Multi-dimensional testing method and device for golf cart battery

Abstract

The invention relates to a multi-dimensional testing method and device for a golf cart battery. The method comprises the following steps: carrying out standard working condition operation data acquisition on a golf cart battery, and constructing an electrochemical-thermal-mechanical coupling theoretical model to obtain a battery multi-dimensional coupling theoretical matrix; the method comprises the following steps: arranging a temperature sensor and a strain gauge on the surface of a golf cart battery, executing real-time data synchronous acquisition to obtain multi-dimensional actual measurement data of the battery, and carrying out electrochemical impedance spectroscopy test on the battery to obtain a battery impedance characteristic vector; dispersing the battery volume into control volume units and carrying out thermal-mechanical coupling iterative solution to obtain a battery thermal-mechanical coupling characteristic matrix; and performing battery performance evaluation based on the battery impedance characteristic vector, the battery thermal coupling characteristic matrix and the battery multi-dimensional measured data to obtain a battery performance evaluation result. According to the invention, the interaction among the physical fields in the battery can be comprehensively captured, and the real-time monitoring and quick response of the multi-dimensional physical quantity of the battery are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of battery testing, and in particular to a multi-dimensional testing method and device for a golf cart battery. Background Art

[0002] Golf carts are increasingly used as short-distance transportation tools, and the performance test and status evaluation of their core power source batteries have become a key link in ensuring safety. At present, golf cart battery testing methods mainly focus on the measurement and analysis of single physical quantities, such as independent monitoring of voltage, current, temperature and other parameters. This testing method ignores the coupling between the physical fields inside the battery, resulting in the test results being unable to fully reflect the actual working status of the battery.

[0003] There are three main problems with traditional battery testing methods. First, in the actual working process of the battery, there are multi-field coupling effects of electrochemical reactions, heat conduction and mechanical stress. These physical fields influence and restrict each other, and the measurement of a single physical quantity cannot accurately describe the performance characteristics of the battery. Second, the existing test data often contains a large amount of interference information related to the working conditions, lacks an effective feature decoupling method, and it is difficult to extract characteristic indicators that reflect the intrinsic performance of the battery. There is a lack of a systematic coordination mechanism in the collection and analysis of test data, which leads to information redundancy and inconsistency between the test results. Summary of the invention

[0004] The main purpose of the present invention is to provide a multi-dimensional testing method and device for a golf cart battery, which can fully capture the interaction between various physical fields inside the battery, and realize real-time monitoring and rapid response of multi-dimensional physical quantities of the battery.

[0005] To achieve the above object, the present invention provides a multi-dimensional testing method for a golf cart battery, comprising the following steps: Collect data of golf cart battery under standard working conditions, build electrochemical-thermal-mechanical coupling theoretical model, and obtain multi-dimensional coupling theoretical matrix of battery; A temperature sensor and a strain gauge are arranged on the surface of the golf cart battery to perform real-time data synchronous acquisition to obtain multi-dimensional measured data of the battery, and an electrochemical impedance spectrum test of the battery is performed to obtain a battery impedance characteristic vector; According to the multi-dimensional measured data of the battery and the multi-dimensional coupling theoretical matrix of the battery, the battery volume is discretized into control volume units and thermal-mechanical coupling iterative solution is performed to obtain the battery thermal-mechanical coupling characteristic matrix; The battery performance is evaluated based on the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the multi-dimensional measured data of the battery to obtain a battery performance evaluation result.

[0006] The present invention also provides a multi-dimensional testing device for a golf cart battery, comprising: A data acquisition unit is used to collect data of golf cart batteries under standard operating conditions, build an electrochemical-thermal-mechanical coupling theoretical model, and obtain a multi-dimensional coupling theoretical matrix of the battery; A testing unit, used to set a temperature sensor and a strain gauge on the surface of the golf cart battery and perform real-time data synchronous acquisition to obtain multi-dimensional measured data of the battery, and perform a battery electrochemical impedance spectrum test to obtain a battery impedance characteristic vector; An iterative solution unit, used to discretize the battery volume into control volume units and perform thermal-mechanical coupling iterative solution according to the multi-dimensional measured data of the battery and the multi-dimensional coupling theoretical matrix of the battery, so as to obtain a battery thermal-mechanical coupling characteristic matrix; The performance evaluation unit is used to evaluate the battery performance based on the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the multi-dimensional measured data of the battery to obtain a battery performance evaluation result.

[0007] In summary, the technical solution provided by the present invention realizes the accurate measurement of the multi-physical field coupling characteristics of the battery: by establishing an electrochemical-thermomechanical-mechanical three-field coupling model and combining the real-time data acquisition of the distributed sensor network, the interaction between the physical fields inside the battery can be fully captured, and the accuracy and completeness of the test data are improved. An effective decoupling solution for operating condition interference is provided: a feature extraction method combining contrast learning and adversarial training is adopted to successfully separate the operating condition-related features and the intrinsic performance characteristics of the battery, and enhance the reliability and versatility of the test results. A multi-dimensional collaborative testing system is established: through the iterative solution of thermal-mechanical coupling and the fusion of multi-scale features, a unified evaluation of various performance indicators of the battery is achieved, avoiding the information redundancy and inconsistency problems in traditional testing methods. The accuracy of performance evaluation is optimized: based on the comprehensive analysis of battery impedance characteristics, thermal-mechanical coupling characteristics and measured data, a complete performance evaluation index system is constructed to improve the accuracy and predictability of battery performance evaluation. The real-time performance of the test process is enhanced: through distributed data acquisition and synchronous control strategies, real-time monitoring and rapid response of multi-dimensional physical quantities of the battery are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a schematic diagram of the steps of a multi-dimensional testing method for a golf cart battery in one embodiment of the present invention; Figure 2 1 is a structural block diagram of a multi-dimensional testing device for a golf cart battery in one embodiment of the present invention.

[0009] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0011] Reference Figure 1 , this embodiment provides a multi-dimensional testing method for a golf cart battery, comprising the following steps: S1, collect standard operating data of golf cart batteries, build an electrochemical-thermal-mechanical coupling theoretical model, and obtain a multi-dimensional coupling theoretical matrix of batteries; Among them, the operating data of the golf cart battery under different charge and discharge conditions are collected, including the voltage, current and temperature distribution data at 0.5C, 1C and 2C rates. These data constitute the original data set of the battery operation. The original data set is preprocessed to remove outliers, interpolate and complete, and perform normalization to obtain a battery standardized data set. The electrochemical-thermomechanical coupling theoretical model of the battery is constructed based on the standardized data set. The battery structure is divided into five layers, namely the negative electrode current collector, the negative electrode, the separator, the positive electrode and the positive electrode current collector. This hierarchical division can describe the electrochemical characteristics of each component inside the battery, and then establish the ion transport equation of each layer. The ion transport equation uses the Poisson-Nernst-Planck equation or the Fick diffusion equation to describe the ion migration behavior in the electrode and the electrolyte, and combines the battery's charge conservation relationship to derive a complete electrochemical reaction characteristic matrix, which is used to characterize the potential distribution, charge accumulation and current density changes inside the battery. A three-dimensional unsteady-state heat conduction equation is established based on the electrochemical reaction characteristic matrix to describe how the heat generated by the battery during the charging and discharging process is transferred and diffused internally. The heat sources inside the battery mainly include Ohmic heating, polarization heat, and side reaction heat, and these heat source terms are directly related to the electrochemical reaction. Therefore, a mapping relationship is established between the heat source terms and the electrochemical reaction to obtain a heat conduction characteristic matrix. To achieve this goal, the battery is meshed and the battery structure is discretized into a finite number of small units. The temperature change of each unit is controlled by a three-dimensional unsteady-state heat conduction equation. This equation is based on Fourier's heat conduction law and combines the thermophysical parameters of the battery material, such as thermal conductivity, specific heat capacity, and density, to calculate the distribution of the temperature field. The equation is solved by numerical methods (such as finite element method or finite difference method) to obtain the heat conduction characteristic matrix of each layer of the battery, which is used to describe the temperature distribution and heat flow path of the battery under different working conditions. Based on the heat conduction characteristic matrix, the volume change of the electrode material during the charging and discharging process is calculated, and the stress distribution function is established in combination with the mechanical constitutive relationship. This function is used to describe the mechanical stress distribution in different regions inside the battery, thereby deriving the mechanical stress characteristic matrix. The mechanical stress characteristic matrix reveals problems such as deformation, expansion, and even cracks that occur in the battery during cycling. The electrochemical reaction characteristic matrix, thermal conductivity characteristic matrix, and mechanical stress characteristic matrix are input into the multi-physics simulation solver for multi-field coupling calculations to obtain the multi-dimensional coupling theory matrix of the battery. The multi-physics simulation solver solves electrochemical, thermodynamic, and mechanical equations simultaneously, and considers their mutual influence. For example, the temperature distribution affects the electrochemical reaction rate of the battery, and changes in the electrochemical reaction rate affect the heat release of the battery. At the same time, changes in mechanical stress may cause material performance to deteriorate, further affecting the electrochemical behavior of the battery.

