean = 3.951V, CV ≈ 6.45%), while charge and discharge capacities exhibit moderate variability (mean = 2.286Ah, CV ≈ 47.99% and mean = 2.350Ah, CV ≈ 52.53%, respectively). Current demonstrates the highest variability, with a mean of 0.280A and a CV of 195.25%, suggesting significant fluctuations possibly due to non-constant current and also likely influenced by process inconsistencies, operational conditions, or measurement sensitivity. Box plot and control chart analyses link many outliers to specific production factors, such as raw material lot changes and equipment maintenance cycles, pinpointing electrode preparation and formation as critical stages for mitigating variability. By integrating statistical insights with practical manufacturing considerations, this work provides a framework for proactive quality control, ultimately supporting scalable and high-quality lithium-ion battery production. While this study focuses on pouch cells, the underlying principles of variability analysis and targeted process improvements remain broadly relevant to other battery formats.

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.

With the rapid development of the new energy sector, production equipment for battery cells faces increasing challenges in maintaining efficiency and quality. Among these, the laser die cutting and winding machine plays a pivotal role in transforming electrode sheets into finished cells. Its performance directly affects the dimensional precision and internal structural consistency of the cells, which are critical to product quality and production-line efficiency. To tackle the challenges in monitoring and maintaining this critical equipment, we propose a time-series data-driven method based on the Transformer architecture, named multiscale dynamic dilated attention, which effectively predicts sensor offset trajectories and provides early shutdown fault warnings when correction sensors approach their operational limits. Furthermore, this model incorporates adjustable segment sizes and counts, and assigns distinct dilation rates to individual attention heads, this design enables a dynamic tradeoff among receptive field, modeling capacity, and computational cost, allowing fine-grained control over long-range dependence modeling while reducing the canonical self-attention complexity from $O(\mathit {L}^{2})$ to approximately $O(\mathit {L})$. Extensive experiments and real-world applications demonstrate that the proposed method achieves state-of-the-art performance in both prediction accuracy and practical deployment, while exhibiting excellent adaptability across diverse operating conditions.

Silicon‐dominant (SD) anodes are widely regarded as a key enabler for next‐generation high‐energy lithium‐ion batteries, yet most studies remain confined to laboratory‐scale electrodes, often yielding inconsistent performance when translated to larger manufacturing environments. Here, we demonstrate a processing strategy that ensures electrochemical and structural consistency of Si‐dominant electrodes across Laboratory‐scale (LS) and Pilot‐line (PL) production, using only commercially available materials and industrially relevant areal capacities of 3.4–4.0 mAh cm − 2 . Despite the use of different mixing technologies, both LS and PL slurries exhibited comparable rheological behavior, leading to similar electrode microstructures and mechanical integrity. Electrochemical evaluation confirmed stable operations under a controlled Si capacity limitation (1000 mAh g − 1 ), enabling a reproducible comparison of degradation trends. The scalability of the approach was further validated in a 1.5 Ah stacked pouch cell, which delivered 253 cycles at C/2 with >99.90% average efficiency. These results demonstrate that careful control of slurry rheology, formulation, and processing parameters enables robust transfer of SD Electrode performance from Laboratory investigations to Pilot‐scale production, providing a practical framework for bridging early‐stage research and industrial manufacturing.

Half‐cells have been employed to investigate the intrinsic electrochemical behavior of the cathode material, as the chemical potential of the alkali metal reference electrode remains relatively constant during discharge. However, in full cells, the discharge mechanism is anode‐dependent. Herein, a rechargeable nonaqueous sodium ion battery (SIB) is fabricated using tungsten trioxide (WO3) nanopowder on a graphite substrate as the anode and a nickel‐hexacyanoferrate Prussian blue (PB) cathode to understand the dominant discharge mechanism. The battery cells are evaluated for reversibility and durability and exhibit reversible charge–discharge plateaus, confirming sodium‐ion intercalation/deintercalation in both electrodes. The sodium‐ion diffusion coefficient of 5.3 × 10−13 cm2.s−1 calculated using electrochemical impedance spectroscopy (EIS) is consistent with a planar finite space diffusion mechanism. Cyclic voltammetry (CV) shows a broad reversible redox peak on the WO3 anode, owing to its multiple valence states, also observed in potential versus differential capacitance (dQ/dV) and simulated density of states (DOS). The full cell demonstrates an open‐circuit voltage (OCV) of 2.2 V (charged), a discharge capacity of 79 mAh.g−1 at 0.1C rate, and retains 69% of its capacity after 500 cycles, indicating promising durability and reversibility for sodium‐ion storage. The charge carrier concentration (ccc), DOS, electrical and thermal conductivities, and chemical potential simulations for the charged and discharged phases, in both electrodes, reveal that the anode determines the shape of the discharge curve and the cathode the capacity of the cell. This study paves the way to predicting the behavior of a full cell, including cycling curve shape, process, dependencies, and thermal runaway.

