车辆质心位置的辨识

The position of the center of gravity (CG) of a vehicle is an important parameter as it significantly affects vehicle loads distribution and vehicle dynamics. This article proposes an approach to estimate the longitudinal position of the CG from inertial measurements. The proposed approach does not require prior knowledge of vehicle parameters apart from vehicle wheelbase. The estimation procedure combines a direct estimation from the equations of motion with the state variable filter method used in the identification of continuous time model from sampled data. The filter is chosen based on general knowledge of vehicle dynamics such as the order of magnitude of characteristic frequencies of pitch motion. The estimates are obtained through a least squares method with instrumental variables. The algorithm can be implemented in both offline batch and online recursive forms, with low computational cost suitable for embedded applications. The method is tested and validated on a high-fidelity road vehicle simulation, with good results for different load distributions.

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In the future, considering the expansion of the autonomous driving society, autonomous driving systems that can drive safely and quickly will be required for the purpose of saving lives and transporting goods even on rough road such as snowy, icy, and unpaved roads. In such unknown environments, technologies that combine model-based control and artificial intelligence (AI) are attracting attention for the purpose of ensuring operational stability and reliability. The second author has proposed a vehicle driving model that is robust to road geometry and ever-changing environmental disturbances. This model is based on a two-wheel model, and expresses the error in the position of the center of gravity of the vehicle by the front wheel steering angle deviation, and adaptively estimates this deviation. However, this model has large modeling errors when driving at high velocity on slippery roads. In this study, we extend this model proposed in previous study, and propose a new vehicle driving model that can handle situations such as driving with large acceleration changes and steering on bad roads such as snowy and wet roads. Then, we demonstrate the usefulness of the proposed method in a simulation using vehicle motion analysis software.

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Dynamical vehicle performances depend on its center of gravity’s position. It is necessary to determine this position as closely as possible. The current methods of determining the center of gravity’s position are either not sufficiently accurate or are very demanding in relation to finances, time and special equipment. At the same time, there is a lack of accurate data on the center of gravity’s position of current vehicles for further research. Therefore, the purpose of this study is to propose such a methodology of measuring the center of gravity’s position, including its height, which is accurate enough and has as low-demand measuring equipment as possible. Another purpose of this study is to obtain data on the change in the centers of gravity’s positions in different vehicles due to various load distributions. Extensive experimental measurements, according to the innovative methodology, were performed with five vehicles. The positions of the center of gravity were determined for various load distributions, and the results were analyzed in detail.

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This paper addresses the center-of-gravity height and dynamics estimation problem, which is key for rollover-prevention systems in automotive. We model the vehicle as a spring-damper system and develop a Bayesian method that outputs estimates of the center-of-gravity height, suspension stiffness and damping coefficient. We leverage the model structure to design a computationally efficient particle filter, which, combined with Bayesian optimization for particle initialization and a particle-size adaptation scheme, leads to an implementation that provides accurate, smooth estimates of CoG height, stiffness, and damping. A Monte-Carlo simulation study on a standardized maneuver shows that the method almost instantaneously provides reliable estimates that represent well the true parameter values.

In this study, the adaptive adjustment technology, based on an Attitude and Heading Reference System, is applied to loaders, aiming to improve the rollover protection performance of articulated engineering vehicles. A dynamic rollover stability index for loaders is developed, coupling the roll angular velocity and the slope and height of the center of gravity. A nonlinear predictive model of the material weight is built, in order to attain the height of the center of gravity. An adaptive attitude adjustment system is proposed, totally based on electro-hydraulic proportional control technology, to adjust the attitude of the working system, so as to achieve a higher threshold value of the vehicle’s anti-tip performance. Finally, the rollover stability index and control methods, as proposed in this article, are validated using experiments. The boundary values that trigger the system’s operation are reduced to ensure safety, while the validation results prove that the proposed method is effective.