[0012] S2, setting a temperature sensor and a strain gauge on the surface of the golf cart battery and performing real-time data synchronous acquisition to obtain multi-dimensional measured data of the battery, and performing an electrochemical impedance spectrum test on the battery to obtain a battery impedance characteristic vector; Specifically, sensors are arranged reasonably on the battery surface to obtain temperature and stress data. In this process, the surface of the golf cart battery is divided into equally spaced grids to ensure uniform distribution of measurement points. M temperature sensors are evenly arranged on the divided grid points to obtain temperature distribution information on the battery surface. At the same time, since the battery is affected by mechanical stress caused by thermal expansion, material expansion and current flow during charging and discharging, a stress concentration area of ​​the golf cart battery shell is subjected to force analysis to determine the location with greater mechanical stress. After completing the force analysis, N strain gauges are arranged at the target structure position to measure the mechanical stress and deformation of the battery during operation. The output signals of the temperature sensor and strain gauge are conditioned to eliminate noise in the signal and improve the measurement accuracy. Since the temperature sensor and strain gauge output analog signals, these analog signals are standardized to ensure the comparability and consistency of the measurement data. Standardized analog signals can eliminate deviations caused by individual differences in sensors, thereby improving the reliability of the data. After completing the signal conditioning, the standardized analog signals are digitized using analog-to-digital conversion technology to obtain digital measurement data for subsequent analysis. Based on the digital measurement data, the sampling timing of the temperature sensor and the strain gauge are synchronously controlled to obtain the multi-dimensional measured data of the battery. The state of charge test points are selected according to the multi-dimensional coupling theory matrix of the battery to ensure that the test points represent the characteristics of the battery under different working conditions. The selection of the state of charge test points is based on the SOC curve of the battery to cover low, medium and high states of charge, thereby characterizing the electrochemical behavior of the battery. The battery is subjected to an electrochemical impedance spectroscopy test to analyze its impedance characteristics. The electrochemical impedance spectroscopy test obtains the impedance change of the battery at different frequencies by applying a small amplitude alternating current signal to the battery and measuring its voltage response. By fitting and analyzing the impedance data at different frequencies, the internal equivalent circuit parameters of the battery, including ohmic resistance, charge transfer resistance and diffusion impedance, are extracted to characterize the electrochemical performance of the battery. Through the above steps, the multi-dimensional measured data of the battery and the battery impedance characteristic vector are obtained.

[0013] Based on the electrochemical reaction characteristic data in the battery multi-dimensional coupling theory matrix, the test conditions are optimized and appropriate state of charge test points are selected to ensure that the test of the battery impedance characteristics fully reflects its internal state. Since the electrochemical behavior of the battery is affected by factors such as the state of charge (SOC), temperature, and charge and discharge rate, these factors are comprehensively considered when optimizing the test conditions, and the state of charge test points that can represent different operating states are selected by analyzing the equivalent circuit characteristics of the battery. A suitable AC excitation signal is set for each state of charge test point to stimulate the electrochemical reaction of the battery within different frequency ranges to form a test condition parameter set. The test condition parameter set mainly includes parameters such as the amplitude of the excitation signal, the frequency range, and the measurement time. Based on the test condition parameter set, an AC excitation signal is applied to the golf cart battery for electrochemical impedance spectroscopy scanning. The electrochemical impedance spectroscopy scan analyzes the impedance characteristics of the battery by applying a small amplitude AC signal to the battery and measuring its voltage response. A sinusoidal current signal is applied at different frequencies, and the corresponding voltage changes are measured to calculate the AC impedance of the battery. The electrochemical impedance spectroscopy scan covers multiple frequency points from high frequency to low frequency to fully characterize the internal transmission characteristics of the battery. Through this method, the impedance response data of the battery is obtained, including the impedance modulus and phase angle information at different frequencies. According to the impedance response data, an equivalent circuit model is constructed to analyze the electrochemical characteristics of the battery. The battery equivalent circuit model consists of multiple resistors, capacitors and impedance elements, which are used to describe the ohmic loss, charge transfer process, double layer effect and ion diffusion behavior inside the battery. The equivalent circuit model includes series resistance (Rs), which is used to describe the bulk resistance and current collector resistance of the battery; charge transfer resistance (Rct), which is used to characterize the electrochemical reaction impedance at the electrode-electrolyte interface; double layer capacitance (Cdl), which is used to describe the charge accumulation effect on the electrode surface; and Warburg impedance (Zw), which is used to characterize the ion diffusion behavior inside the battery. By reasonably constructing an equivalent circuit model, the electrochemical impedance characteristics of the battery can be effectively analyzed. The impedance response data is input into the nonlinear least squares fitting algorithm to solve the unknown parameters in the equivalent circuit model. The nonlinear least squares fitting algorithm iteratively optimizes the circuit parameters by minimizing the error between the experimental data and the calculation results of the equivalent circuit model to obtain the best fitting result. This method accurately extracts the equivalent circuit parameter set, including key parameters such as Rs, Rct, Cdl and Zw, and provides high-precision battery impedance characteristic analysis. The frequency domain characteristic analysis of the equivalent circuit parameter set is performed to extract the key impedance characteristic vector. The impedance in the high-frequency region is mainly dominated by the ohmic impedance (Rs), which reflects the internal resistance and current collection efficiency of the battery; the impedance in the medium-frequency region is mainly determined by the charge transfer impedance (Rct), which reflects the charge transfer kinetics at the electrode-electrolyte interface; and the impedance in the low-frequency region is mainly dominated by the concentration polarization impedance (Zw), which describes the ion diffusion and polarization behavior inside the battery.By analyzing these frequency domain characteristic parameters, the impedance characteristic vector of the battery is extracted.

[0014] S3, based on the multi-dimensional measured data of the battery and the multi-dimensional coupling theoretical matrix of the battery, the battery volume is discretized into control volume units and the thermal-mechanical coupling iterative solution is performed to obtain the battery thermal-mechanical coupling characteristic matrix; It should be noted that the battery volume is divided into three-dimensional grids using multi-dimensional measured data of the battery to ensure accurate description of the temperature field and stress field distribution of the battery during the numerical calculation process. When meshing, the geometric structure of the battery is discretized into multiple small control volume units, and a unique identification number is assigned to each control volume unit to construct a battery grid model. The initial field distribution of heat conduction is established based on the multi-dimensional coupling theory matrix of the battery, and the measurement data of the temperature sensor is mapped to the grid model using the three-dimensional spline interpolation algorithm to obtain the initial value of the temperature field. The interpolation algorithm is used to spatially expand the measured data to reconstruct the complete temperature distribution field inside the battery. The three-dimensional spline interpolation algorithm effectively guarantees the continuity and numerical stability of the temperature field by constructing a high-order smooth interpolation function, and obtains a high-precision initial distribution of the temperature field. The initial value of the temperature field is processed by boundary conditions and the thermal-mechanical coupling relationship is analyzed to establish the thermal system equation. The setting of boundary conditions needs to be combined with the actual use environment of the battery, including factors such as natural convection heat dissipation, radiation heat transfer, and contact thermal resistance with the shell or cooling system. At the same time, since the temperature change inside the battery will cause thermal expansion of the material, and the electrode material will also produce volume changes due to the insertion and extraction of lithium ions during the charging and discharging process, the interaction between temperature and mechanical stress is considered in the thermal system equation, and a thermal-mechanical coupling relationship model is established. This model introduces the thermal stress equation to describe the stress change of the battery under different temperature field distributions, and couples the heat conduction equation with the stress field equation so that the heat transfer and mechanical response are calculated synchronously. The thermal system equation is input into the finite volume method solver to perform alternating iterative calculations of the temperature field and stress field, and a convergence criterion is set during the calculation process to ensure the stability and accuracy of the calculation. The finite volume method is a numerical calculation method for computational fluid dynamics and heat conduction problems. It discretizes the heat conduction equation by analyzing the heat conservation of the control volume unit, and uses a numerical solution method for iterative calculations. During the solution process, the temperature and stress field values ​​of each control volume unit will gradually converge with the increase of the number of iterations. In order to determine whether the calculation has reached a convergence state, a convergence criterion is set, that is, when the relative error of the temperature field of adjacent iteration steps is less than a certain target value, the iteration is stopped and the final thermal-mechanical coupling distribution is obtained. Based on the thermal-mechanical coupling distribution, temperature gradient stress analysis is performed to calculate the thermal stress and strain of each control volume unit, and a heat source correction model under the influence of stress is established. Since battery materials will generate additional stress when they expand due to heat, this stress will affect the heat conduction path inside the battery, thereby changing the thermal distribution of the battery. Therefore, the correction effect of the stress field on the heat source term is calculated through thermal stress analysis, and the heat source term in the thermal equation is adjusted to obtain a thermal distribution correction model that is more in line with the actual situation, reflecting the thermal stress influence of the battery under different operating conditions. The temperature field data and stress field data of each control volume unit in the corrected thermal distribution are combined and mapped to form a battery thermal-mechanical coupling characteristic matrix.