Flow batteries are gaining traction as a candidate long-duration energy storage technology. As highlighted at the 2024 International Flow Battery Forum, a lack of standardization of cells and testing protocols hinders fair comparison of flow battery systems. Moreover, research1 on adjacent, yet more mature electrochemical technologies, including lithium-ion batteries,2-4 solid-state batteries,5 solid oxide fuel cells,6 water electrolysers,7,8 and supercapacitors,9 has highlighted both a lack of transparency in methodology and a lack of measurement and/or reporting of uncertainties. Interstudy comparisons of electrochemical flow cells which include measurements of variance can yield more robust (in)conclusions than those relying on single-point measurements. As such, studies which focus on the measurement of variance in flow cell performance can guide the development of standards, protocols, and best practices. Measures of variance are defined differently across literature, often to capture field-specific needs or varying scope in sources of error (e.g., intra- vs. inter-laboratory variance). Here, we define “replicability”,10 the focus of this work, to capture variance in inter-team measurements conducted on the same system, but by different participants (i.e., different people informed by the same instructions, but engaging minimally during the data collection). To explore interlaboratory replicability in flow cell performance, 7 institutions (8 research groups) partnered to collect and analyze polarization, impedance, and charge-discharge cycling data collected using the same flow cell (Figure 1a), same symmetric configuration, and the same electrolyte composition (a near-neutral pH ferri-/ferro-cyanide based electrolyte). To inform experimental design, participants were surveyed about equipment available to them. Subsequently, an experimental request was developed and disseminated to the participants. This request was intended to unambiguously specify the cell setup (Figure 1b) while underspecifying certain aspects of the experiments to allow participants to employ some of their typical experimental and analytical practices. After data was collected, we surveyed participants to capture these variable experimental practices and to probe decision-making during their analysis. Finally, anonymized data were processed by two other authors, to explore variance in the data. In this talk, we will discuss the development of this round-robin-style study, the collected flow cell data, and our analyses, which focused on quantifying and explaining the variance in the data. We found that our experimental request, written with detail similar to that of typical methodology sections in journal articles, was interpreted differently by participants, highlighting challenges in protocol communication. We observed variances across the gathered polarization, impedance, and cycling data which appear weakly or not correlated to each other, or to differences in the unspecified portions of the setups (e.g., inclusion or lack of electrolyte sparging, reservoir material or geometry). Finally, we will discuss actions to mitigate these discrepancies and reduce replicability error in single-cell flow battery testing. The magnitude of variation in our study suggests a general value in understanding the sources of error that contribute to repeatability, thereby strengthening comparative studies. However, identifying the specific pathway to improvements (e.g., by refining protocols, experimental practice, or consistency in the material sets used when gathering data) and defining tolerable levels of error require further efforts and context. References [1] G. Smith and E. J. F. Dickinson, Nat . Commun, vol. 13, no. 1, pp. 1–6, 2022. [2] N. M. Vargas-Barbosa, Nat . Nanotechnol, vol. 19, no. 4, pp. 419–420, 2024. [3] M. D. L. Garayt et al., J Electrochem Soc, vol. 170, no. 8, p. 080516, 2023. [4] J. Li et al., J Power Sources, vol. 452, p. 227824, 2020. [5] S. Puls et al., Nat. Energy, vol. 9, pp. 1310-1320, 2024. [6] Z. Luo et al., Energy Environ Sci, vol. 17., pp. 6873-6896, 2024. [7] I. Rios Amador et al., STAR Protoc., vol. 4, no. 4, p. 102606, 2023. [8] G. Bender et al., Int J Hydrogen Energy, vol. 44, no. 18, pp. 9174–9187, 2019. [9] J. W. Gittins et al., J Power Sources, vol. 585, p. 233637, 2023. [10] S. L. McArthur et al., Biointerphases, vol. 14, no. 2, 2019. Figure 1

No abstract available

Passive films are essential for the longevity of metals and alloys in corrosive environments. A great deal of research has been devoted to understanding and characterizing passive films, including their chemical composition, thickness, uniformity, thickness, porosity, and conductivity. Many characterization techniques are conducted under vacuum, which do not portray the true in‐service environments passive films will endure. Scanning electrochemical probe microscopy (SEPM) techniques have emerged as necessary tools to complement research on characterizing passive films to enable the in situ extraction of passive film parameters and monitoring of local breakdown events of compromised films. Herein, we review the current research efforts using scanning electrochemical microscopy, scanning electrochemical cell microscopy (or droplet cell measurements), and local electrochemical impedance spectroscopy techniques to advance the knowledge of local properties of passivated metals. The future use of SEPM for quantitative extraction of local film characteristics within in‐service environments (i.e., with varying pH, solution composition, and applied potential) is promising, which can be correlated to nanostructural and microstructural features of the passive film and underlying metal using complementary microscopy and spectroscopy methods. The outlook on this topic is highlighted, including exciting avenues and challenges of these methods in characterizing advanced alloy systems and protective surface films.

No abstract available

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).

No abstract available

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

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.

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

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.

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The Electrochemical Impedance Spectroscopy (EIS) technique is widely used to characterise electrochemical reaction mechanisms. This technique identifies the main phenomena occurring in an electrochemical device. When dealing with Solid Oxide Electrolysers (SOECs) and Fuel Cells (SOFCs), button cells are generally used for improving their structure, choosing the right electrode materials, and testing manufacturing processes. EIS techniques are then employed to evaluate their performance under different operating conditions. However, larger industrial-sized cells are used for being operated in real test environments, meaning that a larger number of experimental tests on ever-increasing cell area are required to evaluate and assess their performance properly. In such a context, this work aims to cover this research gap by providing EIS spectra as preliminary results of an experimental campaign carried out on a 5x5 cm2 rSOC operating at different conditions in terms of temperature and gas composition. In SOEC mode, an increase in the water content at the fuel electrode leads to unstable conditions and an increase in the polarisation and ohmic resistances. In SOFC mode, a decrease in the hydrogen content at the fuel electrode leads to a maximum increase of the polarisation resistance of 34.17%, while a decrease in the oxygen content at the air electrode leads the system to highly unstable conditions. In both cases, the cell temperature variation leads to a maximum reduction in the ohmic resistance of 12 and 3.8% in SOEC and SOFC modes, respectively.