This paper presents a practical and cost-effective method for in-motion estimation of a vehicle’s CoG position in all three directions by measuring accelerations during two types of maneuvers: braking (longitudinal and vertical CoG estimation) and cornering (lateral and vertical CoG estimation). The proposed method’s main advantage is that it does not require knowledge of vehicle characteristics, such as mass distribution, suspension geometry, or inertia parameters. It relies solely on the known distances between the sensors and their positions relative to a defined reference point on the vehicle. To validate the developed method, experimental tests were conducted on a prototype vehicle, varying the load conditions for the proposed driving scenarios. The CoG position obtained from dynamic maneuvers was compared with reference values derived from static measurements. The results showed that the proposed method could estimate the CoG position with an average error of 3% in the longitudinal direction, a maximum error of 12% in the lateral direction, and a maximum error of 14% in the vertical direction.

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S-E-A’s flagship Vehicle Inertia Measurement Facility (VIMF) has been providing centre-of-gravity (CG) and inertia measurements for over 30 years, with new versions now available to meet client needs

This article focuses on the estimation of static/ dynamic tire load and vehicle parameters using intelligent tires and vehicle dynamics. This study is conducted to improve and ensure the performance of advanced vehicle control using accurate vehicle and tire states. The contact angle between tire and road surface is calculated by an intelligent tire and is used for tire load estimation. The tire load estimation results are validated by the flexible ring tire model. For a fast sampling rate and high robustness, a new estimation algorithm, which combines intelligent tire and vehicle dynamics, is proposed in this article. Not only the tire load but also the vehicle parameters, such as total mass, center of gravity point (CG point), and center of gravity height (CG height), are estimated by the proposed estimation algorithm. The proposed estimation algorithm is verified by an indoor test using Flac trac (tire test system) and a real-time test using AutoBox III. In short, the estimation algorithm proposed in this article can estimate static/dynamic tire load and vehicle parameters with a fast sampling rate and high robustness.

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The accurate estimation of the mass and center of gravity (CG) position is key to vehicle dynamics modeling. The perturbation of key parameters in vehicle dynamics models can result in a reduction of accurate vehicle control and may even cause serious traffic accidents. A dual robust embedded cubature Kalman filter (RECKF) algorithm, which takes into account unknown measurement noise, is proposed for the joint estimation of mass and CG position. First, the mass parameters are identified based on directly obtained longitudinal forces in the distributed drive electric vehicle tires using the whole vehicle longitudinal dynamics model and the RECKF. Then, the CG is estimated with the RECKF using the mass estimation results and the vertical vehicle model. Finally, different virtual tests show that, compared with the cubature Kalman algorithm, the RECKF reduces the root mean square error of mass and CG by at least 7.4%, and 2.9%, respectively.

Many load identification methods have been proposed, but most are affected by the basic axle parameters and lateral distribution of vehicles. To effectively measure traffic flow with lateral distribution information, this article presents an innovative method that employs a strain decoupling model (SDM) and a vehicle information identification model (VIDM) to measure multi-lane vehicle load depending on the bending strain and shear strain from long-gauge fiber Bragg grating sensors. The SDM decouples the measured coupling strain into the strain for a single lane load, thereby simplifying the complex structural response resulting from lateral distributed vehicles. By exploiting the distinct characteristics of different strain types that reflect various aspects of the structure, the VIDM establishes a sophisticated mapping relationship between bending, shear strain and axle parameters, which enables the accurate determination of axle parameters including axle speed and spacing. The real-time estimation of the multi-lane vehicle load is achieved by combining the obtained axle information with the decoupled bending strain. This method effectively solves the problem of large load estimation error caused by inaccurate identification of axle parameters, and enables accurate acquisition of vehicle load in lateral distribution using bending and shear strains near the bridge entrance. Both numerical studies and laboratory tests are carried out on a simply supported beam for conceptual verification. The results demonstrate that the proposed method successfully improves the measurement of multi-lane vehicle load.