[0015] The control volume discretization is performed on the equations of the thermal system, and the continuous partial differential equations are converted into a set of algebraic equations suitable for numerical calculation. In the control volume method, the calculation domain of the battery is divided into multiple small control volume units, and the discretized representation of the heat conduction equation and the mechanical equilibrium equation is established in each unit. The discretization of the heat conduction equation adopts the finite volume method, and the integral form of the heat flux on the control volume surface is converted into an algebraic relationship to obtain a discrete algebraic equation containing the thermal conductivity matrix of the temperature field. For the mechanical equilibrium equation, based on the idea of ​​the finite element method, the deformation and stress of the internal materials of the battery are discretized, and a set of equations containing the stiffness matrix of the stress field is established. By combining the control volume method and the finite element method, a complete control volume discrete equation is constructed to describe the relationship between heat conduction and stress distribution inside the battery. Based on the control volume discrete equation, the coupling matrix of the momentum equation and the energy equation is constructed to realize the joint solution of the temperature field and the stress field. The momentum equation is mainly used to describe the deformation behavior of the internal materials of the battery under the action of thermal expansion and mechanical stress, while the energy equation is used to describe the heat conduction and temperature distribution inside the battery. When constructing the coupling matrix, the boundary conditions of the temperature field and the stress field are reasonably processed to ensure the stability and accuracy of the calculation results. The processing method of the boundary conditions depends on the working environment of the battery. For example, if there is a forced cooling system outside the battery, the convection heat transfer coefficient is introduced in the boundary conditions of the energy equation, and if the battery housing is subject to fixed constraints, the displacement constraint or stress boundary condition is applied in the boundary conditions of the momentum equation. By setting the boundary conditions reasonably, it is ensured that the coupling solution matrix accurately describes the thermodynamic behavior of the battery under the actual working state. The coupling solution matrix is ​​input into the SIMPLE algorithm iterative solver to calculate the thermal stress increment caused by the temperature field and obtain the temperature stress correction. The SIMPLE algorithm is an iterative solution method for computational fluid dynamics and heat transfer problems, which gradually approaches a stable solution by alternately solving pressure, velocity and temperature fields. In this embodiment, the SIMPLE algorithm is used to calculate the influence of the temperature field on the stress field, and updates the interaction between the temperature field and the stress field in each iteration. Specifically, the distribution of the temperature field is calculated by solving the energy equation, and then the temperature field is used to solve the mechanical equilibrium equation, calculate the stress increment caused by the temperature change, and further update the stress field distribution. Through continuous iterative calculations, it gradually converges to a stable thermomechanical coupling state, and obtains the temperature stress correction. According to the temperature stress correction, the temperature field distribution of the control volume unit is corrected and calculated, and the velocity-pressure coupling relationship between the stress field and the temperature field is established to ensure the physical consistency of the calculation. Since changes in the temperature field will cause the material to expand or contract, thereby affecting the distribution of the stress field, in each iterative step, the temperature field is corrected and the new stress distribution is calculated to ensure the accuracy of the coupled solution.To this end, the correction coefficient matrix is ​​constructed using the data of the corrected temperature field and the corrected stress field. The matrix is ​​used to describe the interaction between the temperature field and the stress field, and the temperature field is dynamically adjusted in the subsequent iterative calculation to ensure the convergence and stability of the calculation. The relative error of the temperature field of adjacent iteration steps is calculated based on the correction coefficient matrix, and the relative error is compared with the set target value to determine whether the iteration termination criterion is met. At the end of each iteration step, the relative error between the current temperature field and the temperature field of the previous iteration step is calculated, and it is determined whether the error is less than the preset convergence threshold. If the relative error is less than the target value, it is considered that the calculation has converged, the iteration is terminated and the calculation results are output; otherwise, it is necessary to continue iterating until the convergence condition is met. By setting a reasonable convergence criterion, the accuracy of the calculation results is ensured and unnecessary waste of computing resources is avoided. After the iteration termination criterion is met, the calculated temperature field and stress field data are post-processed to extract the temperature distribution and stress distribution data of each control volume unit and generate the thermal-mechanical coupling distribution. The main contents of post-processing include data visualization, statistical analysis, and joint analysis with other physical field data. For example, use heat maps or contour maps to display the temperature distribution inside the battery to identify hot spots; at the same time, analyze the stress distribution to assess the risk of structural damage or fatigue failure during long-term cycling of the battery.

[0016] S4, performing battery performance evaluation based on the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the battery multi-dimensional measured data to obtain a battery performance evaluation result.

[0017] Specifically, the battery impedance characteristic vector, the thermomechanical coupling characteristic matrix and the multi-dimensional measured data of the battery are input into the dual encoder network to fuse and weight the feature data from different sources. The dual encoder network consists of two parallel encoder modules, which process the electrochemical characteristics and thermodynamic and mechanical characteristics of the battery respectively, and learn the working condition feature representation through the feature weighting mechanism to reduce the influence of the measurement conditions and improve the generalization ability of the model. In this process, the self-attention mechanism is used to dynamically adjust the importance of different features, so that the key features obtain higher weights during the calculation process, ensuring that the working condition feature representation fully reflects the operating status of the battery. The InfoNCE contrast loss function is constructed based on the working condition feature representation, and the batch normalization layer and Adam optimizer are used to iteratively optimize the dual encoder network. The InfoNCE contrast loss function maximizes the similarity of positive samples while minimizing the similarity of negative samples, thereby improving the robustness of the model under different working conditions. The batch normalization layer is used to stabilize the network training process, reduce internal covariate shift, and improve the efficiency of feature extraction. The Adam optimizer combines momentum optimization and adaptive learning rate adjustment mechanism to make the network converge quickly during training and effectively suppress gradient oscillation. After multiple rounds of iterative training, the model gradually removes the working condition dependency and extracts stable working condition-independent features. The working condition-independent features are input into the discriminator network for feature adversarial training to minimize the accuracy of working condition prediction, thereby enhancing working condition independence. By constructing a working condition classification cross entropy loss function and combining it with a weighted combination of feature reconstruction loss functions, gradient update optimization is performed to enable the model to effectively remove redundant information related to the working condition. The role of the discriminator network is to try to predict the working condition category based on the extracted features, while the goal of adversarial training is to make the features generated by the encoder unable to be accurately classified by the discriminator, thereby forcing the encoder to further remove the working condition information and only retain the essential features related to battery performance. Through the adversarial training strategy, it is ensured that the performance feature vector finally extracted has strong generalization ability and can maintain consistency under different working conditions, thereby improving the accuracy of battery performance evaluation. The stable performance feature vector is constrained by probability distribution to ensure its numerical stability and interpretability in high-dimensional space. The KLD loss function is calculated based on the performance feature vector, and the feature is mapped to the normal distribution space by minimizing the relative entropy between the feature distribution and the target distribution. The role of KLD loss is to make the feature distribution learned by the model smoother, thereby reducing noise interference and improving the generalization ability of the model. In this process, the variational autoencoder method is used to constrain the feature distribution to the standard normal distribution, and the transferability of the gradient is ensured by the reparameterization technique of the latent variable. By minimizing the KLD loss, a normalized battery feature set is obtained, which can remain stable under different test conditions. Performance indicators are calculated for the normalized battery feature set to quantify the health status of the battery.Performance indicators include capacity decay rate, internal resistance growth rate, temperature consistency coefficient and stress concentration coefficient. Among them, capacity decay rate is used to describe the degree of capacity loss of the battery during the cycle use process, and is calculated by comparing the current capacity with the initial capacity; the internal resistance growth rate reflects the increasing trend of the internal ohmic impedance and polarization impedance of the battery, which is related to the battery's charging and discharging efficiency and power performance; the temperature consistency coefficient is used to measure the uniformity of the temperature distribution inside the battery. A large temperature gradient will lead to a shortened battery life or increased safety risks; and the stress concentration coefficient is used to analyze the mechanical stress distribution of the battery structure to predict structural damage or material fatigue. By calculating these key performance indicators, the health status of the battery is evaluated. The capacity decay rate, internal resistance growth rate, temperature consistency coefficient and stress concentration coefficient are input into the degradation mechanism analysis model for time series feature mapping and performance degradation warning. The degradation mechanism analysis model is based on time series modeling methods, such as long short-term memory networks or time convolution networks, to learn the historical data of the battery and predict future performance degradation trends. By performing time series analysis on indicators such as capacity decay rate and internal resistance growth rate, the degradation mode of the battery is identified and its future health status is predicted. In combination with physical modeling methods, such as equivalent circuit models or pseudo-two-dimensional models, the internal chemical and physical processes of the battery are modeled to improve the accuracy of degradation mechanism analysis. By comprehensively utilizing data-driven and physical modeling methods, high-precision battery performance degradation warning results are generated.