Local electrochemical impedance spectroscopy (LEIS) has emerged as a technique to characterize local electrochemical processes on heterogeneous surfaces. However, current LEIS heavily relies on lock-in amplifiers that have a poor gain effect for weak currents, limiting the achievements of high-spatial imaging. Herein, an integrated scanning electrochemical cell microscopy is developed by directly collecting the alternating current (AC) signal through a preamplifier. The recorded local current (sub nA-level) is compared with the initial excitation signal to get the parameters for Nyquist plotting. By integrating this method into scanning electrochemical cell microscopy (SECCM), an image of LEIS at the Indium Tin Oxide/gold (ITO/Au) electrode is obtained with a spatial resolution of 180 nm. The established SECCM platform is integrated such that it could be positioned into the limited space (e.g. glove box) for real characterization of electrodes.

Electrochemical impedance spectroscopy (EIS) has become a standard measurement technique for detecting degradation in single cells and stacks of solid oxide cells (SOCs). Depending on the experimental setup and test equipment, instabilities and unexpected results can be observed in EIS measurements. For example, in the low-frequency range, instabilities can be induced by feed gas flow fluctuations. Another phenomenon are parasitic, inductive impedances that degrade the high-frequency range. To compensate for such influences in large EIS data sets, we propose a specially developed EIS data curation pipeline. Based on the results of its application, we demonstrate the impact on the quantitative and qualitative attribution of electrochemical processes from EIS using equivalent circuit modeling and distribution of relaxation times. Furthermore, the substantial differences in the temporal evolution of the latter during long-term experiments are highlighted for EIS measurements obtained at the SOC stack and single cell level. In addition, the significant misestimation of aging rates, especially with respect to the fuel electrode and the high-frequency series resistance, is shown when comparing EIS measurements, few of which exhibit a parasitic inductive impedance.

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The Distribution of Relaxation Times (DRT) technique has been widely used in fuel cell diagnostics due to its higher resolution of electrode processes compared to the Nyquist plot derived from impedance spectroscopy. However, DRT is often treated as a plug‐and‐play tool in many studies, leading to inappropriate or even erroneous interpretations due to insufficient investigation of optimization algorithms. This study focuses on the inversion‐based DRT and examines a common linear optimization algorithm based on Gaussian functions, spotlighting the trade‐off between accuracy and resolution as influenced by three parameters: the number of basis functions, their width, and the regularization factor. It is found that the first one affects the accuracy. The integrated peak area provides a more reliable measure than the peak height, evidenced by a standard deviation of 0.11 versus 0.16 for the latter. Meanwhile, the last two parameters significantly influence the resolution; improper selection of them leads to the emergence of spurious peaks. Using appropriately chosen parameters, the state of a 120‐cell stack is detected. Results reveal the clear evolution of oxygen transport and oxygen reduction reaction at different positions under varying stoichiometric ratios. This study underscores the importance of computational efforts in selecting key parameters to enable reliable DRT‐based diagnostics.

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.

To swiftly accomplish online fault diagnosis of water management for proton exchange membrane fuel cell (PEMFC), an online diagnosis method of water management faults based on hybrid-frequency electrochemical impedance spectroscopy is proposed. First, guided by the principle of time-frequency signal processing, the online diagnosis of water management faults is conducted via the full-frequency band electrochemical impedance spectroscopy and the characteristic information of the abscissa coordinates at the intersection of high and low frequencies in electrochemical impedance spectroscopy. Subsequently, an online diagnosis method of water management faults based on hybrid-frequency electrochemical impedance spectroscopy is proposed to enhance the diagnostic speed. To validate the effectiveness and feasibility of the proposed method, a test platform is established, and numerous tests and analyses are conducted on PEMFC under diverse operating conditions. Experimental results demonstrate that the proposed method can accurately and swiftly identify various faults of water management in PEMFC, achieving a diagnostic accuracy of 95.56%, with a diagnostic duration of approximately 4 s.

High-temperature proton exchange membrane fuel cells (HT-PEMFC) directly convert hydrogen and oxygen to produce electric power at a temperature significantly higher than conventional low-temperature fuel cells. This achievement is due to the use of a phosphoric acid-doped polybenzimidazole membrane that can safely operate up to 200 °C. PBI-based HT-PEMFCs suffer severe performance limitations, despite the expectation that a higher operating temperature should positively impact both fuel cell efficiency and power density, e.g., improved ORR electrocatalyst activity or absence of liquid water flooding. These limitations must be overcome to comply with the requirements in mobility and stationary applications. In this work a systematic analysis of an HT-PEMFC is performed by means of electrochemical impedance spectroscopy (EIS), aiming to individuate the contributions of components, isolate physical phenomena, and understand the role of the operating conditions. The EIS analysis indicates that increases in both the charge transfer and mass transport impedances in the spectrum are negatively impacted by air humidification and consistently introduce a loss in performance. These findings suggest that water vapor reduces phosphoric acid density, which in turn leads to liquid flooding of the catalyst layers and increases the poisoning of the electrocatalyst by phosphoric acid anions, thus hindering performance.

In this work, we apply data from Electrochemical Impedance Spectroscopy (EIS) measurements, conducted on a Solid Oxide Cell (SOC) stack, to an automatic data curation and evaluation pipeline. Latter is developed to enable the use of historic data from EIS measurements conducted on SOC stack experiments for Machine Learning (ML) models. We show that the proposed procedure can curate parasitic, inductive impedances, obtained as a common effect of measurements on stack level. In addition, drifts induced by temperature and by steam supply gradients during the EIS measurement can be compensated. The results are experimentally validated on a two-layer SOC stack. For extraction of feature values for subsequent ML models distribution of relaxation times (DRT) deconvolution and equivalent circuit modeling (ECM) are used. To determine a suitable regularization parameter for DRT deconvolution, a variance test is implemented.