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For different transportation agencies that monitor vehicle overloads, develop policies to mitigate the impact of vehicles on infrastructure, and provide the necessary data for road maintenance, they all rely on precise, detailed and real-time vehicle data. Currently, real-time collection of vehicle data (type, axle load, geometry, etc) is typically performed through weigh-in-motion (WIM) stations. In particular, the bridge WIM (BWIM) technology, which uses instrumented bridges as weighing platforms, has proven to be the most widely used inspection method. For most of the BWIM algorithms, the position of the vehicle’s axle (i.e. vehicle wheelbase) needs to be measured before calculating the axle load, and the identification of the axle load is very sensitive to the accuracy of the vehicle wheelbase. In addition, the vehicle’s wheelbase is also important data when counting stochastic traffic flow and classifying passing vehicles. When performing these statistics, the amount of data is often very large, and the statistics can take years or even decades to complete. Traditional manual inspection and recording approaches are clearly not up to the task. Therefore, to achieve automatic measurement of the on-road vehicles’ wheelbase, a framework based on computer vision and view geometry is developed. First, images of on-road vehicles are captured. From the images, the vehicle and wheel regions can be accurately detected based on the You Only Look Once version 5 (YOLOv5) architecture. Then, the residual unified network model is improved and an accurate semantic segmentation of the wheel within the bounding box is performed. Finally, a view geometry-based algorithm is developed for identifying vehicle wheelbase. The accuracy of the proposed method is verified by comparing the identified results with the true wheelbases of both two-axle vehicles and multi-axis vehicles. To further validate the effectiveness and robustness of the framework, the effects of important factors, such as camera position, vehicle angle, and camera resolution, are investigated through parametric studies. To illustrate its superiority, the developed vehicle wheelbase measurement algorithm is compared with two other advanced vehicle geometry parameter identification algorithms and the results show that the developed algorithm outperforms the other two methods in terms of the degree of automation and accuracy.

This paper proposes a calculation method for accurately measuring axle weight, total weight and centroid coordinates of vehicles based on forward tilting method. The composition of the measuring device is elaborated, and the centroid calculation method is proposed.Then the vehicle’s centroid height error and error influencing factors are discussed under different conditions. It is concluded that the variation of the centroid error is smaller when the inclination angle of the bearing platform is greater than 12 degrees, so the inclination angle is recommended to be 12∼15 degrees. The system is calibrated by standard parts, and the position of the center of mass of the vehicle is actually measured. The repeatability error of the centroid position of the measuring system is calculated to be 0.86 mm, and the relative error when the center of mass height is maximum is 0.1%, which achieves high test accuracy.Therefore, the measuring device can be extended to the actual use field.

In the automotive sector, the use of multi-body software for modeling of existing vehicles has become essential due to its advantages in understanding vehicle dynamics in different situations and improving vehicle performances. This paper aims to model an off-road truck (4 × 4) by using ADAMS software 2020. Several steps must be achieved, including experimentally identifying some truck characteristics such as the mass, center of gravity coordinates, and tire vertical stiffness. The truck features leaf springs in both the front and rear suspensions, which must be validated before their integration into the full model due to their modeling complexity. This validation is performed by comparing the force–displacement characteristics obtained experimentally with simulation results from ADAMS, showing a good agreement. Then, the full truck is modeled in ADAMS software and validated through an experimental test using a repeated double-lane change scenario with two tests for the validation of the truck’s lateral dynamics. The comparison between the results shows a good correlation, validating the multi-body truck model.

There is a certain gas phase space in the filling of tank trucks, and the position of the liquid center of mass in the tank will change when the tank truck is being steered, which affects the driving stability of the vehicle. Taking the elliptical cross section of the tank truck body as the research object and based on the quasi-static mechanical model, the exact algorithm model of liquid centroid positions in different steering conditions of tank trucks have been deduced. Then, taking centrifugal acceleration, the liquid filling ratio, and the tank shape and dimension as independent variables and the centroid coordinates as the dependent variables, respectively, and applying MATLAB to describe the change of the liquid centroid position, the liquid centroid position in the elliptical tank truck is described when the elliptical tank truck is being steered. The factors affecting the steering stability of the tank truck are also analyzed. The conclusion provides a theoretical basis for further research into the running stability of tank trucks, the optimization of a tank’s shape and size, the modeling of lateral sloshing mechanics, and the software algorithm for the reconstruction of tank truck accidents.