[0018] In one example, standard operating data of a golf cart battery is collected to construct an electrochemical-thermal-mechanical coupling theoretical model, and a multi-dimensional coupling theoretical matrix of the battery is obtained, including: The voltage, current and temperature distribution data of the golf cart battery under 0.5C, 1C and 2C rate charge and discharge conditions are collected to obtain the original battery operation data set, and the original battery operation data set is preprocessed to obtain the battery standardized data set; Based on the battery standardized data set, the battery structure is divided into five layers: negative electrode current collector, negative electrode, separator, positive electrode, and positive electrode current collector. The ion transport equations of each layer are established to obtain the electrochemical reaction characteristic matrix. A three-dimensional unsteady-state heat conduction equation is established based on the electrochemical reaction characteristic matrix, the golf cart battery is meshed, a mapping function between the heat source term and the electrochemical reaction is established, and a heat conduction characteristic matrix is ​​obtained; Based on the thermal conductivity characteristic matrix, the volume change of the electrode material during the charge and discharge process is calculated, and the stress distribution function is established to obtain the mechanical stress characteristic matrix; The electrochemical reaction characteristic matrix, thermal conduction characteristic matrix and mechanical stress characteristic matrix are input into the multi-physics field simulation solver for coupling calculation to obtain the multi-dimensional coupling theoretical matrix of the battery.

[0019] In this example, the battery operation data is collected at different charge and discharge rates to ensure that the test results cover a variety of actual usage scenarios. When the charge and discharge tests are performed at 0.5C, 1C and 2C rates, the voltage, current and temperature distribution data of the battery are recorded synchronously to form the original data set of the battery operation. The original data set is preprocessed to improve the data quality and calculation accuracy. The data preprocessing process includes removing outliers, interpolating and completing, time alignment of different measurement signals, and performing normalization to ensure that the data of different physical quantities have the same numerical scale. After data processing, a standardized battery data set is obtained. Based on the standardized data set, different functional layers are divided according to the internal structure of the battery, and the corresponding ion transport equations are established to characterize the electrochemical behavior inside the battery. The basic structure of a lithium-ion battery includes five main layers: negative current collector, negative electrode, separator, positive electrode and positive current collector. Each layer plays a different role in the battery operation process. In the negative electrode and positive electrode, the insertion and extraction of lithium ions are the main electrochemical reactions, while in the separator, lithium ions are mainly transported by diffusion, so the ion transport equations applicable to different layers are established respectively. Assume that the distribution of lithium ion concentration in the x direction is , then its diffusion behavior is described by Fick's first law:

[0020] in, represents the ion flux, is the diffusion coefficient, represents the concentration gradient. According to the law of conservation of mass, Fick's second law is obtained:

[0021] This equation describes the diffusion process of lithium ions in the electrode and separator, and needs to be solved in combination with boundary conditions and initial conditions to obtain the electrochemical reaction characteristic matrix of the battery. At the same time, at the electrode interface, the charge transfer process is described by the Butler-Volmer equation:

[0022] in, is the current density, is the exchange current density, is the electron transfer coefficient, is Faraday's constant, is the overpotential, is the gas constant, is the temperature. By solving this equation, the current distribution on the electrode surface is obtained, and the internal potential distribution of the battery is calculated to form a complete electrochemical reaction characteristic matrix. After constructing the electrochemical reaction characteristic matrix, the three-dimensional non-steady-state heat conduction equation of the battery is established to describe the temperature distribution of the battery during the charging and discharging process. The heat sources of the battery mainly include ohmic heat, polarization heat and side reaction heat, which will be conducted and diffused inside the battery and affect its thermal stability. Assume that the temperature distribution inside the battery is , then its evolution is described by the three-dimensional unsteady heat conduction equation:

[0023] in, is the material density, is the specific heat capacity, is the thermal conductivity, is the heat source term per unit volume. In order to relate the electrochemical reaction and heat conduction, the heat source term is established The relationship between the current density and the potential distribution. The heat source term is expressed as:

[0024] in, is the current, is the ohmic internal resistance of the battery, is the interfacial current density, is the overpotential of the electrode. By establishing the mapping function of the heat source term, the thermal conductivity characteristic matrix of the battery is constructed to simulate the temperature distribution of the battery under different working conditions. Based on the thermal conductivity characteristic matrix, the volume change of the electrode material during the charge and discharge process is calculated, and the stress distribution function is established to describe the mechanical deformation of the battery during the cycle. Since the insertion and extraction of lithium ions will cause the volume expansion and contraction of the electrode material, the stress distribution is solved by the thermal-mechanical coupling equation. Assume that the strain field of the electrode is , the stress field is , then the relationship is described by the linear elastic constitutive equation:

[0025] in, is Young's modulus, It is expressed as:

[0026] in, is the coefficient of thermal expansion, is the volume expansion coefficient caused by changes in lithium ion concentration, is the temperature change, is the change in lithium ion concentration. By solving the stress equation, the mechanical stress characteristic matrix of the battery is obtained, and the structural damage risk of the battery is predicted. The electrochemical reaction characteristic matrix, thermal conductivity characteristic matrix and mechanical stress characteristic matrix are input into the multi-physics field simulation solver for coupling calculation, and a complete battery multi-dimensional coupling theory matrix is ​​obtained. In the simulation solution process, the finite volume method or finite element method is used to discretize the calculation domain, and the numerical solution method is used for iterative calculation to obtain the steady-state or transient battery multi-dimensional field distribution. Through this method, the internal state of the battery at different charge and discharge rates can be accurately simulated.

[0027] In one example, a temperature sensor and a strain gauge are set on the surface of a golf cart battery and real-time data synchronous acquisition is performed to obtain multi-dimensional measured data of the battery, and a battery electrochemical impedance spectrum test is performed to obtain a battery impedance characteristic vector, including: The surface of the golf cart battery is divided into equally spaced grids, and M temperature sensors are evenly arranged on the divided grid points. At the same time, the stress concentration area of ​​the golf cart battery shell is subjected to force analysis, and N strain gauges are arranged at the target structural position; Conditioning the output signals of the temperature sensor and the strain gauge to obtain standardized analog signals, and digitally converting the standardized analog signals to obtain digital measurement data; Based on the digital measurement data, the sampling timing of the temperature sensor and the strain gauge is synchronously controlled to obtain multi-dimensional measured data of the battery; According to the battery multi-dimensional coupling theory matrix, the state of charge test points are selected and the battery electrochemical impedance spectrum test is performed to obtain the battery impedance characteristic vector.

