Method and system for constructing equivalent circuit model of lithium ion battery and medium

Abstract

The invention relates to the technical field of battery model construction, in particular to a construction method and system of a lithium ion battery equivalent circuit model and a medium. The method comprises the following steps: collecting initial battery electrochemical parameters, obtaining ion migration characteristic data through charge density gradient decoupling, further reconstructing a polarization potential field to generate a lithium ion battery charge-discharge curve, performing potential migration analysis on the curve, mapping the curve into time domain charge mapping data, and calculating the time domain charge mapping data according to the time domain charge mapping data. Voltage distribution data are captured through transient voltage, circuit topology is reconstructed, circuit temperature distribution data are obtained, electric field-temperature field cross locking is carried out to determine a temperature influence coefficient, an initial equivalent circuit model is corrected based on the temperature influence coefficient, charging and discharging simulation is carried out on the corrected model, and aging factors are embedded. And finally generating an equivalent circuit model of the lithium ion battery. The method for constructing the equivalent circuit model of the lithium ion battery is more reliable and more intelligent.

Description

Technical Field

[0001] The present invention relates to the technical field of battery model construction, and particularly to a method, a system and a medium for constructing an equivalent circuit model of a lithium-ion battery. Background Art

[0002] As an important energy storage device for modern electronic devices and electric vehicles, the performance and lifespan of lithium-ion batteries directly affect the usage effect and safety of the devices. However, existing battery models often fail to accurately reflect the dynamic characteristics of the batteries under actual working conditions, resulting in the inability of battery management systems to make timely and effective adjustments during the charging and discharging processes, increasing the risk of battery failures. Researchers urgently need to develop more accurate equivalent circuit models to improve the accuracy and reliability of battery performance prediction. At the same time, existing research has insufficient consideration in aspects such as battery thermal management and aging behavior, and has not comprehensively analyzed the influence of temperature on battery performance. Especially in extreme working environments, the electrochemical behavior of the batteries changes significantly, and there is a lack of effective models to capture these changes, resulting in large errors in the performance evaluation and prediction of the batteries in practical applications, thereby affecting the safety and economy of the batteries. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, a system and a medium for constructing an equivalent circuit model of a lithium-ion battery to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing an equivalent circuit model of a lithium-ion battery includes the following steps:

[0005] Step S1: Collect initial battery electrochemical parameters; perform charge density gradient decoupling on the initial battery electrochemical parameters to obtain ion migration characteristic data; perform polarization potential field reconstruction on the ion migration characteristic data to generate a charge-discharge curve of the lithium-ion battery;

[0006] Step S2: Perform potential migration analysis on the charge-discharge curve of the lithium-ion battery to generate charge directional distribution data; perform impedance characteristic mapping on the charge directional distribution data to obtain time-domain charge mapping data;

[0007] Step S3: Perform transient voltage capture on the time-domain charge mapping data to obtain voltage distribution data; perform circuit topology reconstruction based on the voltage distribution data to generate initial equivalent circuit topology data;

[0008] Step S4: Obtain circuit temperature distribution data; perform multi-dimensional cross-locking of the electric field and temperature field on the initial equivalent circuit topology data based on the circuit temperature distribution data to obtain a temperature influence coefficient; perform thermoelectric joint correction modeling on the initial equivalent circuit topology data according to the temperature influence coefficient to generate a corrected equivalent circuit model;

[0009] Step S5: Perform charge and discharge simulation processing on the corrected equivalent circuit model to obtain simulation circuit operation data; embed aging factors into the corrected equivalent circuit model based on the simulation circuit operation data to generate an equivalent circuit model of a lithium-ion battery.

[0010] The present invention realizes the accurate evaluation of battery performance by collecting initial battery electrochemical parameters. The charge density gradient decoupling provides an in-depth understanding of ion migration characteristics. The charge and discharge curves generated by the polarization potential field reconstruction lay the foundation for battery behavior prediction. The charge directional distribution data generated by the potential migration analysis enhances the visualization of the internal process of the battery. The implementation of impedance feature mapping improves the accuracy of battery performance analysis. The transient voltage capture provides a dynamic perspective of voltage distribution. The completion of circuit topology reconstruction ensures the scientificity and practicality of the battery model. The acquisition of temperature distribution data improves the understanding of battery thermal management. The temperature influence coefficient determined by the electric field-temperature field cross-lock provides a reliable basis for model correction. The corrected equivalent circuit model generated by the thermoelectric joint correction modeling enhances the adaptability of the battery under different working conditions. The implementation of charge and discharge simulation processing provides data support for the practical application of battery performance. The embedding of aging factors improves the model's prediction ability for battery life and reliability, and overall promotes the scientific and intelligent development of the equivalent circuit model of lithium-ion batteries.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Collect initial battery electrochemical parameters; perform signal smoothing filtering on the initial battery electrochemical parameters to obtain an electrochemical filtering signal;

[0013] Step S12: Perform ion concentration stratification extraction on the electrochemical filtering signal to generate concentration distribution data; construct a charge density field for the concentration distribution data to obtain density field data;

[0014] Step S13: Perform gradient matrix decomposition on the density field data to obtain ion migration characteristic data;

[0015] Step S14: Perform polarization field tensor reconstruction on the ion migration characteristic data to generate field strength distribution data;

[0016] Step S15: Perform electrochemical response mapping on the initial battery electrochemical parameters according to the field strength distribution data to generate a charge and discharge curve of a lithium-ion battery.

[0017] The present invention ensures the accuracy and reliability of data through the step of collecting the initial electrochemical parameters of the battery. Signal smoothing filtering improves the quality of the electrochemical signal, reduces noise interference. The implementation of ion concentration layer extraction provides detailed concentration distribution data for subsequent analysis. The construction of the charge density field enhances the understanding of the internal ion distribution state of the battery. The application of gradient matrix decomposition effectively extracts ion migration characteristic data. The field strength distribution data generated by the reconstruction of the polarization field tensor provides an important basis for battery performance evaluation. The electrochemical response mapping realizes the accurate correlation between the initial electrochemical parameters of the battery and the charge-discharge curve, overall improving the scientificity and practicality of the construction of the equivalent circuit model of the lithium-ion battery, and promoting the efficient development and application of battery management technology.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: Perform potential migration stratification on the charge-discharge curve of the lithium-ion battery to obtain potential tomography matrix data; perform steady-state-perturbation dissection on the potential tomography matrix data to generate steady-state parameter mapping data;

[0020] Step S22: Perform charge transition locking on the steady-state parameter mapping data to generate charge directional distribution data;

[0021] Step S23: Perform non-linear parasitic impedance elimination on the charge directional distribution data to generate impedance purification data; construct a polarization lag matrix for the impedance purification data to obtain a polarization characteristic matrix;

[0022] Step S24: Perform frequency-divided charge coupling operation on the polarization characteristic matrix to obtain time-domain charge mapping data.

[0023] The present invention realizes a detailed analysis of the charge-discharge curve through the implementation of potential migration stratification. The acquisition of potential tomography matrix data provides a basis for subsequent steady-state and perturbation dissections. The generation of steady-state parameter mapping data enhances the understanding of the steady-state characteristics of the battery. The application of charge transition locking improves the visualization of charge distribution. The generation of charge directional distribution data provides important information for battery performance analysis. The elimination of non-linear parasitic impedance effectively improves the accuracy of impedance data. The impedance purification data lays a foundation for the analysis of polarization characteristics. The construction of the polarization lag matrix reveals the polarization behavior of the battery during charge and discharge. The implementation of frequency-divided charge coupling operation generates time-domain charge mapping data that provides strong support for the evaluation of the dynamic performance of the battery, overall improving the accuracy and scientificity of the construction of the equivalent circuit model of the lithium-ion battery, and promoting the intelligent and efficient development of the battery management system.

[0024] Preferably, step S23 includes the following steps:

[0025] Extract impedance components from the charge directional distribution data to obtain impedance component data; identify non-linear features from the impedance component data to generate non-linear feature data;

[0026] Based on the non-linear feature data, screen the parasitic impedance of the charge directional distribution data to generate parasitic impedance data; perform impedance purification processing on the parasitic impedance data to obtain impedance purification data;

[0027] Perform optional lag point detection on the impedance purification data to obtain candidate lag points; perform extreme lag screening on the candidate lag points to generate extreme lag points;

[0028] Calibrate the lag time for the extreme lag points to obtain time calibration data; calculate the lag amount for the time calibration data to generate lag amount data;

[0029] Based on the lag amount data, perform matrix construction to generate an extreme value feature matrix.