Rollover index is an essential metric to detect vehicle rollover propensity, which is used to warn a driver or trigger an active rollover prevention system to perform corrective action. To give continuous and completed rollover information, this paper presents a novel rollover index based on a mass-center-position (MCP) metric. The MCP is first determined by estimating the positions of the center of mass of the vehicle, which consists of one sprung mass and two unsprung masses with two switchable roll motion models, before and after tire liftoff. The roll motion information can then be provided through the MCP continuously without saturation for both tripped and untripped rollovers. Moreover, to describe the completed rollover status, different criteria are derived from D'Alembert's principle and the moment balance conditions based on the MCP. In addition to tire liftoff, three new rollover statuses, “rollover threshold,” “rollover,” and vehicle jumping “in the air,” can be all identified by the proposed criteria. Furthermore, the sensitivity analysis is conducted on some key parameters, such as the sprung/unsprung masses and the height of the center of gravity, to validate the robustness of the proposed detection method. Compared with an existing representative rollover index, lateral load transfer ratio, the fishhook maneuver simulation results in CarSim for an electric vehicle show that the MCP metric can successfully predict vehicle impending rollover status continuously and completely for both untripped and tripped rollovers.

The Tilting Method(TM) is applied to measure the automobile mass center position widely for simple principles and easy operations. However, the TM has some problems, such as inaccurate calculation of tire force point and large tilting angle of the vehicle, so the Improved Tilting Method (ITM) is proposed in this paper. In the process of running ITM, the tire force direction is always straight up, so the friction and other adverse factors are less than that in TM. ITM applies a combined parallelogram mechanism to keep the supporting plates horizontal. In this research, we study the ITM measurement process by simulation analysis. The research results show that the design scheme and the equipment movement outcomes prove the feasibility and practicability of the ITM.

The coordinates of the center of gravity are the main input geometric parameters in the dynamic models used to assess the stability, handling and braking performance of cars. At the same time, it is known that the location of the center of gravity depends on the design features of the car and the distribution and size of the payload. The use of averaged data on the load on the axles leads to significant inaccuracies when using existing dynamic models. This report presents the results of the study on the influence of the design features and the distribution and size of the payload on the location of the center of gravity in passenger cars and estimates the mistake that is allowed when not taking into account the displacement of the center of mass of the car.

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Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour.

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The position of the vehicle’s centre of gravity has an impact on its driving performances, and it also affects the vehicle’s impact on its surroundings. Therefore, the study pays attention to the change in the centre of gravity’s position due to different load distribution. The research was performed by wide-ranging experimental measurements with three different vehicles, i.e. with a passenger car, a van and a truck. The measurement results and their analysis point to the rate of change in the centre of gravity’s position due to different load distribution, and to the fact that a vehicle axle can be overloaded even when the vehicle load capacity is not used completely. In addition, the contribution of the study is a database which can be used for other research via modelling.

Background. Agricultural production is facing a shortage of tractors due to insufficient available machinery. To address this issue, a technological module was developed to increase the versatility of Class 1.4 tractors by upgrading them to a higher traction class. To assess the operational safety of a tractor equipped with a technological module, the maximum static climbing angle was calculated. Purpose. Theoretical calculations were conducted to determine the maximum static climbing angle of a modular power and technological vehicle. Materials and methods. The stability of the MTZ-82 tractor equipped with a technological module, including the coordinates of the overall center of gravity and the maximum static climbing (slope) angle, was analyzed using computational models. Results. The horizontal and vertical coordinates of the center of gravity of the tractor and technological module were found to be 0.38 m and 0.885 m respectively. The maximum static climbing angle of the MTZ-82 tractor with technological module was found to be 71.7°. Conclusion.  Theoretical calculations of the maximum static angle of ascent have shown that the modular energy technology device is capable of performing the full range of technological operations without compromising operational safety. EDN: MSPDNZ

Accurate accident reconstruction requires the knowledge of the mass properties of vehicles, namely the centre of gravity location, the mass and the inertia tensor. Such data are seldom available, especially in case of newly produced electric vehicles. In this paper, vehicle inertia measurements, performed at Politecnico di Milano, refer to a number of electric vehicles. In addition to the “simple” measurement of vehicle inertia, measured mass properties are analysed to derive the proper empirical formulae for the estimation of the centre of gravity height and the moments of inertia. Both internal combustion and electric vehicles are considered. Data show a significant difference in the mass properties of the two types of vehicles. The proposed formulae can be effectively employed to quickly obtain a reasonable estimation of the mass properties of any vehicle. The results show that electric vehicles are characterised by higher values of mass with respect to internal combustion vehicles, but they present a lower centre of gravity location and proportionally lower values of the moments of inertia.