[0028] In this example, the battery surface is meshed with equal spacing to ensure uniform distribution of measurement points and obtain temperature and stress data with high spatial resolution. Based on the battery geometry and thermodynamic properties, the meshing technology in the finite element method is used to divide the battery surface into multiple regular or irregular units. The center point of each unit is the installation location of the sensor. Assume that the length of the battery surface is , the width is , then divide it into grids, each with a size of and , thus ensuring that the temperature sensors are evenly distributed in the center of the grid. At the same time, in order to accurately measure the stress of the battery casing during operation, the stress concentration area of ​​the battery casing is analyzed to determine the optimal arrangement position of the strain gauge. The stress concentration area is identified by using the finite element analysis method. By establishing a mechanical model of the battery casing and applying boundary conditions under actual operating conditions, such as fixed support of the battery, external vibration, and internal thermal expansion, the stress distribution of the battery casing is calculated. Assume that the material of the battery casing has an elastic modulus , Poisson's ratio , and is subjected to external load in a certain direction , then its stress distribution is solved by the elastic mechanics equation:

[0029] in, is the stress tensor, is the material stiffness tensor, is the strain tensor. By numerically solving this equation, the stress distribution of different parts of the battery casing is obtained, and the stress concentration area is arranged A strain gauge is used to monitor the mechanical stress changes of the battery. After the temperature sensor and strain gauge are arranged, their output signals are conditioned to remove noise and convert them into standardized analog signals. The original signals of the temperature sensor and strain gauge are usually analog signals, containing high-frequency noise and drift, and are filtered and amplified. Assume that the output signal of the temperature sensor is , which is related to temperature The relationship is expressed as:

[0030] in, is the sensitivity of the sensor, is the bias voltage. In order to remove noise, a low-pass filter is used, such as a first-order RC filter, whose transfer function is expressed as:

[0031] in, and are the resistance and capacitance parameters of the filter respectively. After filtering, the signal amplitude is adjusted through the amplification circuit to ensure that it is within the appropriate range. After completing the signal conditioning, the standardized analog signal is digitized to obtain digital measurement data. The digital conversion adopts analog-to-digital conversion technology, and its core formula is:

[0032] in, For digitized data, is the input voltage, is the reference voltage, is the resolution of the ADC (such as 12 bits or 16 bits). After completing the ADC conversion, the digital measurement data of temperature and stress are obtained. The sampling timing of the sensor is synchronously controlled based on the digital measurement data. Due to the different rates of change of temperature and stress, a synchronous sampling strategy is adopted, that is, a unified sampling frequency is set. , and read the data of all sensors simultaneously in each sampling cycle. Assume that the temperature data at a certain moment is , the stress data is , then after synchronous sampling, construct the battery multi-dimensional measured data matrix:

[0033] This matrix is ​​used to describe the temperature and stress state of the battery at different times. According to the battery multi-dimensional coupling theory matrix, the state of charge test points are selected, and the battery electrochemical impedance spectroscopy (EIS) test is performed to obtain the impedance characteristic vector of the battery. The EIS test applies a small amplitude AC signal to the battery at different frequencies, measures its impedance response, and uses an equivalent circuit model for analysis. Assume that the equivalent circuit model of the battery consists of an ohmic resistor , charge transfer resistance , double layer capacitance and Warburg impedance The impedance expression is:

[0034] in, is the angular frequency, is an imaginary unit, It is mainly used to describe the ion diffusion behavior inside the battery. By measuring the impedance data at different frequencies and using the nonlinear least squares method for parameter fitting, the impedance characteristic vector of the battery is extracted. , used to evaluate the electrochemical performance of batteries.

[0035] In one example, a state of charge test point is selected according to the battery multi-dimensional coupling theory matrix and a battery electrochemical impedance spectrum test is performed to obtain a battery impedance characteristic vector, including: The test condition is optimized according to the electrochemical reaction characteristic data in the battery multi-dimensional coupling theory matrix, the state of charge test points are selected, and an AC excitation signal is set for each state of charge test point to obtain a test condition parameter set; Based on the test condition parameter set, an AC excitation signal is applied to the golf cart battery to perform an electrochemical impedance spectrum scan to obtain impedance response data; Based on the impedance response data, an equivalent circuit model including series resistance, charge transfer resistance, double layer capacitance and Warburg impedance is constructed; The impedance response data is input into a nonlinear least squares fitting algorithm, and parameter identification calculation is performed on the equivalent circuit model to obtain an equivalent circuit parameter set; The frequency domain characteristic analysis of the equivalent circuit parameter set is performed to extract the battery impedance characteristic vector including the ohmic impedance in the high frequency region, the charge transfer impedance in the medium frequency region and the concentration polarization impedance in the low frequency region.

[0036] In this example, the test conditions are optimized based on the electrochemical reaction persistence data in the battery multi-dimensional coupling theory matrix to ensure that the test accurately reflects the internal state of the battery. The electrochemical behavior of the battery is affected by factors such as the state of charge (SOC), temperature, and charge and discharge rate. When optimizing the test conditions, these factors are comprehensively considered, and appropriate state of charge test points are selected by analyzing the equivalent circuit characteristics of the battery. Assume that the battery's state of charge range is In order to fully cover the operating characteristics of the battery, select one or more test points in the low, medium and high SOC regions. The test point is selected based on the open circuit voltage of the battery. The relationship between the state of charge and the battery's open circuit voltage is measured And combined with the known SOC curve, the test point is accurately selected. After the test point is determined, an AC excitation signal is set for each state of charge test point to stimulate the electrochemical reaction of the battery within different frequency ranges and obtain the test condition parameter set. The AC excitation signal is a small-amplitude sinusoidal current signal, and its mathematical expression is:

[0037] in, is the applied alternating current, is the amplitude of the excitation current, is the angular frequency, is the frequency of the excitation signal. The EIS test covers multiple frequency points from millihertz (mHz) to kilohertz (kHz) to fully characterize the impedance characteristics of the battery. Set multiple frequency points Scan to form a complete set of test condition parameters:

[0038] Based on the test condition parameter set, an AC excitation signal is applied to the golf cart battery, and an electrochemical impedance spectroscopy (EIS) scan is performed to obtain the impedance response data of the battery. The principle of the EIS test is to apply a small AC signal to the battery, measure the voltage response of the battery, and then calculate its impedance. :

[0039] in, is the complex impedance, and The battery frequency is The data of EIS test are shown in Nyquist plot (real part Imaginary part ) or Bode plot (frequency versus impedance modulus and phase angle) to reflect the internal dynamic characteristics of the battery. After obtaining the impedance response data, an equivalent circuit model is constructed to analyze the electrochemical characteristics of the battery. The equivalent circuit model of a lithium-ion battery consists of multiple resistors, capacitors, and impedance elements to describe the ohmic loss, charge transfer process, double layer effect, and ion diffusion behavior inside the battery. A typical equivalent circuit model includes: series resistance , which represents the body resistance of the battery and the resistance of the current collector and electrolyte; charge transfer resistance , used to characterize the electrochemical reaction impedance at the electrode-electrolyte interface; double layer capacitance , describing the charge accumulation effect on the electrode surface; Warburg impedance , describing the ion diffusion behavior inside the battery. The total impedance expression of the equivalent circuit is written as:

[0040] in, is an imaginary unit, is the angular frequency. Warburg impedance It is expressed as:

[0041] in, is the Warburg coefficient, which characterizes the ion diffusion rate. In order to extract the equivalent circuit parameters from the experimental data, the impedance response data is input into the nonlinear least squares fitting algorithm to optimize the values ​​of the circuit parameters. The goal of the nonlinear least squares method is to minimize the experimentally measured impedance value. Impedance value calculated with equivalent circuit model The error between:

[0042] In this optimization process, the Levenberg-Marquardt algorithm (LM algorithm) is used for iterative solution to quickly converge to the optimal parameter value. After optimization, a complete equivalent circuit parameter set is obtained:

[0043] After obtaining the equivalent circuit parameters, frequency domain characteristic analysis is performed to extract the key impedance characteristic vector of the battery. The impedance characteristics of the battery are divided into high frequency region, medium frequency region and low frequency region, among which: the impedance of the high frequency region is mainly composed of ohmic impedance Dominant, reflecting the internal resistance of the battery and the current collection efficiency:

[0044] The impedance in the mid-frequency region is mainly composed of charge transfer impedance Decision, reflecting the charge transfer kinetics at the electrode-electrolyte interface:

[0045] The impedance in the low-frequency region is mainly composed of concentration polarization impedance Dominant, describing the ion diffusion and polarization behavior inside the battery:

[0046] By extracting the high frequency ohmic impedance , medium frequency charge transfer impedance , low frequency concentration polarization impedance , obtain the complete battery impedance characteristic vector:

[0047] In one example, based on the multi-dimensional measured data of the battery and the multi-dimensional coupling theoretical matrix of the battery, the battery volume is discretized into control volume units and the thermal-mechanical coupling iterative solution is performed to obtain the battery thermal-mechanical coupling characteristic matrix, including: The battery volume is divided into three-dimensional grids according to the multi-dimensional measured data of the battery, the battery structure is discretized into control volume units, and a unique identification number is assigned to each control volume unit to obtain a battery grid model; The initial field distribution of heat conduction is established based on the battery multi-dimensional coupling theory matrix, and the temperature sensor measurement data is mapped to the battery grid model through the three-dimensional spline interpolation algorithm to obtain the initial value of the temperature field; The boundary conditions of the initial value of the temperature field are processed and the thermal-mechanical coupling relationship is analyzed to obtain the thermal system equation; The thermodynamic system equations are input into the finite volume method solver, and the temperature field and stress field are calculated alternately and iteratively. The convergence criterion is set as the relative error of the temperature field in adjacent iteration steps is less than the target value, and the thermomechanical coupling distribution is obtained. Based on the thermal-mechanical coupling distribution, the temperature gradient stress analysis is performed, the thermal stress and strain of each control volume unit are calculated, and a heat source correction model under the influence of stress is established to obtain the corrected thermal-mechanical distribution; The temperature field data and stress field data of each control volume unit in the corrected thermal distribution are combined and mapped to obtain the battery thermal coupling characteristic matrix.