This paper aims to design a lumped-capacity model of a reversible solid oxide cell stack for hydrogen electrolysis. The lumped-capacity model needs to have an adequate representation of the electrical dynamics over a wide operating range and a model structure suitable for the design of a physical emulator. The grey-box model is based on data obtained by electrochemical impedance spectroscopy conducted on a commercial solid oxide cell stack for four different gas compositions at six aging stages. In addition, a comparison of the experimental and simulated voltage response of the reversible solid oxide cell stack in cyclic reversible operation mode was conducted at different aging levels of the stack.

A miniaturized and low-cost electrochemical 3D-printed system for rapid and accurate quantification of ethanol content in ethanol fuel using electrochemical impedance spectroscopy (EIS) was developed. The monolithic design of the system incorporates insulating thermoplastic electrode separators, with only the cover being mobile, allowing for easy assembly and handling. The portable device, measuring approximately 26 × 24 mm, has a maximum capacity of 1 mL, making it suitable for lab-on-a-chip and portable analysis. By utilizing the dielectric constant of ethanol and ethanol fuel mixtures with water, the miniaturized EIS cell quantifies ethanol content effectively. To validate its performance, we compared measurements from four gas stations with a digital densimeter, and the values obtained from the proposed system matched perfectly. Our miniaturized and low-cost electrochemical 3D-printed device can be printed and assembled in two hours, offering a cost-effective solution for fast and precise ethanol quantification. Its versatility, affordability, and compatibility with lab-on-a-chip platforms make it easily applicable, including for fuel quality control and on-site analysis in remote locations.

An analytic equation for electrochemical impedance of a single-cell measured with a microelectrode is presented. A previously reported equation had a practical problem that it is valid only when the microelectrode resides at the center of the cell under test. In this work, we propose a new analytic equation incorporating dependence on the cell position and confirmed its effectiveness by numerical simulation. Comparisons show that our proposed equation gives excellent agreement with the simulated impedance values. Discrepancies between the results from our equation and numerical simulation are suppressed within 13%, which is a dramatic reduction from the previously reported discrepancy of 58%. The proposed analytic equation is expected to enable more accurate analysis in actual cell experiments.

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Recently, various algorithms have been developed for generating appealing music. However, the style control in the generation process has been somewhat overlooked. Music style refers to the representative and unique appearance presented by a musical work, and it is one of the most salient qualities of music. In this paper, we propose an innovative music generation algorithm capable of creating a complete musical composition from scratch based on a specified target style. A style-conditioned linear Transformer and a style-conditioned patch discriminator are introduced in the model. The style-conditioned linear Transformer models musical instrument digital interface (MIDI) event sequences and emphasizes the role of style information. Simultaneously, the style-conditioned patch discriminator applies an adversarial learning mechanism with two innovative loss functions to enhance the modeling of music sequences. Moreover, we establish a discriminative metric for the first time, enabling the evaluation of the generated music’s consistency concerning music styles. Both objective and subjective evaluations of our experimental results indicate that our method’s performance with regard to music production is better than the performances encountered in the case of music production with the use of state-of-the-art methods in available public datasets. 近年来,研究人员开发了各种算法来生成动听的音乐。然而,在生成过程中有时忽略了风格控制。音乐风格是指音乐作品呈现的具有代表性的特征,是音乐最突出的特质之一。本文提出一种创新的音乐生成算法,该算法能够根据指定的风格从零开始创作完整的音乐作品。算法引入了风格约束的线性生成器和风格鉴别器。风格约束生成器模拟MIDI事件序列,强调风格信息的作用。风格鉴别器应用对抗学习机制并引入两种创新的损失函数,以加强对音乐序列的建模。此外,本文首次建立了一个判别指标,以评估生成音乐与训练数据在音乐风格上的一致性。在现有公共数据集上,实验结果的客观和主观评价都表明我们的算法在音乐制作方面优于现有先进方法。

One of the not so well understood aspects of vanadium redox flow battery cells is the capacity fade that happens during continuous charge–discharge cycles. While several modeling and experimental studies have been reported, reliable data are not available in the open literature. In the present work, long‐duration charge–discharge cycling studies are reported in cells of industrial size having an electrode area of about 400 and 1800 cm 2 . The continuous current and cell voltage measurements have been supplemented by periodic measurement of concentration of the electro‐active vanadium ions in the positive and the negative electrolytes. The weight of the electrolyte tanks and the open circuit voltage of the cell have also been monitored. The mutual self‐consistency of these data has been verified by post‐test analyses. Data from over thirteen different cases spanning a range of current densities and electrolyte circulation rates show that the capacity fade is about (0.15 ± 0.05)% per cycle. In all cases, an asymptotic capacity fade pattern is established in which the concentration of V(V) increased and V(IV) decreased steadily on the positive side, and that of V(III) decreased steadily but slightly on the negative side.