[0030] The present invention realizes in-depth analysis of the battery impedance characteristics through impedance component extraction of the charge directional distribution data. The application of non-linear feature recognition improves the understanding of the complex behavior of the battery, provides a basis for subsequent parasitic impedance screening. The generation of parasitic impedance data ensures the effective elimination of interference factors. The impedance purification processing enhances the accuracy and reliability of the data. The detection of optional lag points provides candidate data for lag characteristic analysis. The screening of extreme lag points ensures the scientific nature of the lag behavior. The implementation of lag time calibration provides a time reference for subsequent analysis. The data generated by the lag amount calculation provides important parameters for battery performance evaluation. The extreme value feature matrix formed by the matrix construction based on the lag amount data provides support for the precision of the battery performance model, overall improving the scientific nature and practicality of the construction of the equivalent circuit model of the lithium-ion battery, and promoting the innovation and progress of battery management technology.

[0031] Preferably, step S3 includes the following steps:

[0032] Step S31: Capture non-uniform charge and discharge segment data from the time-domain charge mapping data to obtain charge and discharge segment data; perform transient voltage cross-locking on the charge and discharge segment data to generate voltage distribution data;

[0033] Step S32: Perform impedance gradient hedging simulation on the voltage distribution data to generate an impedance hedging matrix; perform equivalent capacitance-resistance coupling extraction on the impedance hedging matrix to obtain capacitance impedance mapping data;

[0034] Step S33: Perform non-linear charge expansion fitting on the capacitance impedance mapping data to obtain equivalent circuit element parameter data;

[0035] Step S34: Perform topological mapping transformation on the equivalent circuit element parameter data based on the capacitance impedance mapping data to obtain the initial equivalent circuit topology data.

[0036] The present invention ensures a detailed analysis of the battery's dynamic behavior through the implementation of non-uniform charge and discharge segment capture. The generation of charge and discharge segment data provides a necessary basis for the study of transient voltages. The transient voltage cross-lock enhances the accuracy of voltage distribution data. The impedance gradient counterflush simulation enables an in-depth exploration of the battery's performance. The construction of the impedance counterflush matrix supports the subsequent coupled analysis of capacitance and resistance. The generation of capacitance impedance mapping data improves the understanding of the battery's equivalent circuit characteristics. The non-linear charge expansion fitting provides a scientific basis for the extraction of circuit element parameters. The implementation of topological mapping transformation ensures the accuracy and rationality of the initial equivalent circuit topology data, overall improving the accuracy and applicability of the construction of the lithium-ion battery equivalent circuit model and promoting the intelligent and efficient development of the battery management system.

[0037] Preferably, step S4 includes the following steps:

[0038] Step S41: Obtain the lithium-ion battery temperature data; perform circuit temperature distribution analysis on the lithium-ion battery temperature data to generate circuit temperature distribution data;

[0039] Step S42: Perform thermal coupling modal decomposition on the initial equivalent circuit topology data according to the circuit temperature distribution data to obtain a temperature distribution matrix; reorganize the thermal conduction path of the temperature distribution matrix to generate heat flow distribution data;

[0040] Step S43: Perform electric field-thermal field multi-dimensional cross-lock on the initial equivalent circuit topology data based on the heat flow distribution data to obtain a temperature influence coefficient; perform circuit dynamic correction on the initial equivalent circuit topology data according to the temperature influence coefficient to generate corrected equivalent circuit topology data;

[0041] Step S44: Reconstruct the circuit model of the corrected equivalent circuit topology data to generate a corrected equivalent circuit model.

[0042] The present invention ensures comprehensive monitoring of the battery thermal state by obtaining the temperature data of the lithium-ion battery. The data generated by the circuit temperature distribution analysis provides a basis for subsequent thermal coupling modal decomposition. The obtaining of the temperature distribution matrix enhances the understanding of the internal heat conduction characteristics of the battery. The implementation of the heat conduction path recombination enhances the accuracy of the heat flow distribution data. The application of the electric field-temperature field multi-dimensional cross-locking realizes the scientific calculation of the temperature influence coefficient. The execution of the circuit dynamic correction ensures the matching of the initial equivalent circuit topology data with the actual working state. The generation of the corrected equivalent circuit model improves the accuracy and reliability of the battery model, overall enhancing the scientificity and practicality of the construction of the lithium-ion battery equivalent circuit model and promoting the intelligent and efficient development of the battery management system.

[0043] Preferably, step S43 includes the following steps:

[0044] Perform time-domain decomposition on the heat flow distribution data to obtain time-series heat flow characteristics; construct a vector matrix based on the time-series heat flow characteristics to generate a heat flow characteristic matrix;

[0045] Recombine the heat conduction path based on the heat flow characteristic matrix to generate heat flow distribution data;

[0046] Perform electric field distribution analysis on the initial equivalent circuit topology data to obtain electric field distribution characteristics; analyze the corresponding relationship between the heat flow distribution data and the electric field distribution characteristics to generate a field domain mapping matrix;

[0047] Perform electric field-temperature field multi-dimensional cross-locking on the initial equivalent circuit topology data according to the field domain mapping matrix to obtain the temperature influence coefficient;

[0048] Perform circuit parameter mapping on the initial equivalent circuit topology data according to the temperature influence coefficient to obtain temperature influence parameters; update the topology structure of the initial equivalent circuit topology data based on the temperature influence parameters to generate corrected equivalent circuit topology data.

[0049] The present invention ensures that the dynamic characteristics of the heat flow distribution data are comprehensively captured through the implementation of domain decomposition. The generation of the time-series heat flow characteristics provides a basis for the analysis of heat flow behavior. The heat flow characteristic matrix formed by the vector matrix construction improves the efficiency of data processing. The application of the heat conduction path recombination enhances the understanding of the heat flow distribution. The execution of the electric field distribution analysis provides important information about the relationship between the heat flow and the electric field. The generation of the field domain mapping matrix lays a foundation for the interactive analysis between the electric field and the temperature field. The calculation of the temperature influence coefficient ensures the matching of the circuit model with the actual working state. The implementation of the circuit parameter mapping provides a necessary basis for circuit performance optimization. The execution of the topology structure update improves the accuracy and applicability of the corrected equivalent circuit topology data, overall enhancing the scientificity and practicality of the lithium-ion battery equivalent circuit model and promoting the progress and application of battery management technology.

[0050] Preferably, step S5 includes the following steps:

[0051] Step S51: Perform mesh generation on the modified equivalent circuit model to obtain mesh model data; discretize the mesh model data in the state space to generate state space data;

[0052] Step S52: Map the circuit parameters for the state space data to obtain circuit simulation data; perform model response simulation on the circuit simulation data based on the modified equivalent circuit model to obtain simulated circuit operation data;

[0053] Step S53: Extract the cyclic life characteristics from the simulated circuit operation data to obtain life characteristic data; trace the capacity attenuation path for the life characteristic data to generate attenuation trajectory data;

[0054] Step S54: Perform parameter attenuation mapping on the attenuation trajectory data to obtain the battery attenuation coefficient; modify the model parameters of the modified equivalent circuit model based on the battery attenuation coefficient to generate the equivalent circuit model of the lithium-ion battery.

[0055] Through the implementation of mesh generation, the present invention ensures that the spatial structure of the modified equivalent circuit model is refined. The generation of mesh model data provides a basis for subsequent numerical analysis. The application of state space discretization improves the accuracy and efficiency of data processing. The execution of circuit parameter mapping provides necessary support for simulation analysis. The generation of circuit simulation data enhances the understanding of circuit dynamic behavior. The implementation of model response simulation ensures the reliability of circuit operation data. The step of extracting cyclic life characteristics provides an important basis for evaluating the battery usage performance. The capacity attenuation path tracing realizes an in-depth analysis of the battery attenuation process. The generation of attenuation trajectory data provides an intuitive reference for battery performance optimization. The calculation of the battery attenuation coefficient ensures the scientific modification of model parameters. The generation of the equivalent circuit model of the lithium-ion battery improves the accuracy and reliability of battery performance prediction, and overall promotes the innovation and application development of lithium-ion battery management technology.

[0056] The present invention also provides a construction system for the equivalent circuit model of a lithium-ion battery, which is used to execute the method for constructing the equivalent circuit model of a lithium-ion battery as described above. The construction system for the equivalent circuit model of a lithium-ion battery includes:

[0057] An electrochemistry decoupling module, configured to collect initial battery electrochemistry parameters; perform charge density gradient decoupling on the initial battery electrochemistry parameters to obtain ion migration characteristic data; perform polarization potential field reconstruction on the ion migration characteristic data to generate the charge-discharge curve of the lithium-ion battery;

[0058] A potential mapping module, which is used to perform potential migration analysis on the charge and discharge curves of a lithium-ion battery to generate charge directional distribution data; perform impedance characteristic mapping on the charge directional distribution data to obtain time-domain charge mapping data;

[0059] A topology reconstruction module, which is used to perform transient voltage capture on the time-domain charge mapping data to obtain voltage distribution data; perform circuit topology reconstruction based on the voltage distribution data to generate initial equivalent circuit topology data;

[0060] A thermoelectric correction module, which is used to obtain circuit temperature distribution data; perform multi-dimensional cross-locking of the electric field and temperature field on the initial equivalent circuit topology data based on the circuit temperature distribution data to obtain a temperature influence coefficient; perform thermoelectric joint correction modeling on the initial equivalent circuit topology data according to the temperature influence coefficient to generate a corrected equivalent circuit model;

[0061] An aging simulation module, which is used to perform charge and discharge simulation processing on the corrected equivalent circuit model to obtain simulation circuit operation data; embed aging factors into the corrected equivalent circuit model based on the simulation circuit operation data to generate an equivalent circuit model of the lithium-ion battery.