In vehicular accident reconstruction, a number of parameters need to be estimated, as commonly no specific measurement data or convenient measurement methods are available. One of these parameters is the position of a car's centre of gravity. Depending on the impact configuration, the centre of gravity may have a significant influence on the reconstruction result. A number of regression models and rules of thumb have already been developed in the past to calculate the position of the centre of gravity. The further automotive vehicle development in recent years has led to different vehicle architectures with larger masses. This study therefore deals with developing and testing a new regression model for vehicles, distinguishing between conventional and electric drives. That is based on the analysis of 147 rollover stability measurements of road vehicles from the years 2016-2022. The model developed from these tests for the centre of gravity height shows a good fit with the measurement data and only requires knowledge of the roof height.

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Abstract The paper presents the results of road and simulation tests depicting the impact of vertical static load on the coupling on the braking distance of a combination vehicle. A hypothesis was put forward that verification by the vehicle user and a possible change in the static vertical load on the coupling (as a result of the incorrect position of the trailer’s centre of gravity) could influence the braking distance of a combination vehicle. The article presents the geometric and mass data obtained for the vehicles tested. This is followed by a description of road tests carried out under real conditions and the results of simulation calculations, where the braking distance of a vehicle combination consisting of a tractor unit and a loaded (in six variants) single-axle unbraked trailer was assessed. The parameters of the road surface on which the tests were carried out were defined. In the course of the tests, the distance travelled by the vehicles and the force applied to the brake pedal were recorded. The result of the work is a report presenting the results of the assessment of the braking distance depending on the static vertical load on the coupling device.

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Nowadays, there is also a trend in rail transport to change to more environmentally friendly power cells. One such solution is the introduction of hydrogen fuel cells into the construction of rail vehicles. This paper studies the changes in the running properties of a modified rail vehicle and its components after replacing the standard diesel-electric powertrain unit with a new one using hydrogen fuel cells. This modification results in significant changes in the vehicle structure, especially in the mass and position of the centre of gravity of its various components. The running characteristics are analysed by simulation calculations using the commercial simulation software Simpack. A multibody simulation model consisting of rigid bodies connected by flexible force elements is used. This analysis is performed for both the original rail vehicle and the modified rail vehicle with hydrogen fuel cells. As a result, the modified rail vehicle with a hydrogen powertrain was found to have a similar force distribution in wheel/rail contact, but the axle load of this vehicle did not meet the criteria.

Distributed drive electric vehicles (DDEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size, and inertial parameter has seldom been tackled and systematically estimated. This paper presents a dual central difference Kalman filter (DCDKF) where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters, such as vehicle sideslip angle, vehicle mass, vehicle yaw moment of inertia, the distance from the front axle to centre of gravity. The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs. The four-wheel nonlinear vehicle dynamics estimation model considering payload variations, Pacejka tire model, wheel and motor dynamics model is developed, the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory. To address system nonlinearities in vehicle dynamics estimation, the DCDKF and dual extended Kalman filter (DEKF) are also investigated and compared. Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-Carsim^®. The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions . This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

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Influenced by tire effective radius change, suspension rearrangement, and pitch/roll disturbance due to tire blowout, the vehicle center of gravity (CG) can significantly relocate toward the blown-out tire position. This paper proposes an estimation method of the CG relocation for ground vehicles with tire blowout by utilizing vertical force variations and geometric relationships in tire blowout events. Based on a new recursive least square (RLS) formulation in this paper, the three-dimensional (3D) CG relocation (i.e., the height, the longitudinal and lateral positions) can be estimated simultaneously. Matlab/Simulink and CarSim® co-simulation results for different tire blowout locations validate that the proposed estimation method can effectively and accurately capture the vehicle 3D CG relocation after tire blowout.