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Introduction One of the critical technological isuues to constract carbon neutral society is the suppression of the impact of the volatility in power generation from inexpensive variable renewable energy power sources, such as solar cells and wind powers. It is necessary to maintain a balance between power generation, transmission, and consumption, and to maintain three types of stability in the power system: frequency stability, synchronization stability, and voltage stability. For this reason, energy storage technology is one of the most important issues for making up carbon neutral energy system. Among energy storage technologies, lithium-ion secondary batteries are superior in terms of both output density and energy density, but they present a cost challenge for large-capacity energy storage. Pumped storage power generation is the mainstay of long-term large-capacity energy storage in the current power system, but significant pumped storage expansion is subject to geopolitical constraints. Therefore, a compact, large-capacity energy storage system that can be installed in the power grid is required to expand the introduction of variable renewable energy power sources. In this research, we first describe the operating mechanism and advantages of the "Carbon/Air Secondary Battery (CASB) system" [1,2], a compact power storage system that integrates the functions of a solid oxide fuel cell that generates electricity by producing CO2 through the oxidation reaction of carbon and a solid oxide electrolytic cell that produces carbon through the electrolysis of CO2. The operating mechanism and features of the CASB system are introduced. In addition, we report the calculation results of feasible system efficiency based on thermodynamic considerations compared to power storage system using H2/H2O-solid oxide fuel cells/electrolysis cells. In addition, the advantages/disadvantages of the two types of CASBs, integrated type of CASB between carbon deposition /electrochemical reaction, and the separated type of CASBs, as well as their representative charge/discharge characteristics will be presented. Theoreticl advantage of CASB as a fixed compact power storage with large capacity The theoretical charge/discharge efficiency of CASB, which is the ΔG/ΔH of reaction “C+O2→CO2” is 100%. This means that there is no heat input and output ideally in both carbon generation and CO2 electrolysis (in charge/discharge). Therefore, temperature control is simple and a heat exchanger can be minimized, which may allow the system to be compact. The feasible charge/discharge system efficiency of CASB including possible heat exchange were estimated as 92% based on thermodynamics, which is much higher than that of power storage system using H2/H2O-solid oxide fuel cells/electrolysis cells (63%). In addition, since CO2 can be liquefied at about 6.4 MPa at 25°C, its volumetric energy density to 1.62 × 103 Wh L-1, about four times higher than the volumetric energy density of hydrogen compressed at 20 MPa at 25°C, 3.79 × 102 Wh L-1. A key technological point of CASB at its discharging , which is combination between electrochemical reaction (CO2→ CO+O2-) and thermochemical reaction (2CO→ C+CO2) by achiving at closed system The discharge reaction of the CASB is the same as that of the Rechargeable Direct Carbon Fuel Cell [3,4,5]. The reason for the first successful demonstration of the CASB is that the combining electrochemical reaction with low overpotential (CO2 →CO+O2-) with the thermochemical reaction, the Boudouard equilibrium reaction (2CO → C+CO2) were achieved at the charging by making the system a closed system. Two types of CASBs, (1) Integrated type of CASB between carbon deposition /electrochemical reaction, and (2) the separated type of CASBs Since generally, the carbon deposition on the electrode cause its degradation [6], two type of CASBs can be designed and demonstrated (as shown in Fig.1). Both types of CASBs could be demonstrated. References [1] K. Kameda, S. Manzhos, and M. Ihara, J. Power Sources, 516, (2021) 230681. [2] K. Kameda. T. Wakamiya, C. Kexin, S. Manzhos, and M. Ihara, SCEJ 89th Annual Meeting, (2024). [3] M. Ihara, K. Matsuda, H. Sato and C. Yokoyama, Solid state fuel storage and utilization through reversible carbon deposition on an SOFC anode, Solid State Ionics, 175(1-4), (2004), 51-54 [4] M. Ihara and S. Hasegawa, Quickly Rechargeable Direct Carbon Solid Oxide Fuel Cell with Propane for Recharging, Journal of The Electrochemical Society, 153, (2006), A1544-A1546 [5] Gür, T. M. Critical Review of Carbon Conversion in“ Carbon Fuel Cells”. Chem. Rev. 113, 6179-6206 (2013). [6] Y. Jin, H. Yasutake, K. Yamahara and M. Ihara, Suppressed carbon deposition behavior in Nickel / Yittria-stabilized zirconia anode with SrZr0.95Y0.05O3-α in dry methane fuel, Journal of The Electrochemical Society, 157(1), (2010), B130-B134 Figure 1

Among battery cells of various form factors, cylindrical-type battery cells are advantageous in mass production and relatively economical compared to other form factors. As the demand for higher storage capacity gradually increases, automobile manufacturers focus more on developing high-capacity and high-density batteries such as 46XX (where 46 means diameter and XX height ranging from 80 to 115). The energy capacity of the 46XX battery cells is over five times greater than that of the 1865 and 2170 cells. However, since no manufacturers were previously producing the 46XX, new issues were emerged in the process of manufacturing these new large-capacity cells. This research studies the manufacturing of the 46XX large-capacity cylindrical battery cell can through a multi-step forming process, utilizing finite element analysis. It also explores the industrial challenges encountered during the production of cylindrical battery cells and highlights the obstacles that need to be addressed for the widespread industrial application of the 46XX large-capacity cylindrical battery cell can.

With the rapid popularization of new energy vehicles worldwide, the demand for power lithium-ion batteries has surged. Consequently, the industry is now facing the challenge of a large number of retired lithium batteries. As these batteries reach the end of their life cycle, efficiently utilizing their residual value has become a key issue that needs to be resolved. This paper reviews the key issues in the cascade utilization process of retired lithium batteries at the present stage. It focuses on the development status and existing challenges of residual capacity estimation methods and consistency sorting technology. Based on the review, this paper also looks forward to the future research trend of the cascade utilization technology of retired batteries, and the efficient cascade utilization of retired lithium batteries will not only alleviate the pressure on resources but also play a positive role in realizing the goal of carbon neutrality and promoting the development of green economy.

Vanadium redox flow battery offers significant potential for large-scale energy storage but face capacity decay challenges. In order to enhance battery performance and extend its service life in a simple yet effective manner, this study constructs a 2D model that takes into account the factors contributing to capacity decay. Building on this foundation, the effects of different operating conditions on discharge capacity loss are systematically investigated using the control variable method. In addition, through orthogonal testing and range analysis, the key factors affecting capacity retention rate were identified in descending order of influence: discharge cut-off voltage, charge cut-off voltage, and electrolyte flow rate. The optimal operating parameters were determined as charge cut-off voltage of 1.6 V, discharge cut-off voltage of 1.2 V, and electrolyte flow rate of 150 mL/min. It is indicated that the optimized model achieves 7.1% enhancement in discharge capacity retention rate after 50 cycles, albeit with a slight reduction in initial discharge capacity. The optimization measure effectively prolongs the service life of the battery and offers a novel research approach for the optimal design and operation of vanadium redox flow battery in the future.