[0062] Through the introduction of the electrochemistry decoupling module, the present invention realizes the efficient acquisition of the initial electrochemistry parameters of the lithium-ion battery. The acquisition of the ion migration characteristic data lays a foundation for the battery performance analysis. The charge and discharge curves generated by the polarization potential field reconstruction provide a dynamic perspective of the battery behavior. The application of the potential mapping module improves the analysis accuracy of the battery charge and discharge curves. The combination of the charge directional distribution data and the impedance characteristic mapping enhances the understanding of the internal distribution state of the battery. The topology reconstruction module ensures the scientificity and accuracy of the circuit topology through the analysis of the transient voltage capture and the voltage distribution data. The implementation of the thermoelectric correction module makes the determination of the temperature influence coefficient more accurate, providing a more reliable model basis for the performance of the battery under different working conditions. The application of the aging simulation module not only enhances the prediction ability of the model for the battery aging behavior, but also realizes dynamic update and optimization, overall improving the accuracy and applicability of the equivalent circuit model of the lithium-ion battery, and promoting the intelligent and efficient development of the battery management system.

[0063] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed, it implements the method for constructing the equivalent circuit model of the lithium-ion battery as described in any one of the above.

[0064] The present invention realizes the efficient storage and execution of the method for constructing an equivalent circuit model of a lithium-ion battery through the design of a computer-readable storage medium. The execution of the program ensures the collaborative work among various modules, improves the degree of automation of data processing. The integration of modules such as electrochemical decoupling, topology reconstruction, and thermoelectric correction enhances the comprehensiveness of battery performance analysis. The use of the storage medium reduces the complexity of manual operations and the occurrence of human errors. The programmatic implementation improves the repeatability and reliability of model construction. The ability of dynamic update and optimization enhances the real-time response to battery state changes. Overall, it improves the scientificity and practicality of the equivalent circuit model of lithium-ion batteries, and promotes the progress and application of battery management technology. Brief Description of the Drawings

[0065] Figure 1 It is a schematic diagram of the step flow of a method for constructing an equivalent circuit model of a lithium-ion battery;

[0066] Figure 2 It is a schematic diagram of the detailed implementation step flow of step S2;

[0067] Figure 3 It is a schematic diagram of the detailed implementation step flow of step S3.

[0068] The realization of the object of the present invention, functional features and advantages will be further described in conjunction with embodiments with reference to the drawings. Detailed Embodiments

[0069] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0070] In addition, the drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed related items.

[0072] To achieve the above object, please refer to Figures 1 to 3 , a method for constructing an equivalent circuit model of a lithium-ion battery, comprising the following steps:

[0073] Step S1: Collect initial battery electrochemical parameters; perform charge density gradient decoupling on the initial battery electrochemical parameters to obtain ion migration characteristic data; perform polarization potential field reconstruction on the ion migration characteristic data to generate a charge-discharge curve of the lithium-ion battery;

[0074] Step S2: Perform potential migration analysis on the charge-discharge curve of the lithium-ion battery to generate charge directional distribution data; perform impedance characteristic mapping on the charge directional distribution data to obtain time-domain charge mapping data;

[0075] Step S3: Perform transient voltage capture on the time-domain charge mapping data to obtain voltage distribution data; perform circuit topology reconstruction based on the voltage distribution data to generate initial equivalent circuit topology data;

[0076] Step S4: Obtain circuit temperature distribution data; perform electric field-thermal field multi-dimensional cross-locking on the initial equivalent circuit topology data based on the circuit temperature distribution data to obtain a temperature influence coefficient; perform thermoelectric joint correction modeling on the initial equivalent circuit topology data according to the temperature influence coefficient to generate a corrected equivalent circuit model;

[0077] Step S5: Perform charge-discharge simulation processing on the corrected equivalent circuit model to obtain simulation circuit operation data; perform aging factor embedding on the corrected equivalent circuit model based on the simulation circuit operation data to generate an equivalent circuit model of the lithium-ion battery.

[0078] The present invention realizes the accurate evaluation of battery performance by collecting initial battery electrochemical parameters. The charge density gradient decoupling provides an in-depth understanding of ion migration characteristics. The reconstructed polarization potential field generates charge-discharge curves, laying a foundation for battery behavior prediction. The generated charge directional distribution data by potential migration analysis enhances the visualization of internal battery processes. The implementation of impedance feature mapping improves the accuracy of battery performance analysis. The transient voltage capture provides a dynamic perspective of voltage distribution. The completion of circuit topology reconstruction ensures the scientificity and practicality of the battery model. The acquisition of temperature distribution data improves the understanding of battery thermal management. The determined temperature influence coefficient by the electric field-temperature field cross-locking provides a reliable basis for model correction. The generated corrected equivalent circuit model by thermoelectric joint correction modeling enhances the adaptability of the battery under different working conditions. The implementation of charge-discharge simulation processing provides data support for the practical application of battery performance. The embedding of aging factors improves the model's prediction ability for battery life and reliability, overall promoting the scientific and intelligent development of the equivalent circuit model of lithium-ion batteries.

[0079] In an embodiment of the present invention, the method for constructing the equivalent circuit model of the lithium-ion battery includes the following steps:

[0080] Step S1: Collect initial battery electrochemical parameters; perform charge density gradient decoupling on the initial battery electrochemical parameters to obtain ion migration characteristic data; perform polarization potential field reconstruction on the ion migration characteristic data to generate charge-discharge curves of the lithium-ion battery;

[0081] In this embodiment, when collecting the initial battery electrochemical parameters, a high-precision electrochemical workstation is used to perform constant current charge and discharge tests on the lithium-ion battery, and the data of the battery's voltage, current, and capacity changing with time are recorded. At the same time, the impedance characteristics of the battery are measured using electrochemical impedance spectroscopy (EIS) in the frequency range of 0.01 Hz to 100 kHz to obtain the initial electrochemical parameters, including the open circuit voltage (OCV), internal resistance, charge transfer resistance, and diffusion impedance. When performing charge density gradient decoupling on the initial electrochemical parameters, the finite element analysis (FEA) method is adopted to divide the internal charge distribution of the battery into multiple micro-elements, calculate the charge density gradient of each micro-element, and obtain the ion migration characteristic data, including the lithium-ion concentration distribution and migration rate, by solving the Poisson equation. When reconstructing the polarization potential field for the ion migration characteristic data, a physical model of the battery is established using multi-physics simulation software (such as COMSOL Multiphysics), the ion migration characteristic data is input into the model, and the polarization potential field is reconstructed by solving the Nernst-Planck equation and the Butler-Volmer equation to generate the charge and discharge curve of the lithium-ion battery, which contains information such as voltage plateau, capacity decay, and internal resistance change.

[0082] Step S2: Perform potential migration analysis on the charge and discharge curve of the lithium-ion battery to generate charge directional distribution data; perform impedance characteristic mapping on the charge directional distribution data to obtain time-domain charge mapping data;

[0083] In this embodiment, when performing potential migration analysis on the charge-discharge curve of a lithium-ion battery, the charge-discharge curve is transformed from the time domain to the frequency domain by using the discrete Fourier transform (DFT), and the potential components at different frequencies are extracted. The frequency-domain data is restored to the time-domain charge distribution through the inverse Fourier transform (IFT) to generate charge directional distribution data, which contains information on the migration direction and intensity of charges inside the battery. When performing impedance characteristic mapping on the charge directional distribution data, an equivalent circuit fitting software (such as ZView) is used to match the charge directional distribution data with an equivalent circuit model, and the circuit parameters are optimized by the least squares method to obtain time-domain charge mapping data, which contains the impedance characteristics of the battery under different charge-discharge states.

[0084] Step S3: Perform transient voltage capture on the time-domain charge mapping data to obtain voltage distribution data; based on the voltage distribution data, perform circuit topology reconstruction to generate initial equivalent circuit topology data;

[0085] In this embodiment, when performing transient voltage capture on the time-domain charge mapping data, a high-speed data acquisition card (DAQ) is used to record the voltage fluctuations of the battery during charge and discharge in real time at a sampling rate of 1 MHz, and the transient voltage characteristics are extracted through digital signal processing (DSP) technology to generate voltage distribution data, which contains information on the voltage changes of the battery at different time points. When performing circuit topology reconstruction based on the voltage distribution data, an equivalent circuit model of the battery is established using circuit simulation software (such as PSpice), the voltage distribution data is input into the model, and the connection methods and parameter values of the circuit components are determined through a topology optimization algorithm to generate initial equivalent circuit topology data, which contains the specific configurations of components such as resistors, capacitors, and inductors.