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A variety of control functions are used in modern vehicles to stabilize the vehicle dynamics. These can be improved with precise information of time-varying parameters. The vehicle mass, center of gravity height and the roll moment of inertia are significant for vehicle roll dynamics as they characterize the static and dynamic behavior of the roll motion. These parameters not only affect the driving behavior but can also increase the risk of rollover. Since the roll moment of inertia is particularly significant for transient roll dynamics, the main contribution of this paper is to develop a model-based estimation algorithm to estimate the roll moment of inertia. This paper therefore presents an Unscented Kalman Filter for a simultaneous state and parameter estimation. By using a nonlinear vehicle model which represents the roll, pitch and vertical dynamics, the effects of the center of gravity height and additional masses on the inertia are taken into account. In order to improve the estimation results, an activation condition based on a linear single track model and an underlying observability analysis is presented. Based on that, a precise parameter estimation with a deviation of less than 5 % to the nominal parameter is achieved.

The height of the center of gravity (ZCG) is a critical parameter for evaluating vehicle safety and performance. Systematic errors arise in ZCG measurement via the tilt-table test method due to unlocked suspension systems and variable sprung mass conditions, which compromise accuracy. To address this limitation, a CNN–LSTM–Attention model integrating convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and an attention mechanism is proposed. The CNN extracts spatial correlations among vehicle load transfer, suspension stiffness, and tilt angles. The LSTM captures temporal dependencies in tilt angle sequences, while the attention mechanism amplifies critical load-transfer features near the 0° region. Simulations of vehicles with unlocked suspension and variable sprung mass were conducted in Adams using tilt-table protocols. The CNN–LSTM–Attention model was trained on simulation data and validated with real-world tilt-test data under identical suspension conditions. Results demonstrate that the CNN–LSTM–Attention model achieves at least a 6.9% improvement in computational speed and at least a 0.1% reduction in prediction error compared to CNN, CNN-LSTM, and Transformer baselines. The CNN–LSTM–Attention model demonstrates valid predictive capability for ZCG at 0° tilt angle. This novel approach provides a robust solution for the tilt-table test method ZCG measurement, enhancing practical accuracy in vehicle dynamics parameter quantification.

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Vehicle axle load scales are one of the most important devices for vehicle safety testing. To ensure the stability and reliability of test results, regular calibration of axle load scales is necessary. Traditional calibration methods are inefficient and error-prone. In this work, an automatic calibration device for portable axle load scales was presented, which uses a pump-controlled hydraulic cylinder as a loading unit. The loading unit was controlled by a high-precision force sensor and a PLC. A hydraulic unit based on a servo motor and a gear pump was designed, and control software including automatic control, data acquisition, and report generation was developed. The experimental test was carried out. The results showed that the developed portable automatic calibration device could realize the automatic calibration of a 0~150 kN load range, and the accuracy level was up to ±0.3%. Finally, it was verified that the device had the advantages of compactness and lightweight and simple operation.

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Abstract The paper presents the results of experimental verification of the constructed simulation model of the two-axle vehicle - single-axle unbraked trailer assembly. The model describes the effect of the trailer’s centre of mass position on the braking distance. The constructed model and the assumptions adopted for its construction are described at the beginning. The acquired geometric and mass data of the vehicles used for the tests are presented. The further part of the paper describes road tests in real conditions, where the braking distance of the towing vehicle itself and the towing vehicle - loaded (in six variants) single-axle unbraked trailer assembly were assessed. The parameters of the road surface on which the tests were conducted were determined. During the tests, the distance travelled by the vehicles and the force applied to the brake pedal were recorded. The results of experimental verification of the constructed simulation model are presented, where the values of the braking distance and deceleration were assessed. The model with confirmed usefulness will be used as a software component, which will be a real effect of implementation in the workplace of the solution of one of the co-authors of this study. A utilitarian effect will also be obtained, expanding the knowledge of people dealing with the issue of road accidents (court experts and car appraisers). The summary contains conclusions and comments for further research. The research was carried out as part of the statutory work conducted at the Military Institute of Armoured and Automotive Technology.

The article proposes a method for calculating the static position of the machine body. The mathematical model of the running gear is given. The dependence of the position of the center of mass in height relative to the ground on the pre-tension forces, as well as the angular deviation of the longitudinal axis of the hull from the horizontal position, is shown. A control calculation was performed, the results of which are presented in the form of graphical dependencies. The numerical experiment was carried out on the example of a tracked vehicle of a light category with a rear drive wheel.