Achieving simultaneous high‐rate capability and large reversible capacity remains a core challenge for sodium‐ion battery (SIB) anodes. Herein, the rational design and synthesis of a multicore Fe 2 SSe architecture is reported, enriched with anion vacancies and confined within a multishell carbon matrix (V‐Fe 2 SSe@MC), to address this challenge. This tailored structure effectively accommodates volume fluctuations, maintains robust electrode‐electrolyte interfaces, and significantly accelerates Na + and electron transport. The dense concentration of selenium/sulfur vacancies not only boosts intrinsic electrical conductivity but also generates abundant active sites while lowering the energy barrier for Na + diffusion. Most critically, the coupling of the multicore‐in‐multishell architecture with the induced anion vacancies produces a synergistic effect that alleviates the conventional trade‐off between storage capacity and rate capability. As a result, the V‐Fe 2 SSe@MC anode unlocks exceptionally fast and large‐capacity Na + storage, delivering a high specific capacity of 505 mAh g −1 even at 40C and maintaining 97% capacity over 3000 cycles at 20C. Comprehensive in situ characterizations elucidate the reversible cleavage and regeneration of Fe─S/Se bonds and excellent structural integrity during cycling. This work provides compelling insight into the rational design of high‐performance SIB anodes via the integrated approach combining multicore‐in‐multishell structural design with vacancy engineering.

The shuttling of lithium polysulfides (LiPSs), sluggish reaction kinetics, and uncontrolled lithium deposition/stripping remain the main challenges in lithium‐sulfur batteries (LSBs), which are aggravated under practical working conditions, i.e., high sulfur loading and lean electrolyte in large‐capacity pouch cells. This study introduces a Ti3C2Tx MXene@CuCo2O4 (MCC) composite on a polyethylene (PE) separator to construct an ultrathin MXene@CuCo2O4/PE (MCCP) film. The MCCP functional separator can deliver superior LiPSs adsorption/catalysis capabilities via the MCC composite and regulate the Li+ deposition through a conductive Ti3C2Tx MXene framework, enhancing redox kinetics and cycling lifetime. When paired with sulfur/carbon (S/C) cathode and lithium metal anode, the resultant 10 Ah‐level pouch cell with the ultrathin MCCP separator achieves an energy density of 417 Wh kg−1 based on the whole cell and a stable running of 100 cycles under practical operation conditions (cathode loading = 10.0 mg cm−2, negative/positive areal capacity ratio (N/P ratio) = 2, and electrolyte/sulfur weight ratio (E/S ratio) = 2.6 µL mg−1). Furthermore, through a systematic evaluation of the as‐prepared Li‐S pouch cell, the study unveils the operational and failure mechanisms of LSBs under practical conditions. The achievement of ultrahigh energy density in such a large‐capacity lithium‐sulfur pouch cell will accelerate the commercialization of LSBs.

For large-scale energy storage, flow batteries present many advantages. These benefits include, but are not limited to, decoupling power rating from energy capacity and projected lower cost energy storage and long cycle life. Several reviews and a comprehensive handbook have come available recently describing the various flow battery chemistries and the details of cell and stack construction. However, none of the chemistries proposed are perfect and the commercialization path to widespread adoption remains challenging. For the purposes of this article, we will take a closer look at the concept and development of an all-iron slurry flow battery and the intellectual property (IP) protection and commercialization process that occurred within an academic environment.

Cloud providers, such as AWS and Azure, have started providing 5G services on their cloud infrastructure. In this paper, we present the design and implementation of X-Plane, a system that uses commercial programmable ASICs and DRAM servers on today's cloud infrastructure to implement high-performance 5G User Plane Function (UPF). Building X-Plane is hard because we need to address the following challenges: consistency issues when concurrently accessing UPF state data, slow UE table lookup due to repetitive and numerous Packet Detection Rule (PDR) matching, and the need to handle out-of-order packets from disconnected UEs. X-Plane addresses these challenges by designing three novel technologies: concurrent state data access protocol, fast flow table and paging buffer for handling out-of-order packets. We demonstrate its feasibility and practicality with our implementation on a Tofino-based programmable ASIC. Our evaluation shows that X-Plane can support over ~490Gbps throughput per ASIC pipeline, over 10 million UEs, and finish packet processing within predictable ~4 us on average.

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The increasing use of renewable energies places new challenges on the balance of the electricity system between demand and supply, due to the intermittent nature of renewable energy resources. However, through frequency regulation (FR) services, owners of battery storage systems can become an essential part of the future smart grids. We propose a thorough first study on the use of batteries associated with base stations (BSs) of a cellular network, to participate in ancillary services with respect to FR services, via an auction system. Trade-offs must be made among the number of participating BSs, the degradation of their batteries and the revenues generated by FR participation. We propose a large-scale mathematical programming model to identify the best participation periods from the perspective of a cellular network operator. The objective is to maximize profit while considering the aging of the batteries following their usage to stabilize the electrical grid. Experiments are conducted with data sets from different real data sources. They not only demonstrate the effectiveness of the optimization model in terms of the selection of BSs participating in ancillary services and providing extra revenues to cellular network operators, but also show the feasibility of ancillary services being provided to cellular network operators.