[0086] Step S4: Obtain circuit temperature distribution data; perform multi-dimensional cross-locking of the electric field and temperature field on the initial equivalent circuit topology data based on the circuit temperature distribution data to obtain the temperature influence coefficient; perform thermoelectric joint correction modeling on the initial equivalent circuit topology data according to the temperature influence coefficient to generate a corrected equivalent circuit model;

[0087] In this embodiment, when obtaining the circuit temperature distribution data, an infrared thermal imager is used to measure the surface temperature distribution of the battery with a resolution of 0.1 °C. At the same time, a thermocouple is embedded inside the battery to monitor the core temperature of the battery in real time, generating the circuit temperature distribution data. This data includes the temperature gradient of the battery at different positions. When performing the multi-dimensional cross-locking of the electric field and temperature field on the initial equivalent circuit topology data based on the circuit temperature distribution data, a multi-physics field coupling simulation method is adopted. The electric field and temperature field data are input into the simulation model, and the influence of temperature on the electric field distribution is calculated by solving the Maxwell equations and the heat conduction equation, obtaining the temperature influence coefficient. This coefficient reflects the degree of influence of temperature change on the circuit performance. When performing the thermoelectric joint correction modeling on the initial equivalent circuit topology data according to the temperature influence coefficient, an optimization algorithm (such as the genetic algorithm, Genetic Algorithm) is used to adjust the parameter values of the circuit components to make their performance consistent with the actual battery at different temperatures, generating a corrected equivalent circuit model. This model includes a temperature compensation circuit and a thermal management module.

[0088] Step S5: Perform charge and discharge simulation processing on the corrected equivalent circuit model to obtain the simulation circuit operation data; embed the aging factors into the corrected equivalent circuit model based on the simulation circuit operation data to generate the equivalent circuit model of the lithium-ion battery.

[0089] In this embodiment, when performing charge and discharge simulation processing on the corrected equivalent circuit model, a circuit simulation software is used to set the charge and discharge current to 1C (i.e., the current value corresponding to the battery capacity), run the simulation program, record the voltage, current, and temperature data of the battery under different charge and discharge states, generating the simulation circuit operation data. This data includes the charge and discharge performance and thermal behavior of the battery. When embedding the aging factors into the corrected equivalent circuit model based on the simulation circuit operation data, an accelerated aging experiment method is adopted. The battery is cycled for charge and discharge 1000 times under the conditions of high temperature (45 °C) and high rate (2C), and the data of battery capacity attenuation and internal resistance increase are recorded. The aging characteristics are embedded into the equivalent circuit model through a data fitting algorithm (such as polynomial fitting, Polynomial Fitting) to generate the equivalent circuit model of the lithium-ion battery. This model can accurately predict the performance changes of the battery under different usage conditions.

[0090] Preferably, step S1 includes the following steps:

[0091] Step S11: Collect the initial battery electrochemical parameters; perform signal smoothing filtering on the initial battery electrochemical parameters to obtain the electrochemical filtering signal;

[0092] Step S12: Perform ion concentration stratification extraction on the electrochemically filtered signal to generate concentration distribution data; construct a charge density field for the concentration distribution data to obtain density field data;

[0093] Step S13: Perform gradient matrix decomposition on the density field data to obtain ion migration characteristic data;

[0094] Step S14: Perform polarization field tensor reconstruction on the ion migration characteristic data to generate field strength distribution data;

[0095] Step S15: Perform electrochemical response mapping on the initial battery electrochemical parameters based on the field strength distribution data to generate the charge-discharge curve of the lithium-ion battery.

[0096] In this embodiment, when collecting the initial battery electrochemical parameters, the lithium-ion battery is placed in a constant-temperature test environment. The battery electrodes are connected through an electrochemical workstation, and the constant-current charge-discharge test is carried out in the current step mode. During the test, parameters such as the open circuit voltage, operating voltage, charge-discharge current, and charge capacity of the battery are recorded. The collected data is stored at millisecond time intervals. Subsequently, the adaptive mean filtering method is used to smooth and filter the electrochemical signal to remove high-frequency noise interference while maintaining the continuity of the signal boundary. Finally, a smooth electrochemical filtered signal is obtained. When performing ion concentration stratification extraction on the electrochemical filtered signal, first, a stratification electric field model is established based on the battery internal resistance and electrode potential. The electrochemical signal is converted into spectral data through Fourier transform. The time-frequency decomposition method is used to extract the concentration information of each layer of ions. The extracted ion concentrations are remapped according to the spatial coordinates to generate three-dimensional concentration distribution data. Subsequently, based on the Poisson equation, the charge density distribution on the electrode surface is calculated, and the charge density field is constructed by interpolation fitting. The density field data is stored in a grid form, and the continuity of the boundary data is optimized by interpolation. When performing gradient matrix decomposition on the density field data, first, a density gradient operator is constructed, and the second-order partial derivative is used to calculate the gradient distribution of the density field in each direction. The calculated gradient data is stored in the form of a sparse matrix. Subsequently, the singular value decomposition (SVD) method is used to decompose the gradient matrix to extract the main ion migration channel information, and the ion migration characteristic data is constructed based on this. During the calculation, outlier detection is performed on high-noise data points, and the lost data points are compensated by bidirectional interpolation. When performing polarization field tensor reconstruction on the ion migration characteristic data, first, the polarization intensity is calculated based on maximum entropy optimization, and a local polarization vector field is constructed on the ion migration path. The finite element analysis (Using the (FEA) method to calculate the spatial distribution of the polarization tensor, saving the calculated tensor data in the tensor storage format, and applying Lagrange interpolation to optimize the smoothness of the tensor data to finally generate the field strength distribution data. When performing electrochemical response mapping on the initial battery electrochemical parameters based on the field strength distribution data, first construct an electrode-electrolyte interface model based on the field strength data, use the Poisson-Boltzmann equation to calculate the redistribution of charges on the electrode surface, and then use the finite difference method to simulate the charge exchange process between the electrode and the electrolyte to finally generate the charge and discharge curves of the lithium-ion battery. During the calculation of the charge and discharge curves, consider the temperature effect, stress field influence, and polarization effect, and use experimental data to correct the curves so that the final charge and discharge curves can accurately reflect the actual working characteristics of the battery.,

[0097] Preferably, step S2 includes the following steps:

[0098] Step S21: Perform potential migration stratification on the charge and discharge curves of the lithium-ion battery to obtain potential tomography matrix data; perform steady-state-perturbation dissection on the potential tomography matrix data to generate steady-state parameter mapping data;

[0099] Step S22: Perform charge transition locking on the steady-state parameter mapping data to generate charge directional distribution data;

[0100] Step S23: Remove non-linear parasitic impedance from the charge directional distribution data to generate impedance purification data; construct a polarization lag matrix for the impedance purification data to obtain a polarization characteristic matrix;

[0101] Step S24: Perform frequency-divided charge coupling operation on the polarization characteristic matrix to obtain time-domain charge mapping data.