[0048] In this example, the battery volume is meshed in three dimensions based on multi-dimensional measured data to discretize the continuous physical domain and make it suitable for numerical calculations. A non-uniform meshing method is used, that is, a finer mesh is used in areas with temperature gradients and stress concentrations, and a coarser mesh is used in areas with gentle temperature and stress changes. Assume that the three-dimensional geometric dimensions of the battery are , which is divided into control volume units, the volume of each unit is:

[0049] in, Along The number of grids in the direction. Each control volume unit is assigned a unique identification number to ensure the traceability of data storage and calculation and form a complete battery grid model. The initial field distribution of heat conduction is established based on the battery multi-dimensional coupling theory matrix. Since the temperature data is measured by a limited number of sensors, the temperature measurement data is mapped to the entire grid model through a three-dimensional spline interpolation algorithm to obtain the initial value of the temperature field. Assume that The temperature value measured at each sensor location is , the corresponding coordinates are , then the three-dimensional spline interpolation function It is expressed as:

[0050] in, is the three-dimensional spline basis function, is the interpolation coefficient. By solving the interpolation equation group, the interpolation coefficient is determined and the initial distribution of the temperature field inside the entire battery is obtained. The boundary conditions of the initial value of the temperature field are processed and the thermal-mechanical coupling relationship is analyzed to establish a complete thermal system equation. The thermal conduction behavior of the battery is described by the three-dimensional non-steady-state heat conduction equation:

[0051] in, is the density of the battery material, is the specific heat capacity, is the thermal conductivity along different directions, is the heat source term. It is mainly composed of ohmic heating, polarization heat and side reaction heat, which can be expressed as:

[0052] in, is the current, is the ohmic internal resistance, is the current density, is the overpotential of the electrode. At the same time, temperature changes will cause the material to expand and induce internal stress, and this process is described by the thermal stress equation:

[0053] in, is the stress tensor, is the material stiffness tensor, is the strain tensor, which includes the thermal expansion term:

[0054] in, is the coefficient of thermal expansion, is the expansion coefficient caused by the change of lithium ion concentration, is the temperature change, is the change in lithium ion concentration. By solving the coupled system of heat conduction equation and stress balance equation, the complete thermodynamic system equation is obtained. The thermodynamic system equation is input into the finite volume method solver to perform alternating iterative calculations of temperature field and stress field and ensure the convergence of the calculation. The finite volume method discretizes the partial differential equation into algebraic equations by performing energy conservation analysis on the control volume unit. For any control volume unit, the discretized form of the heat conduction equation is expressed as:

[0055] in, and represent the temperature of the current and next time step respectively, is the time step, is the area of ​​the mesh surface, is the thermal conductivity at the mesh surface, is the internal heat source term of the unit. During iterative calculation, the convergence criterion is set as the relative error of the temperature field in adjacent iteration steps is less than the target value, that is:

[0056] in, is the convergence threshold. When all control volume units meet this condition, the iteration is terminated and the final thermal-mechanical coupling distribution is obtained. Based on the thermal-mechanical coupling distribution, the temperature gradient stress analysis is performed to calculate the thermal stress and strain of each control volume unit and establish a heat source correction model under the influence of stress. The heat conduction equation is corrected based on the stress field. Assume is the effective thermal conductivity after stress correction, and its calculation formula is:

[0057] in, is the initial thermal conductivity, is the material characteristic parameter, is the local stress. The final corrected thermal distribution is obtained by iteratively calculating the corrected heat conduction equation. The temperature field data and stress field data in the corrected thermal distribution need to be combined and mapped to generate a complete battery thermal coupling characteristic matrix.

[0058] In one example, the thermal system equations are input into the finite volume method solver, and the temperature field and stress field are alternately iterated. The convergence criterion is set as the relative error of the temperature field in adjacent iteration steps is less than the target value, and the thermal-mechanical coupling distribution is obtained, including: The control volume discretization process is performed on the thermodynamic system equations, and a discrete algebraic equation group including the temperature field thermal conductivity matrix and the stress field stiffness matrix is ​​established to obtain the control volume discretization equations; Based on the control volume discrete equation, the coupling matrix of momentum equation and energy equation is constructed, and the boundary conditions of temperature field and stress field are processed to obtain the coupling solution matrix; The coupled solution matrix is ​​input into the SIMPLE algorithm iterative solver to calculate the thermal stress increment caused by the temperature field and obtain the temperature stress correction; According to the temperature stress correction amount, the temperature field distribution of the control volume unit is corrected and calculated, the velocity-pressure coupling relationship between the stress field and the temperature field is established, and the correction coefficient matrix is ​​obtained; The relative error of the temperature field in adjacent iteration steps is calculated based on the correction coefficient matrix, and the relative error is compared with the set target value to obtain the iteration termination criterion; The temperature field and stress field data that meet the iteration termination criteria are post-processed, and the temperature distribution and stress distribution data of each control volume unit are extracted to obtain the thermal-mechanical coupling distribution.

[0059] In this example, the control volume discretization is performed on the thermal system equations to accurately simulate the temperature and stress distribution inside the battery in numerical calculations. The thermal system equations consist of a three-dimensional unsteady heat conduction equation and an elastic mechanics equation. The heat conduction equation is used to describe the temperature change inside the battery, while the mechanics equation is used to describe the stress distribution caused by the temperature change. Assume that the temperature field of the battery Satisfies the three-dimensional heat conduction equation:

[0060] in, is the material density, is the specific heat capacity, The battery is The thermal conductivity in the direction, is the internal heat source term, which mainly includes ohmic heat, polarization heat and side reaction heat:

[0061] in, is the current, is the ohmic resistance of the battery, is the current density, is the overpotential of the electrode. In order to discretize, the calculation area is divided into multiple control volume units, and the volume of each unit is , and calculate the temperature value at the center point of each unit. Apply the finite volume method, integrate the heat conduction equation on the control volume and use discrete approximation to obtain the discrete equation of the temperature field:

[0062] in, and represent the temperature of the current and next time step respectively, is the mesh surface area, is the thermal conductivity on the mesh surface, is the heat source term in the control volume unit. At the same time, the mechanical equation describes the stress change caused by thermal expansion, assuming that the stress tensor Satisfies the linear elastic constitutive relation:

[0063] in, is the material stiffness tensor, is the strain tensor, given by:

[0064] in, is the coefficient of thermal expansion, is the expansion coefficient caused by the change of lithium ion concentration, is the temperature change, is the change in lithium ion concentration. By discretizing the mechanical equations, the discrete equations of the stress field are obtained, forming a set of discrete algebraic equations including the thermal conductivity matrix of the temperature field and the stiffness matrix of the stress field. Based on the control volume discrete equation, the coupling matrix of the momentum equation and the energy equation is constructed, and the boundary conditions of the temperature field and the stress field are processed to ensure the stability and accuracy of the calculation. The boundary conditions include convection heat transfer boundaries, fixed constraint boundaries, and free expansion boundaries. In the heat conduction equation, the boundary heat flux satisfies:

[0065] in, is the convective heat transfer coefficient, is the ambient temperature, and in the mechanical equation, the boundary force satisfies:

[0066] in, is the boundary normal vector, is the externally applied load. After boundary condition processing, the coupling solution matrix is ​​obtained. The coupling solution matrix is ​​input into the SIMPLE algorithm iterative solver to calculate the thermal stress increment caused by the temperature field and obtain the temperature stress correction. The SIMPLE algorithm adjusts the flow field through the pressure correction equation so that it converges to a physically reasonable state. The SIMPLE algorithm is used to solve the temperature field and stress field at the same time, so that the iterative process of the two can converge stably. The temperature field distribution of the control volume unit is corrected and calculated according to the temperature stress correction, and the velocity-pressure coupling relationship between the stress field and the temperature field is established. Since the temperature gradient affects the expansion behavior of the material and thus changes the local heat conduction characteristics, a correction coefficient matrix is ​​introduced to keep the solution process of the temperature field and the stress field consistent. Assumptions is the corrected effective thermal conductivity, and its calculation formula is:

[0067] in, is the initial thermal conductivity, is the material characteristic parameter, is the local stress. Through this correction model, the physical consistency of the thermal-mechanical coupling solution is ensured. The relative error of the temperature field in adjacent iteration steps is calculated based on the correction coefficient matrix, and it is compared with the set target value to determine whether the iteration termination criterion is met. The method for calculating the relative error is as follows:

[0068] When the relative error of all control volume elements Both are less than the preset threshold When , the iteration is terminated and the final thermomechanical coupling distribution is obtained. After the iteration termination criterion is met, the calculated temperature field and stress field data are post-processed to extract the temperature distribution and stress distribution data of each control volume unit and generate a complete thermomechanical coupling distribution. Post-processing includes data visualization, contour analysis, and stress concentration area identification. For example, in practical applications, if the temperature gradient in a certain area exceeds the design threshold, or the stress concentration factor is high, the battery structure is adjusted or the cooling strategy is optimized to reduce the risk of degradation in long-term use.