One of the main challenges of Lombok Island, Indonesia, is the significant disparity between peak load and base load, reaching 100 MW during peak hours, which is substantial considering the island’s specific energy dynamics. Battery energy storage systems provide power during peak times, alleviating grid stress and reducing the necessity for grid upgrades. By 2030, one of the proposed capacity development scenarios on the island involves deploying large-scale lithium-ion batteries to better manage the integration of solar generation. This paper focuses on the life cycle assessment and life cycle costing of a lithium iron phosphate large-scale battery energy storage system in Lombok to evaluate the environmental and economic impacts of this battery development scenario. This analysis considers a cradle-to-grave model and defines 10 environmental and 4 economic midpoint indicators to assess the impact of battery energy storage system integration with Lombok’s grid across manufacturing, operation, and recycling processes. From a life cycle assessment perspective, the operation subsystem contributes most significantly to global warming, while battery manufacturing is responsible for acidification, photochemical ozone formation, human toxicity, and impacts on marine and terrestrial ecosystems. Recycling processes notably affect freshwater due to their release of 4.69 × 10−4 kg of lithium. The life cycle costing results indicate that over 85% of total costs are associated with annualized capital costs at a 5% discount rate. The levelized cost of lithium iron phosphate batteries for Lombok is approximately 0.0066, demonstrating that lithium-ion batteries are an economically viable option for Lombok’s 2030 capacity development scenario. A sensitivity analysis of input data and electricity price fluctuations confirms the reliability of our results within a 20% margin of error. Moreover, increasing electricity prices for battery energy storage systems in Lombok can reduce the payback period to 3.5 years.

The rapid adoption of Battery-Operated Vehicles (BOVs) as an alternative to internal combustion engine vehicles (ICE) has gained significant attention in sustainable mobility. Still, BOVs face sustainability issues that hinder large-scale application. This paper examines sustainability issues related to BOVs, focusing on consumption, energy usage, environmental impacts, and socio-economic effects to identify research gaps and areas needing more focus for efficient integration into transportation systems. A scientometric analysis of major academic publications from relevant databases was conducted, including citation analysis, author co- citation, and content analysis to identify the most cited articles, connected authors, and topics of interest. The study shows increasing interest in BOVs, particularly in battery technology, charging infrastructure, and lifecycle emissions. Problems such as insufficient battery capacity, significant environmental impact of battery production, and green energy usage remain. Despite advancements in battery systems and charging infrastructure, the primary issue is the environmental impact of battery production and disposal. Policies and regulations are insufficient for widespread BOV use. Results suggest further cooperation among policymakers and industry to propose long-lasting measures, including enhancing battery recycling technologies, expanding charging infrastructure, and creating a coordinated regulatory environment. Further investigations are needed on life cycle assessment and renewable energy’s role in mitigating BOV environmental impact.

Integration of large‐scale wind farms (WFs) into the grid has to meet the critical constraints set in the national grid code. Wind farm operators (WFOs) are inclined to comply with these constraints and avoid heavy penalty costs for violating such regulations. However, this may result in reduced power sent to the grid. Moreover, the addition of new rules to account for the increased penetration of WFs brings challenges to the profitability of the WFs. A battery energy storage system (BESS), if sized optimally, can be a reliable method to fulfill the grid code requirements without sacrificing profit. This paper provides a techno‐economic model to find the optimal rated capacity and power for a BESS in WFs. This optimization model takes the absolute production and delta production constraints into account. Two approaches are studied for integrating these constraints into the grid code. It is shown that the flexible strategy financially outperforms the strict addition of the new rules. This will be useful, especially to attract investments in wind energy projects despite the abovementioned limitations in the grid code. All the modeling and analysis are done for a potential offshore wind power plant (OWPP) in Turkey. Simulation results show the effectiveness of the optimal BESS in increasing the amount of energy delivered to the grid and improving the profitability of the OWPP.

Ag–Zn batteries have the advantages of being a high‐safety and stable discharge platform but still face challenges such as high cost, electrolyte hydrolysis, and zinc dendrites. In this study, a low‐cost roughness graphite paper (GP) enables more uniform loaded Ag particles to construct Ag/rough graphite paper (Ag/RGP) cathode with high wettability of low‐concentration ZnCl2 electrolyte, achieving both capacity and reversibility enhancement. Both a few additions of citric acid (CA) to the low‐concentration ZnCl2 electrolyte can also inhibit the hydrolysis of Zn2+ and increase the nucleation overpotential due to suitable acidity and complexation ability, resulting in more even Zn deposited behavior on low‐cost Zn foil anode. Meanwhile, the ZnCl2‐CA electrolyte can also promote redox reaction and thus improve the capacity of the Ag/RGP cathode. As a result, the assembled large‐size Ag–Zn battery (50 cm2) with low‐cost Ag/RGP and low‐concentration ZnCl2‐CA obtains a capacity of 0.76 mAh cm−2 at a current density of 0.4 mA cm−2, with a cycle life of ≈100% capacity retention over 50 cycles, also easily powering a LED array and displaying high practicability. This work provides a feasible and practical route to fabricate large‐size and low‐cost Ag–Zn batteries.

Cost-effective magnesium ion batteries (MIBs) offer a promising new pathway for next-generation large-scale energy storage, and yet its development is largely hindered by the severe polarization, limited rate capability, and poor cycling stability, where the challenges are largely rooted in the sluggish kinetics of Mg2+ storage. Here, we report a metastable phase evolution strategy that enables high-performance Mg2+ storage by leveraging Ti-modulated VS4 (T-VS4), demonstrating a structurally soft and yet dynamically adaptive lattice, which are among the preconditions for the metastable phase formation. Metastable MgxT-VS4 is formed during the initial Mg2+ intercalation, significantly facilitating the subsequent Mg2+ migration, favoring multi-electron redox reaction, and enhancing the charge transfer kinetics. Impressively, the T-VS4 cathode demonstrates exceptional Mg2+ storage performance with a high specific capacity (205.4 mAh g-1 at 50 mA g-1), excellent rate capability (up to 1000 mA g-1), and long-term cycling stability (over 3000 cycles). This work exemplifies a new metastable phase engineering approach as the design paradigm for breaking kinetic limitations in MIBs, offering a novel avenue toward next-generation energy storage systems.