[0102] In this embodiment, when performing potential migration stratification on the charge-discharge curve of a lithium-ion battery, first, the charge-discharge curve data is segmented along the time axis. The potential data within each time period is extracted and the second-order difference is calculated to obtain the potential change rate. Subsequently, the wavelet decomposition method is used to decompose the potential change rate signal into different frequency band components, and principal component analysis (PCA) is performed on each frequency band signal to extract the main potential migration features. The obtained potential components are rearranged according to the potential gradient change trend to construct potential tomography matrix data. When storing the matrix data, a sparse matrix storage format is adopted to optimize the calculation efficiency. At the same time, interpolation optimization is performed on the boundary data to ensure the continuity and resolvability of the data. When performing steady-state-perturbation dissection on the potential tomography matrix data, first, a steady-state filter is constructed based on the charge-discharge cycle characteristics of the battery to filter out high-frequency perturbation signals and extract steady-state potential data. Subsequently, statistical analysis is performed on the steady-state signal to calculate its mean and variance to determine the steady-state range. The short-time Fourier transform (STFT) is used to extract the frequency components of the high-frequency perturbation signals, and spectral entropy analysis is used to distinguish the stable interval and the perturbation interval, and local potential anomaly regions are marked within the perturbation interval. The steady-state signal and the perturbation signal are stored separately, and steady-state parameter mapping data is constructed. This mapping data is stored in a multi-layer tensor format to ensure that the correlation between different steady-state partitions remains consistent in subsequent calculations. When performing charge transition locking on the steady-state parameter mapping data, first, the charge accumulation in the steady-state region is extracted, and the Gaussian mixture model (GMM) is used to classify the steady-state charge distribution to determine the local distribution characteristics of the steady-state charge. Subsequently, based on the Poisson equation, the migration trend of the steady-state charge in the polarization field is calculated, and the finite element method (The charge gradient is solved by the finite element method (FEM), and a charge directional migration channel is constructed in the steady-state electric potential field. The charge transition trajectory is modeled by the Markov process to generate charge directional distribution data. This data is optimized for boundary continuity using fourth-order interpolation to ensure the resolvability of the charge transition trajectory. When removing the non-linear parasitic impedance from the charge directional distribution data, first, impedance spectroscopy analysis is performed on the charge directional distribution data to extract the parasitic impedance characteristics inside the battery. An equivalent circuit fitting method is used to construct a parasitic impedance model, and wavelet denoising is performed in the frequency domain to remove the parasitic impedance components. The purified data is converted to the time domain, and the second derivative is used to detect local non-linear distortion regions. After the parasitic impedance removal is completed, a polarization lag matrix is constructed for the impedance-purified data. First, the polarization lag time constant is calculated, and curve fitting is performed using the least squares method to establish the mapping relationship between the polarization lag time and the electric potential gradient. Finally, a polarization characteristic matrix is formed. The data of this matrix is stored in block diagonal storage to optimize the calculation efficiency. When performing frequency-divided charge coupling operation on the polarization characteristic matrix, first, a fast Fourier transform (FFT) is performed on the polarization characteristic matrix to extract the polarization characteristic components of different frequency bands. Harmonic analysis is used to calculate the mutual coupling relationship between different frequency bands. Subsequently, a convolutional neural network (CNN) is used to perform non-linear mapping training on the polarization characteristics to generate charge coupling weights for different frequency bands. During the calculation process, the adaptive weight optimization method is used to adjust the charge distribution, so that the charge responses in different frequency bands have higher time resolution. Finally, the calculated charge response data is converted to the time domain to form time-domain charge mapping data. This data is stored in a time-series database to ensure the efficient access and real-time analysis of the data.,

[0103] Preferably, step S23 includes the following steps:

[0104] Extract the impedance component data from the charge directional distribution data; identify the non-linear characteristics of the impedance component data to generate non-linear characteristic data;

[0105] Parasitic impedance screening is performed on the charge directional distribution data based on the non-linear characteristic data to generate parasitic impedance data; impedance purification processing is performed on the parasitic impedance data to obtain impedance purification data;

[0106] Optional hysteresis point detection is performed on the impedance purification data to obtain candidate hysteresis points; extreme hysteresis screening is performed on the candidate hysteresis points to generate extreme hysteresis points;

[0107] Hysteresis time calibration is performed on the extreme hysteresis points to obtain time calibration data; hysteresis amount calculation is performed on the time calibration data to generate hysteresis amount data;

[0108] Matrix construction is performed based on the hysteresis amount data to generate an extremized feature matrix.

[0109] In this embodiment, when extracting the impedance components from the charge directional distribution data, the charge directional distribution data is first transformed into the frequency domain. The impedance spectral components in the charge response signal are extracted using the Fast Fourier Transform (FFT). The signal frequency band is refined by setting the frequency resolution in the range of 0.01 Hz to 10 kHz. Subsequently, the Hanning Window function is used to smooth the frequency domain data to reduce spectral leakage. Based on the Nyquist plot, the real and imaginary components are separated to obtain the impedance component data containing the resistance (real part) and reactance (imaginary part). This data is represented in the form of a complex matrix during storage to ensure the integrity of the frequency domain characteristics. When identifying the non-linear characteristics of the impedance component data, Wavelet Packet Decomposition is applied to decompose the impedance component data into multi-scale signals, and the non-linear characteristics at different scales are extracted. Subsequently, the Lyapunov Exponent is used to quantify the dynamic stability of the signal to determine the non-linear characteristic region in the impedance components. Then, based on the Support Vector Machine (SVM) classification algorithm, the linear and non-linear regions are segmented to generate the characteristic data containing the non-linear response. All non-linear characteristic data is stored in the form of feature vectors for subsequent analysis and processing. When screening the parasitic impedance of the charge directional distribution data based on the non-linear characteristic data, the Equivalent Circuit Model Fitting method is used to compare the extracted non-linear characteristic data with the parasitic impedance model of the standard battery. The difference is fitted by the Least Squares Method to identify the parasitic impedance components in the charge directional distribution. During the screening process, the threshold deviation between the charge directional path and the potential gradient is set to be less than 5% to ensure the screening accuracy. The screened parasitic impedance data is recorded in the form of an independent impedance vector to ensure its separation from the original data. When performing impedance purification processing on the parasitic impedance data, a Frequency Domain Filter is used to remove high-frequency noise. The Butterworth Low-pass Filter is selected, and the cut-off frequency is set to 1 kHz to ensure the retention of the effective low-frequency signals in the parasitic impedance data. At the same time, the Adaptive Filtering Algorithm is applied to dynamically adjust the filtering parameters to cope with the changes in the parasitic impedance under different battery states. The purified impedance data is saved in both time-domain and frequency-domain formats for subsequent analysis and modeling. When performing optional lag point detection on the impedance purified data,First, use the Sliding Window Algorithm to traverse the impedance time series data. Set the window size to 50ms, calculate the standard deviation of the impedance change rate within each window, and mark the time points with significant changes in the standard deviation as candidate lag points. Subsequently, perform statistical analysis on the candidate lag points through the Z-score normalization method to screen out the initial set of lag points. These candidate lag points are recorded in the database in the form of timestamps and impedance change rates. When performing extreme lag screening on the candidate lag points, use the Extreme Value Detection Algorithm to perform a second-order difference on the impedance change amplitude of the candidate lag points, and screen out the extreme points with a change amplitude exceeding twice the average value as the extreme lag points. Further, use the Kalman Filter to smooth the lag points to eliminate random noise and ensure that the selected extreme lag points have significant physical meanings. The screening results are stored in the form of an extreme lag point sequence. When calibrating the lag time for the extreme lag points, based on the timestamps corresponding to each extreme lag point, calculate the time intervals of the impedance changes before and after, and use the Polynomial Interpolation Method to refine the calibration of the lag time to ensure that the accuracy of the lag time is at the millisecond level. At the same time, adjust the calibration coefficient according to different battery charge and discharge states to adapt to the dynamic characteristics of the battery. The calibrated time data is saved in the high-precision floating-point format. When calculating the lag amount for the time-calibrated data, use the relationship between the lag time and the impedance change rate to calculate the lag amount of each extreme lag point, and use the Integration Method to accumulate the impedance changes within the lag time to obtain the lag amount data. Set the integration step size to 1ms during the lag amount calculation process to ensure the time resolution of the data. The calculated lag amount data is stored in matrix form for further analysis. When constructing the matrix based on the lag amount data, use the Sparse Matrix Representation to organize the lag amount data into matrix form. The rows of the matrix represent different extreme lag points, and the columns represent the corresponding lag amounts. Subsequently, apply the Normalization Process to standardize the lag amount data to the range of 0 to 1 to ensure the comparability of data with different dimensions. Finally, generate the extreme value feature matrix, which is stored in the Compressed Sparse Row (CSR) format to optimize the storage efficiency and calculation performance.

[0110] Preferably, step S3 includes the following steps:

[0111] Step S31: Capture non-uniform charge and discharge segment data from the time-domain charge mapping data to obtain charge and discharge segment data; perform transient voltage cross-locking on the charge and discharge segment data to generate voltage distribution data;

[0112] Step S32: Perform impedance gradient hedging simulation on the voltage distribution data to generate an impedance hedging matrix; perform equivalent capacitance-resistance coupling extraction on the impedance hedging matrix to obtain capacitance impedance mapping data;

[0113] Step S33: Perform non-linear charge expansion fitting on the capacitance impedance mapping data to obtain equivalent circuit element parameter data;

[0114] Step S34: Perform topological mapping conversion on the equivalent circuit element parameter data based on the capacitance impedance mapping data to obtain initial equivalent circuit topology data.