[0069] In one example, the battery performance evaluation is performed based on the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the multi-dimensional measured data of the battery to obtain the battery performance evaluation results, including: The battery impedance characteristic vector, battery thermal-mechanical coupling characteristic matrix and multi-dimensional measured data of the battery are input into the dual encoder network for feature weighting to obtain the working condition feature representation; The InfoNCE contrast loss function is constructed based on the working condition feature representation, and the dual encoder network is iteratively optimized based on the batch normalization layer and the Adam optimizer to obtain working condition-independent features; The working condition-independent features are input into the discriminator network, and the working condition prediction accuracy is minimized through adversarial training. A weighted combination of the working condition classification cross entropy loss function and the feature reconstruction loss function is constructed for gradient update to obtain the performance feature vector. The KLD loss function is calculated based on the performance feature vector, and the feature distribution is constrained within the normal distribution space. The normalized battery feature set is obtained by minimizing the relative entropy between the feature distribution and the target distribution. The performance indicators of the normalized battery feature set are calculated to obtain the capacity attenuation rate, internal resistance growth rate, temperature consistency coefficient and stress concentration coefficient; The capacity attenuation rate, internal resistance growth rate, temperature consistency coefficient and stress concentration coefficient are input into the degradation mechanism analysis model for time series feature mapping and performance degradation early warning to obtain the battery performance evaluation results.

[0070] In this example, the battery impedance characteristic vector, battery thermal coupling characteristic matrix and battery multi-dimensional measured data are input into the dual encoder network to perform feature weighting and obtain the operating condition feature representation. Assume that the battery impedance characteristic vector is , which contains the high frequency ohmic impedance , medium frequency charge transfer impedance and low frequency concentration polarization impedance ,Right now ; Assume that the thermal coupling characteristic matrix of the battery is , which is used to characterize the temperature and stress distribution, namely ,in is the temperature distribution, is the stress distribution; suppose the multi-dimensional measured data of the battery is , which contains information such as current, voltage and ambient temperature, that is, The dual encoder network consists of two independent encoders, which are used to process impedance and thermal coupling characteristics, as well as multi-dimensional measured data. Assume that the encoder parameters are and , then the encoded feature is expressed as:

[0071] Perform weighted fusion on the encoded features to obtain the final working condition feature representation :

[0072] in, and is a trainable feature weighting coefficient to ensure that the contributions of different data sources are reasonably distributed. The InfoNCE contrast loss function is constructed based on the working condition feature representation to optimize the dual encoder network and obtain working condition-independent features. The goal of the InfoNCE loss is to maximize the similarity of positive samples and minimize the similarity of negative samples to ensure that the features learned by the model can remain stable under different working conditions. Assume that the positive sample is , the negative sample is , then the InfoNCE loss function is defined as:

[0073] in, represents the cosine similarity, is the total number of negative samples. In order to improve the training stability, the batch normalization layer is used to normalize the features during the training process, and the Adam optimizer is used for gradient optimization so that the model can converge quickly. The operating condition-independent features are input into the discriminator network to minimize the operating condition prediction accuracy through adversarial training and extract the core performance feature vector of the battery. The discriminator predicts the operating condition category of the battery based on the input features, while the goal of the dual encoder network is to generate operating condition-independent features that are difficult to be correctly classified. This adversarial training mechanism makes the model pay more attention to the physical properties of the battery itself rather than information related to the operating condition. The loss function of the discriminator is composed of the operating condition classification cross entropy loss and feature reconstruction loss The mathematical expression is:

[0074]

[0075] in, is the number of working condition categories, is the actual working condition label, is the predicted probability, is the reconstructed feature representation. The final adversarial loss function is a weighted combination of the two:

[0076] in, and is the weight coefficient. Through adversarial training, the performance feature vector is obtained :

[0077] in, is the mapping function of the discriminator network. Based on the performance feature vector, the KLD loss is calculated to constrain the feature distribution within the normal distribution space to ensure that the features are consistent under different batteries and working conditions. The KLD loss function is used to measure the two probability distributions. and The relative entropy between is expressed as:

[0078] in, is the distribution of the current feature, is the target normal distribution. By minimizing the KLD loss, the final battery characteristics are ensured to meet the standardization requirements and a standardized battery feature set is obtained. The performance index of the standardized battery feature set is calculated to extract key health status parameters. Among them, the capacity decay rate By comparing the current capacity With initial capacity The calculation results are:

[0079] Internal resistance growth rate By measuring the current internal resistance With initial internal resistance The change in is calculated as:

[0080] Temperature Coefficient of Conformity Characterized by calculating the standard deviation of the temperature distribution inside the battery:

[0081] in, is the average temperature of the battery. Stress concentration factor By calculating the maximum gradient of the stress field, we get:

[0082] These performance indicators are input into the degradation mechanism analysis model for time series feature mapping and performance degradation warning. The degradation mechanism analysis model uses a long short-term memory network (LSTM) or a temporal convolutional network (TCN) to learn the time series trend of battery performance and predict future health status. Assuming that the performance of the battery changes over time The following recurrence relationship is satisfied:

[0083] in, is the performance change at the current moment. By training the model, the future battery performance degradation trend can be predicted and an early warning can be issued before the battery reaches a certain safety threshold.

[0084] Reference Figure 2 , this embodiment provides a multi-dimensional testing device for a golf cart battery, comprising: Data acquisition unit 1 is used to collect standard operating data of the golf cart battery, build an electrochemical-thermal-mechanical coupling theoretical model, and obtain a multi-dimensional coupling theoretical matrix of the battery; Test unit 2 is used to set a temperature sensor and a strain gauge on the surface of the golf cart battery and perform real-time data synchronous acquisition to obtain multi-dimensional measured data of the battery, and perform a battery electrochemical impedance spectrum test to obtain a battery impedance characteristic vector; Iterative solution unit 3 is used to discretize the battery volume into control volume units and perform thermal-mechanical coupling iterative solution according to the multi-dimensional measured data of the battery and the multi-dimensional coupling theoretical matrix of the battery to obtain the thermal-mechanical coupling characteristic matrix of the battery; The performance evaluation unit 4 is used to evaluate the battery performance based on the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the multi-dimensional measured data of the battery to obtain the battery performance evaluation result.

[0085] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0086] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0087] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-dimensional testing method for golf cart batteries, characterized in that: The following steps are involved: Collect data of golf cart battery under standard working conditions, build electrochemical-thermal-mechanical coupling theoretical model, and obtain multi-dimensional coupling theoretical matrix of battery; A temperature sensor and a strain gauge are arranged on the surface of the golf cart battery to perform real-time data synchronous acquisition to obtain multi-dimensional measured data of the battery, and an electrochemical impedance spectrum test of the battery is performed to obtain a battery impedance characteristic vector; According to the multi-dimensional measured data of the battery and the multi-dimensional coupling theoretical matrix of the battery, the battery volume is discretized into control volume units and thermal-mechanical coupling iterative solution is performed to obtain the battery thermal-mechanical coupling characteristic matrix; The battery performance is evaluated based on the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the multi-dimensional measured data of the battery to obtain a battery performance evaluation result.