Thermal runaway, a major battery safety issue, is triggered when the local temperature exceeds a threshold value resulting from slower heat dissipation relative to heat generation inside the cell. However, improving internal heat transfer is challenged by the low thermal conductivity of metal current collectors (CCs) and challenges in manufacturing nonmetal CC foils at large scales. Here we report a rapid temperature-responsive nonmetallic CC that can substitute benchmark Al and Cu foils to enhance battery safety. The nonmetallic CC was fabricated through a continuous thermal pressing process to afford a highly oriented Gr foil on a hundred-meter scale. This Gr foil demonstrates a high thermal conductivity of 1,400.8 W m−1 K−1, about one order of magnitude higher than those of Al and Cu foils. Importantly, LiNi0.8Co0.1Mn0.1O2||graphite cells integrated with these temperature-responsive foils show faster heat dissipation, eliminating the local heat concentration and circumventing the fast exothermic aluminothermic and hydrogen-evolution reactions, which are critical factors causing the thermal failure propagation of lithium-ion battery packs. Understanding and preventing thermal runaway is critical to ensuring the safe and reliable operation of batteries. Here the authors demonstrate the large-scale production of a highly conductive graphene-based foil current collector to mitigate thermal runaway in high-capacity batteries.

Room-temperature sodium - sulfur (RT Na-S) batteries are highly promising due to the favorable techno-economics and the greater availability of both sodium and sulfur. RT Na-S cells are held back by several primary challenges including dissolution of polysulfides species in liquid electrolytes, sluggish sulfur redox kinetics, as well as the large cathode volume expansion (170%) on cell discharge. Here, we develop a novel molybdenum carbide (MoC/Mo2C) electrocatalyst to promote Na-S reaction kinetics and extend batteries’ cycling lifetime. MoC/Mo2C nanoparticles is in-situ grown in conjunction with activation of nitrogen-doped hollow porous carbon nanotubes. Sulfur impregnation (50wt.% S) results in unique triphasic architecture termed MoC/Mo2C@PCNT-S. MoC/Mo2C electrocatalyst and carbon nanoporosity synergistically promote the sulfur utilization in carbonate electrolyte. In-situ time-resolved Raman, XPS and optical analysis demonstrate a quasi-solid-state phase transformation within MoC/Mo2C@PCNT-S, where minimal polysulfides are dissolved in electrolyte. As results, MoC/Mo2C@PCNT-S cathodes performed promising rate performance of 987 mAh g-1 at 1 A g-1, 818 mAh g-1 at 3 A g-1, and 621 mAh g-1 at 5 A g-1. The cells delivered a retained capacity of 650 mAh g-1 after 1000 cycles at 1.5 A g-1, which corresponds to only 0.028% capacity decay per cycle. Such promising cycling stability is also obtained for high mass loading cathodes (64wt.% S, 12.7 mg cm-2). Complementary Density Functional Theory (DFT) simulation provide fundamental insight regarding the electrocatalytic role of MoC/Mo2C nanoparticles. Favorable charge transfer between the sulfur and Mo sites on the surface of carbides contributes to a strong binding of Na2Sx (1 ≤ x ≤ 4) on MoC/Mo2C surfaces. Consequently, the formation energy of Na2Sx (1 ≤ x ≤ 4) on MoC/Mo2C is significantly lowered compared to the analogous redox in liquid, as well as the case of baseline ordered carbon.

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Bamboo Winding Composite Pipes (BWCPs) have many advantages such as good mechanical properties, low cost, low carbon, environmental friendliness, thermal insulation and easy installation et al. At present, BWCP is widely used in urban water supply, drainage, communication cable protection, farmland irrigation etc. It is obvious that the Bamboo Winding Composite materials have good application prospects in the field of utility tunnels. However, considering that the size of utility tunnels is much larger than that of normal pipelines, the wide application of Bamboo Composite Utility Tunnels (BCUTs) is still a challenge. In this paper, the force performance of BCUT in Datong Shanxi Province, was studied using two methods: real time monitoring and numerical simulation. Wireless sensor networks were used for monitoring, and the real time monitoring data of horizontal convergence and strains under the condition of backfill were obtained. The monitoring data indicated that the horizontal convergence can meet the requirement of relevant technical standards. A three-dimensional numerical model of the utility tunnel was established using FEM software. The influence of soil parameters on the deformation and strain of the BCUT was studied using the numerical method. The simulation results were compared with real-time monitoring data. The soil parameters K _ 0 and k _ sh have significant effects on the deformation, stress and strain of the utility tunnel. This kind of utility tunnel is recommended to use in areas with relatively good geological conditions. 竹缠绕复合管(BWCP) 具有力学性能优良、成本低、低碳环保、隔热、安装方便等优点。目前, 竹缠绕复合管被广泛应用于城市给排水、电缆保护、农田灌溉等领域。竹缠绕复合材料在综合管廊领域具有良好的应用前景。然而, 考虑到综合管廊的尺寸要远大于一般管道, 竹缠绕复合管廊(BCUT) 的广泛应用仍然是一个挑战。本文采用实时监测和数值模拟两种方法对山西大同的竹缠绕复合管廊受力性能进行了研究。采用WSN对其进行监测, 获得了管廊收敛和应变实时监测数据。监测数据表明水平收敛能够满足相关技术规范的要求。利用有限元软件建立了管廊的三维数值模型, 分析土体参数对管廊变形和应变的影响, 并将数值模拟结果与实测数据进行比较分析。研究表明, 土体参数 K _ 0 和 k _ sh 对管廊变形、应力和应变影响非常显著。竹缠绕复合管廊建议在地质条件较好的地区使用。

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