[0115] In this embodiment, when capturing non-uniform charge and discharge segments from time-domain charge mapping data, first, the time-series of the time-domain charge mapping data collected from the battery under different load conditions is partitioned. Using the SlidingWindow technique, the entire dataset is divided into equally-spaced time segments with a window size of 100 ms and an overlap rate of 50%. Subsequently, the rate of charge change for each time segment is differentiated to calculate the instantaneous slope of the charge change. The threshold method is used to set the criteria for determining the charging and discharging states, with the threshold set at ±0.1 C / s (coulombs per second). Segments above the positive threshold are marked as charging segments, segments below the negative threshold are marked as discharging segments, and the intermediate values are regarded as steady-state segments. The extracted charge and discharge segment data is stored in the form of timestamps and charge rate change curves to ensure that the complete non-uniform charge and discharge process is captured. When performing transient voltage cross-locking on the charge and discharge segment data, first, the transient voltage data corresponding to the charge and discharge segments is synchronously collected, and a high-precision voltage sampling module (accuracy ±0.01 V) is used to record the voltage changes. Subsequently, the linear interpolation method (Linear Interpolation) is used to compensate for the voltage changes that occur during the charging and discharging processes to ensure that the data time axes are aligned. Then, the cross-correlation algorithm (Cross-correlation Algorithm) is used to analyze the similarity between the charging and discharging voltage waveforms, and the key matching points of the charging and discharging voltages are locked through the maximum correlation coefficient. The voltage values corresponding to the matching points are extracted to form voltage distribution data, which is represented in the form of a two-dimensional matrix. The rows of the matrix represent different charge and discharge segments, and the columns represent the corresponding transient voltage points. When performing impedance gradient hedging simulation on the voltage distribution data, based on the obtained voltage distribution data, the numerical differentiation method (Numerical Differentiation) is used to calculate the gradient of the voltage change over time. Subsequently, the current change data is combined with the voltage gradient data, and the dynamic impedance value at each moment is calculated according to Ohm's Law. On this basis, the finite element simulation (Finite Element Simulation) method is applied to construct a multi-dimensional impedance gradient field, and the impedance change trend in different voltage regions is calculated by the difference method. During the simulation process, the time step is set at 1 ms, and the spatial resolution is 0.1V, the generated impedance hedging matrix is stored in a three-dimensional data structure, where the matrix axes represent time, voltage, and impedance gradient respectively. When performing equivalent capacitance-resistance coupling extraction on the impedance hedging matrix, first convert the impedance hedging matrix into frequency-domain data, and use the Discrete Fourier Transform (DFT) to extract the impedance characteristics at different frequencies. Subsequently, based on the series and parallel relationships of the equivalent circuit model, use the Least Squares Curve Fitting method to separate the independent components of capacitance and resistance. During the fitting process, set the capacitance range from 1 μF to 5000 μF and the resistance range from 1 mΩ to 100 Ω to ensure coverage of the battery characteristics under different operating conditions. The capacitance impedance mapping data after coupling extraction is saved in the form of a capacitance-resistance correspondence table for subsequent parameter fitting. When performing non-linear charge expansion fitting on the capacitance impedance mapping data, select the Polynomial Regression method to model the non-linear relationship between charge and impedance as a high-order polynomial function, and use a cubic or quartic polynomial to describe the charge expansion characteristics. In the regression analysis, use 5-fold Cross Validation to ensure the robustness and fitting accuracy of the model. The fitting parameters are optimized by the Gradient Descent Method, and the learning rate is set to 0.01. The fitting results generate equivalent circuit element parameter data, including key parameters such as equivalent capacitance, resistance, and inductance. When performing topological mapping conversion on the equivalent circuit element parameter data based on the capacitance impedance mapping data, first construct the basic framework of the circuit topology structure, use the Graph Theory Algorithm to map the capacitance and resistance parameters into the form of nodes and edges, define the nodes to represent charge storage elements, and the edges to represent charge flow paths. Subsequently, according to the coupling relationships extracted from the capacitance impedance mapping data, determine the series or parallel structure of the components, and use the Adjacency Matrix to describe the connection relationships between the components. Finally, generate the initial equivalent circuit topology data.

[0116] Preferably, step S4 includes the following steps:

[0117] Step S41: Obtain the temperature data of the lithium-ion battery; perform circuit temperature distribution analysis on the temperature data of the lithium-ion battery to generate circuit temperature distribution data;

[0118] Step S42: Perform thermal coupling mode decomposition on the initial equivalent circuit topology data according to the circuit temperature distribution data to obtain a temperature distribution matrix; reorganize the thermal conduction paths of the temperature distribution matrix to generate heat flow distribution data;

[0119] Step S43: Perform electric field-temperature field multi-dimensional cross-locking on the initial equivalent circuit topology data based on the heat flow distribution data to obtain the temperature influence coefficient; perform circuit dynamic correction on the initial equivalent circuit topology data according to the temperature influence coefficient to generate corrected equivalent circuit topology data;

[0120] Step S44: Reconstruct the circuit model for the corrected equivalent circuit topology data to generate a corrected equivalent circuit model.

[0121] In this embodiment, when obtaining the temperature data of the lithium-ion battery, firstly, high-precision temperature sensors, such as thermocouples or thermistors, are arranged at different positions of the lithium-ion battery. The sensors are distributed at key positions such as the positive electrode, the negative electrode, the diaphragm and the shell to ensure that the temperature gradient inside and on the surface of the battery is covered. The accuracy of the temperature sensor is set to ±0.1°C, and the data sampling frequency is 1Hz. The real-time temperature data is recorded and transmitted to the processing unit using a data acquisition system (DAQ). Subsequently, the acquired temperature data is preprocessed, and a moving average filter is used to eliminate high-frequency noise. The filter window size is set to 5 data points. When the circuit temperature distribution analysis is performed on the lithium-ion battery temperature data, the temperature data of different sensor points are interpolated in two or three dimensions using a spatial interpolation method based on the acquired temperature time series data, and the Kriging interpolation method is used. The temperature distribution of each position inside the battery is calculated by using the interpolation resolution of 0.1 mm to ensure that the refined temperature distribution map can reflect the small temperature gradient changes inside the battery. The interpolation result is then converted into circuit temperature distribution data. The circuit temperature distribution data is stored in a matrix form, where rows represent different time points and columns represent temperature values ​​at different spatial positions inside the battery. When the initial equivalent circuit topology data is subjected to thermal coupling modal decomposition according to the circuit temperature distribution data, the initial equivalent circuit topology data is firstly synchronously matched with the circuit temperature distribution data. The finite element thermal analysis is used to perform thermal response analysis on each component in the equivalent circuit to determine the working state of each component at different temperatures. The modal analysis method is then applied to decompose the thermal coupling effect and extract the temperature modal characteristics of different components in the circuit. During the modal decomposition process, the Lanczos algorithm is used to calculate the eigenvalues ​​and eigenvectors to obtain the temperature distribution matrix. Each row of the temperature distribution matrix represents a circuit element and each column represents a different temperature modal parameter. When the temperature distribution matrix is ​​subjected to thermal conduction path reorganization, the Fourier's Law of Heat Conduction is used to calculate the temperature distribution matrix. Conduction), conduct path analysis on the temperature distribution matrix, use the Thermal Network Analysis method to reorganize the heat conduction path in the circuit, and calculate the heat flow distribution between the various parts of the circuit by building a thermal resistance network model. The thermal resistance value setting range in the model is 0.From 01 K / W to 10 K / W, the effects of different materials and structures inside the analog circuit on heat flow are simulated, and finally heat flow distribution data is generated. The heat flow distribution data is represented in matrix form. The rows of the matrix represent the starting points of heat flow, the columns represent the ending points of heat flow, and the values in the matrix represent the heat flow intensity on the corresponding paths. When performing electric field-temperature field multi-dimensional cross-locking on the initial equivalent circuit topology data based on the heat flow distribution data, first, the heat flow distribution data and the electric field distribution data are synchronously superimposed. Using the Multiphysics Coupling Analysis method, a joint model of the electric field and the temperature field is established. The Finite Element Electro-Thermal Coupling Analysis is used to calculate the electric field changes of each component in the circuit under different heat flow conditions. Subsequently, the Cross-Correlation Algorithm is applied to analyze the interaction between the electric field and the temperature field, and the influence coefficient of temperature on the electric field is extracted. The temperature influence coefficient is stored in matrix form. The rows of the matrix represent different circuit components, and the columns represent the electric field change rates under different temperature conditions. When dynamically correcting the initial equivalent circuit topology data according to the temperature influence coefficient, first, the temperature influence coefficient is compared with the parameters of the initial equivalent circuit model, and the Parameter Adjustment Algorithm is used to dynamically correct the parameters such as resistance and capacitance of the circuit components. The Gradient Descent Method is used to optimize the parameters during the correction process, and the learning rate is set to 0.01. When reconstructing the circuit model for the corrected equivalent circuit topology data, based on the corrected circuit topology data, an equivalent circuit model is reconstructed, and a circuit simulation software (such as SPICE) is used to simulate and verify the model. Different charge and discharge conditions and temperature environments are set during the simulation process to verify the accuracy and stability of the model under various working conditions. The reconstructed corrected equivalent circuit model is saved in XML or JSON format to ensure good compatibility between different simulation platforms, and finally a complete equivalent circuit model of the lithium-ion battery is formed.