2. The multi-dimensional testing method for golf cart batteries according to claim 1, characterized in that: The standard operating data of the golf cart battery is collected, and an electrochemical-thermal-mechanical coupling theoretical model is constructed to obtain a battery multi-dimensional coupling theoretical matrix, including: Collecting voltage, current and temperature distribution data of a golf cart battery under 0.5C, 1C and 2C rate charge and discharge conditions to obtain a battery operation raw data set, and performing data preprocessing on the battery operation raw data set to obtain a battery standardized data set; Based on the battery standardized data set, the battery structure is divided into five layers: negative electrode current collector, negative electrode, separator, positive electrode, and positive electrode current collector, and the ion transport equation of each layer is established to obtain the electrochemical reaction characteristic matrix; A three-dimensional non-steady-state heat conduction equation is established according to the electrochemical reaction characteristic matrix, the golf cart battery is meshed, a mapping function between a heat source term and an electrochemical reaction is established, and a heat conduction characteristic matrix is ​​obtained; Based on the thermal conductivity characteristic matrix, the volume change of the electrode material during the charge and discharge process is calculated, and a stress distribution function is established to obtain a mechanical stress characteristic matrix; The electrochemical reaction characteristic matrix, the heat conduction characteristic matrix and the mechanical stress characteristic matrix are input into a multi-physics field simulation solver for coupling calculation to obtain a battery multi-dimensional coupling theoretical matrix.

3. The multi-dimensional testing method of golf cart batteries according to claim 1, characterized in that: The temperature sensor and strain gauge are arranged on the surface of the golf cart battery and real-time data synchronous acquisition is performed to obtain multi-dimensional measured data of the battery, and the battery electrochemical impedance spectrum test is performed to obtain the battery impedance characteristic vector, including: Divide the surface of the golf cart battery into equally spaced grids, evenly arrange M temperature sensors on the divided grid points, and at the same time, perform a force analysis on the stress concentration area of ​​the golf cart battery housing, and arrange N strain gauges at the target structural position; Conditioning the output signals of the temperature sensor and the strain gauge to obtain standardized analog signals, and digitally converting the standardized analog signals to obtain digital measurement data; Based on the digital measurement data, synchronously control the sampling timing of the temperature sensor and the strain gauge to obtain multi-dimensional measured data of the battery; According to the battery multi-dimensional coupling theory matrix, a state of charge test point is selected and a battery electrochemical impedance spectrum test is performed to obtain a battery impedance characteristic vector.

4. The multi-dimensional testing method for golf cart batteries according to claim 3, characterized in that: The step of selecting a state of charge test point according to the battery multi-dimensional coupling theory matrix and performing a battery electrochemical impedance spectrum test to obtain a battery impedance characteristic vector includes: Optimizing the test conditions according to the electrochemical reaction characteristic data in the multi-dimensional coupling theory matrix of the battery, selecting the state of charge test points, and setting an AC excitation signal for each state of charge test point to obtain a test condition parameter set; Based on the test condition parameter set, applying an AC excitation signal to the golf cart battery to perform an electrochemical impedance spectrum scan to obtain impedance response data; According to the impedance response data, an equivalent circuit model including series resistance, charge transfer resistance, double layer capacitance and Warburg impedance is constructed; Inputting the impedance response data into a nonlinear least squares fitting algorithm, performing parameter identification calculation on the equivalent circuit model, and obtaining an equivalent circuit parameter set; The frequency domain characteristic analysis is performed on the equivalent circuit parameter set to extract the battery impedance characteristic vector including the ohmic impedance in the high frequency region, the charge transfer impedance in the medium frequency region and the concentration polarization impedance in the low frequency region.

5. The multi-dimensional testing method for golf cart batteries according to claim 1, characterized in that: According to the multi-dimensional measured data of the battery and the multi-dimensional coupling theoretical matrix of the battery, the battery volume is discretized into control volume units and thermal-mechanical coupling iterative solution is performed to obtain the battery thermal-mechanical coupling characteristic matrix, including: Dividing the battery volume into three-dimensional grids according to the multi-dimensional measured data of the battery, discretizing the battery structure into control volume units, and assigning a unique identification number to each control volume unit to obtain a battery grid model; Establishing the initial field distribution of heat conduction based on the multi-dimensional coupling theory matrix of the battery, and mapping the temperature sensor measurement data to the battery grid model through a three-dimensional spline interpolation algorithm to obtain the initial value of the temperature field; Performing boundary condition processing and thermal-mechanical coupling relationship analysis on the initial value of the temperature field to obtain a thermal system equation; Inputting the thermodynamic system equation into a finite volume method solver, performing alternating iterative calculations on the temperature field and the stress field, and setting a convergence criterion that the relative error of the temperature field in adjacent iteration steps is less than a target value, to obtain a thermomechanical coupling distribution; Based on the thermal-mechanical coupling distribution, a temperature gradient stress analysis is performed to calculate the thermal stress and strain of each control volume unit, and a heat source correction model under the influence of stress is established to obtain a corrected thermal-mechanical distribution; The temperature field data and stress field data of each control volume unit in the modified thermal distribution are combined and mapped to obtain a battery thermal coupling characteristic matrix.

6. The multi-dimensional testing method for golf cart batteries according to claim 5, characterized in that: The thermodynamic system equation is input into the finite volume method solver, the temperature field and the stress field are alternately iterated and calculated, and the convergence criterion is set as the relative error of the temperature field in adjacent iteration steps is less than the target value, and the thermomechanical coupling distribution is obtained, including: The control volume discretization process is performed on the thermodynamic system equations to establish a discrete algebraic equation group including a temperature field thermal conductivity matrix and a stress field stiffness matrix to obtain a control volume discretization equation; Based on the control volume discrete equation, a coupling matrix of the momentum equation and the energy equation is constructed, and the boundary conditions of the temperature field and the stress field are processed to obtain a coupling solution matrix; Inputting the coupling solution matrix into the SIMPLE algorithm iterative solver, calculating the thermal stress increment caused by the temperature field, and obtaining the temperature stress correction amount; According to the temperature stress correction amount, the temperature field distribution of the control volume unit is corrected and calculated, the velocity-pressure coupling relationship between the stress field and the temperature field is established, and a correction coefficient matrix is ​​obtained; Calculate the relative error of the temperature field in adjacent iteration steps based on the correction coefficient matrix, and compare the relative error with the set target value to obtain an iteration termination criterion; The temperature field and stress field data satisfying the iteration termination criterion are post-processed, and the temperature distribution and stress distribution data of each control volume unit are extracted to obtain the thermal-mechanical coupling distribution.

7. The multi-dimensional testing method for golf cart batteries according to claim 1, characterized in that: The battery performance evaluation is performed based on the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the multi-dimensional measured data of the battery to obtain a battery performance evaluation result, including: Inputting the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the multi-dimensional measured data of the battery into a dual encoder network for feature weighting to obtain a working condition feature representation; An InfoNCE contrast loss function is constructed according to the working condition feature representation, and the dual encoder network is iteratively optimized based on a batch normalization layer and an Adam optimizer to obtain working condition-independent features; The working condition-independent features are input into the discriminator network, the working condition prediction accuracy is minimized through adversarial training, a weighted combination of the working condition classification cross entropy loss function and the feature reconstruction loss function is constructed for gradient update, and a performance feature vector is obtained; Calculating the KLD loss function based on the performance feature vector, constraining the feature distribution within the normal distribution space, and obtaining a normalized battery feature set by minimizing the relative entropy between the feature distribution and the target distribution; Calculating performance indicators of the standardized battery feature set to obtain capacity attenuation rate, internal resistance growth rate, temperature consistency coefficient and stress concentration coefficient; The capacity attenuation rate, internal resistance growth rate, temperature consistency coefficient and stress concentration coefficient are input into the degradation mechanism analysis model for time series feature mapping and performance degradation early warning to obtain the battery performance evaluation result.

8. A multi-dimensional testing device for golf cart batteries, characterized in that: For implementing the steps of the method according to any one of claims 1 to 7, the device comprises: A data acquisition unit is used to collect data of golf cart batteries under standard operating conditions, build an electrochemical-thermal-mechanical coupling theoretical model, and obtain a multi-dimensional coupling theoretical matrix of the battery; A testing unit, used to set a temperature sensor and a strain gauge on the surface of the golf cart battery and perform real-time data synchronous acquisition to obtain multi-dimensional measured data of the battery, and perform a battery electrochemical impedance spectrum test to obtain a battery impedance characteristic vector; An iterative solution unit, used to discretize the battery volume into control volume units and perform thermal-mechanical coupling iterative solution according to the multi-dimensional measured data of the battery and the multi-dimensional coupling theoretical matrix of the battery, so as to obtain a battery thermal-mechanical coupling characteristic matrix; The performance evaluation unit is used to evaluate the battery performance based on the battery impedance characteristic vector, the battery thermal-mechanical coupling characteristic matrix and the multi-dimensional measured data of the battery to obtain a battery performance evaluation result.

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