[0122] Preferably, step S43 includes the following steps:

[0123] Perform time-domain decomposition on the heat flow distribution data to obtain time-series heat flow characteristics; construct a vector matrix according to the time-series heat flow characteristics to generate a heat flow characteristic matrix;

[0124] Recombine the heat conduction paths based on the heat flow characteristic matrix to generate heat flow distribution data;

[0125] Perform an analysis of the electric field distribution on the initial equivalent circuit topology data to obtain the characteristics of the electric field distribution; analyze the corresponding relationship between the heat flow distribution data and the characteristics of the electric field distribution to generate a field mapping matrix;

[0126] Perform an electric field-temperature field multi-dimensional cross-lock on the initial equivalent circuit topology data according to the field mapping matrix to obtain the temperature influence coefficient;

[0127] Perform a circuit parameter mapping on the initial equivalent circuit topology data according to the temperature influence coefficient to obtain the temperature influence parameter; update the topology structure of the initial equivalent circuit topology data based on the temperature influence parameter to generate the modified equivalent circuit topology data.

[0128] In this embodiment, when performing time-domain decomposition on the heat flux distribution data, the heat flux distribution data is first organized into a time series data format, where each data point contains the heat flux intensity, position, and timestamp. The fast Fourier transform (FFT) is used to perform frequency-domain analysis on the heat flux data to extract the heat flux characteristics corresponding to different frequency components. The resolution of the Fourier transform is set to 0.1 Hz to capture minute heat flux fluctuations. Subsequently, the wavelet transform is applied to further decompose the local characteristics of the heat flux. The Daubechies wavelet basis is selected for decomposition, and the decomposition level is set to 5 layers. The time-series heat flux characteristics, including the heat flux peak, duration, and frequency distribution, are extracted therefrom. The time-series heat flux characteristics are stored in matrix form, where the rows represent time nodes and the columns represent the heat flux characteristic parameters. When constructing the vector matrix based on the time-series heat flux characteristics, the extracted time-series heat flux characteristics are first normalized using the Z-score normalization method to convert the heat flux characteristics into zero mean and unit variance. Subsequently, the feature vector matrix is constructed. The rows of the matrix represent different time nodes, and the columns represent different heat flux characteristic parameters, such as the heat flux intensity, fluctuation frequency, and peak position. The principal component analysis (PCA) is used to perform dimensionality reduction on the feature matrix, and the principal components with a cumulative contribution rate reaching 95% are retained. Finally, a heat flux feature matrix is generated. When reorganizing the heat conduction path based on the heat flux feature matrix, the heat flux feature matrix is input into the heat network modeling system, and the finite element analysis (FEA) tool is used to simulate the heat conduction path inside the battery. By adjusting the thermal resistance and heat capacity parameters, the main heat conduction paths inside the battery are identified. The initial value of the thermal resistance is set to 0.01 K / W, and the maximum value does not exceed 10 K / W. The range of the heat capacity parameter is 0.Between 1 J / K and 10 J / K, based on the simulation results, reorganize the heat conduction path to generate new heat flux distribution data. The newly generated heat flux distribution data is represented in the form of a three-dimensional matrix, where the coordinate axes represent time, spatial position, and heat flux intensity respectively. When performing the electric field distribution analysis on the initial equivalent circuit topology data, import the corrected equivalent circuit topology data into the electric field simulation system, and use the finite element method to calculate the electric field distribution of each node and component in the circuit. In the electric field simulation, consider the conductivity and permittivity of the electrode material. The conductivity range is set from 1 S / m to 1000 S / m, and the permittivity value range is from 1 to 10. The simulation results are output in the form of an electric field intensity distribution diagram. Further extract the electric field distribution characteristics, including electric field intensity, direction, and distribution uniformity. The electric field distribution characteristics are stored in matrix form. The rows of the matrix represent the circuit nodes, and the columns represent the electric field characteristic parameters. When analyzing the correspondence between the heat flux distribution data and the electric field distribution characteristics, use the Multivariate Regression Analysis method to match the heat flux distribution data with the electric field distribution characteristics, analyze the influence of heat flux changes on the electric field distribution, and construct the mapping relationship between heat flux and electric field. The input variables in the regression model are heat flux characteristic parameters, and the output variables are electric field intensity and direction. The goodness of fit R of the model 2 is required to be greater than 0.9 to ensure the accuracy and reliability of the model. Finally, generate the field mapping matrix. The rows of the field mapping matrix represent the heat flux characteristics, and the columns represent the corresponding electric field distribution characteristics. When performing the multi-dimensional cross-lock of the electric field-temperature field on the initial equivalent circuit topology data, fuse the field mapping matrix with the equivalent circuit topology data, and use the multi-physics field coupling simulation tool for electro-thermal coupling analysis. Input different heat flux and electric field parameters in the simulation, observe the circuit response, and extract the influence coefficient of temperature on the electric field. The influence coefficient is represented in the form of a numerical matrix. The rows of the matrix represent different circuit components, and the columns represent different temperature conditions. The range of the influence coefficient is set between 0 and 1, reflecting the sensitivity of temperature to the circuit performance. When performing the circuit parameter mapping on the initial equivalent circuit topology data according to the temperature influence coefficient, apply the temperature influence coefficient to each parameter in the equivalent circuit model, and use the parameter adjustment algorithm to correct the component parameters such as resistance, capacitance, and inductance. The correction range of the resistance is ±10%, the correction range of the capacitance is ±5%, and the correction range of the inductance is ±3%. The corrected parameters are stored as the temperature influence parameter matrix. The rows of the matrix represent different components, and the columns represent the corrected parameter values under different temperature conditions. When updating the topology structure of the initial equivalent circuit topology data based on the temperature influence parameters, input the temperature influence parameters into the equivalent circuit model, readjust the circuit connection mode and component arrangement, and use the circuit simulation tool to verify the updated topology structure to ensure the stable performance of the circuit at different temperatures. The updated corrected equivalent circuit topology data is output in a standardized format.

[0129] Preferably, step S5 includes the following steps:

[0130] Step S51: Perform mesh generation on the modified equivalent circuit model to obtain mesh model data; discretize the state space of the mesh model data to generate state space data;

[0131] Step S52: Map the circuit parameters of the state space data to obtain circuit simulation data; perform model response simulation on the circuit simulation data based on the modified equivalent circuit model to obtain simulated circuit operation data;

[0132] Step S53: Extract the cyclic life characteristics of the simulated circuit operation data to obtain life characteristic data; trace the capacity attenuation path of the life characteristic data to generate attenuation trajectory data;

[0133] Step S54: Map the parameter attenuation of the attenuation trajectory data to obtain the battery attenuation coefficient; modify the model parameters of the modified equivalent circuit model based on the battery attenuation coefficient to generate the equivalent circuit model of the lithium-ion battery.

[0134] In this embodiment, when performing mesh generation on the modified equivalent circuit model, first import the modified equivalent circuit model into the Finite Element Analysis (FEA) platform. Select the Delaunay triangulation algorithm to divide the circuit model into meshes, and set the mesh size to 0.1 mm to ensure the accuracy of detailed features. During the mesh generation process, adopt the adaptive refinement technique to generate denser meshes in areas with a large current density gradient to improve the simulation accuracy. The mesh density in the electrode and electrolyte interface area is set to twice that of the standard mesh. After mesh generation, perform a quality check and remove mesh elements with a Quality Factor less than 0.8. Finally, obtain the mesh model data that meets the quality requirements. The mesh model data is stored in a data file in the form of node coordinates, element connection relationships, and boundary conditions. When discretizing the mesh model data in the state space, use the Finite Difference Method (FDM) to discretize the continuous circuit state equations into difference equations. First, extract the node voltages and currents in the mesh model as state variables, divide the circuit response in the time domain into time steps of 0.01 seconds, and use the Implicit Euler Method to discretize the voltages and currents in time. During the process, the resistance, capacitance, and inductance parameters are assigned by node, and a state equation set is established according to the circuit topology. The discretized equation set is stored in matrix form, where the matrix rows represent time steps and the columns represent the state variables of the circuit elements. Finally, generate the state space data, which includes the discrete values of the voltages and currents of the circuit nodes changing with time. When performing circuit parameter mapping on the state space data, match the state space data with the parameters in the modified equivalent circuit model, and use the Least Squares Method to fit the relationship between the state space data and the model parameters. The parameter adjustment ranges of resistance, capacitance, and inductance are set to ±5%, ±3%, and ±2% respectively. Continuously adjust the parameters through the iterative optimization algorithm until the fitting error is less than 0.01. Finally, generate the circuit simulation data, which contains the dynamic response information of each circuit element under different working conditions. When performing model response simulation on the circuit simulation data based on the modified equivalent circuit model, input the circuit simulation data into the multi-physics simulation platform, and use the SPICE circuit simulation tool to perform time-domain simulation on the equivalent circuit model. The simulation time range is set from 0 to 3600 seconds, and the time step is 1 second. During the simulation, apply 0.Under the constant current charge and discharge conditions of 5C, 1C, and 2C rates, record the changes in voltage, current, and power of each component in the circuit over time. The simulation data is stored in the form of a time series, and finally, the simulation circuit operation data is obtained. The simulation circuit operation data includes the response characteristics of the battery under different load conditions. When extracting the cycle life characteristics from the simulation circuit operation data, the simulation data is segmented according to the charge and discharge cycles, and the peak detection algorithm is used to extract the charge and discharge capacity, internal resistance change, and voltage plateau characteristics of each cycle. The internal resistance change is calculated by taking the derivative of the ratio of the voltage across the battery terminals to the current using the difference method. When extracting features, data with a signal-to-noise ratio (SNR) less than 20 dB is filtered out to ensure the accuracy of feature extraction. Finally, the life characteristic data is obtained. The life characteristic data includes the number of cycles, the capacity attenuation rate, and the internal resistance growth trend. When tracking the capacity attenuation path of the life characteristic data, the polynomial fitting method is used to model the change in capacity with the number of cycles. The order of the fitting polynomial is set to 3 to ensure that the model can accurately describe the non-linear attenuation trend. During the tracking process, the temperature data and charge and discharge rate data of the battery are combined to analyze the capacity attenuation path under different working conditions. The tracking result is output in the form of a curve. The horizontal axis of the curve represents the number of cycles, and the vertical axis represents the capacity retention rate. Finally, the attenuation trajectory data is generated. The attenuation trajectory data contains the key nodes of capacity attenuation and the trend change information. When performing parameter attenuation mapping on the attenuation trajectory data, the exponential attenuation model is used to fit the relationship between capacity attenuation and circuit parameter changes. The input variable in the model is the cycle life characteristic parameter, and the output variable is the change rate of resistance and capacitance. During the parameter mapping process, the non-linear least squares method is used for optimization to ensure that the fitting error of the model is less than 0.005. Finally, the battery attenuation coefficient is obtained. The battery attenuation coefficient is represented in matrix form. The rows of the matrix represent different cycle stages, and the columns represent the corresponding circuit parameter change rates. When correcting the model parameters of the modified equivalent circuit model based on the battery attenuation coefficient, the attenuation coefficient is applied to each component parameter in the equivalent circuit model, and the parameter adjustment algorithm is used to dynamically correct the resistance, capacitance, and inductance. The resistance parameter is increased by 5% to 15% according to the attenuation coefficient, and the capacitance parameter is decreased by 2% to 10% according to the attenuation coefficient. The corrected parameters are verified in the simulation platform to ensure the accuracy of the model at different cycle stages. Finally, a lithium-ion battery equivalent circuit model is generated. This model is saved in the standard SPICE format and contains complete circuit topology and parameter information.

[0135] The present invention also provides a construction system for a lithium-ion battery equivalent circuit model, which is used to execute the construction method of the lithium-ion battery equivalent circuit model as described above. The construction system for the lithium-ion battery equivalent circuit model includes:

[0136] An electrochemical decoupling module, configured to collect initial battery electrochemical parameters; perform charge density gradient decoupling on the initial battery electrochemical parameters to obtain ion migration characteristic data; perform polarization potential field reconstruction on the ion migration characteristic data to generate charge-discharge curves of a lithium-ion battery;

[0137] A potential mapping module, configured to perform potential migration analysis on the charge-discharge curves of the lithium-ion battery to generate charge directional distribution data; perform impedance characteristic mapping on the charge directional distribution data to obtain time-domain charge mapping data;

[0138] A topology reconstruction module, configured to perform transient voltage capture on the time-domain charge mapping data to obtain voltage distribution data; perform circuit topology reconstruction based on the voltage distribution data to generate initial equivalent circuit topology data;

[0139] A thermoelectric correction module, configured to obtain circuit temperature distribution data; perform multi-dimensional cross-locking of electric field-temperature field on the initial equivalent circuit topology data based on the circuit temperature distribution data to obtain a temperature influence coefficient; perform thermoelectric combined correction modeling on the initial equivalent circuit topology data according to the temperature influence coefficient to generate a corrected equivalent circuit model;

[0140] An aging simulation module, configured to perform charge-discharge simulation processing on the corrected equivalent circuit model to obtain simulation circuit operation data; perform aging factor embedding on the corrected equivalent circuit model based on the simulation circuit operation data to generate an equivalent circuit model of a lithium-ion battery.

[0141] The present invention realizes efficient acquisition of initial electrochemical parameters of a lithium-ion battery through the introduction of an electrochemical decoupling module. The obtained ion migration characteristic data lays a foundation for battery performance analysis. The charge-discharge curves generated by polarization potential field reconstruction provide a dynamic perspective on battery behavior. The application of the potential mapping module improves the analysis accuracy of the battery charge-discharge curves. The combination of charge directional distribution data and impedance characteristic mapping enhances the understanding of the internal distribution state of the battery. The topology reconstruction module ensures the scientificity and accuracy of the circuit topology through the analysis of transient voltage capture and voltage distribution data. The implementation of the thermoelectric correction module makes the determination of the temperature influence coefficient more accurate, providing a more reliable model basis for the performance of the battery under different working conditions. The application of the aging simulation module not only enhances the prediction ability of the model for battery aging behavior but also realizes dynamic update and optimization, overall improving the accuracy and applicability of the equivalent circuit model of a lithium-ion battery and promoting the intelligent and efficient development of the battery management system.

[0142] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed, it implements the method for constructing an equivalent circuit model of a lithium-ion battery as described in any one of the above.

[0143] The present invention realizes the efficient storage and execution of the method for constructing the equivalent circuit model of lithium-ion batteries through the design of a computer-readable storage medium. The execution of the program ensures the collaborative work among various modules, improves the degree of automation of data processing. The integration of modules such as electrochemical decoupling, topology reconstruction, and thermoelectric correction enhances the comprehensiveness of battery performance analysis. The use of the storage medium reduces the complexity of manual operations and reduces the occurrence of human errors. The programmed implementation improves the repeatability and reliability of model construction. The ability of dynamic update and optimization enhances the real-time response to battery state changes. Overall, it improves the scientificity and practicality of the equivalent circuit model of lithium-ion batteries, and promotes the progress and application of battery management technology.

[0144] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0145] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a lithium-ion battery equivalent circuit model, characterized in that: The following steps are involved: Step S1: collecting initial battery electrochemical parameters; performing charge density gradient decoupling on the initial battery electrochemical parameters to obtain ion migration characteristic data; Reconstruct the polarization potential field of ion migration characteristic data to generate the charge and discharge curve of lithium-ion battery; Step S2: performing potential migration analysis on the charge and discharge curve of the lithium-ion battery to generate charge directional distribution data; performing impedance characteristic mapping on the charge directional distribution data to obtain time domain charge mapping data; Step S3: performing transient voltage capture on the time-domain charge mapping data to obtain voltage distribution data; performing circuit topology reconstruction based on the voltage distribution data to generate initial equivalent circuit topology data; Step S4: acquiring circuit temperature distribution data; performing electric field-temperature field multi-dimensional cross-locking on the initial equivalent circuit topology data based on the circuit temperature distribution data to obtain a temperature influence coefficient; performing thermoelectric joint correction modeling on the initial equivalent circuit topology data according to the temperature influence coefficient to generate a corrected equivalent circuit model; Step S5: Perform charge and discharge simulation processing on the modified equivalent circuit model to obtain simulation circuit operation data; embed aging factors into the modified equivalent circuit model based on the simulation circuit operation data to generate a lithium-ion battery equivalent circuit model.

2. The method for constructing a lithium-ion battery equivalent circuit model according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting initial battery electrochemical parameters; performing signal smoothing filtering on the initial battery electrochemical parameters to obtain an electrochemical filtering signal; Step S12: extracting ion concentrations by stratification on the electrochemical filter signal to generate concentration distribution data; constructing a charge density field on the concentration distribution data to obtain density field data; Step S13: performing gradient matrix decomposition on the density field data to obtain ion migration characteristic data; Step S14: reconstructing the polarization field tensor of the ion migration characteristic data to generate field intensity distribution data; Step S15: Perform electrochemical response mapping on the initial battery electrochemical parameters according to the field intensity distribution data to generate a lithium-ion battery charge and discharge curve.

3. The method for constructing a lithium-ion battery equivalent circuit model according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing potential migration stratification on the charge and discharge curve of the lithium-ion battery to obtain potential tomography matrix data; performing steady-state-disturbance segmentation on the potential tomography matrix data to generate steady-state parameter mapping data; Step S22: performing charge transition locking on the steady-state parameter mapping data to generate charge directional distribution data; Step S23: performing nonlinear parasitic impedance elimination on the charge oriented distribution data to generate impedance purified data; performing polarization hysteresis matrix construction on the impedance purified data to obtain a polarization characteristic matrix; Step S24: performing frequency division charge coupling operation on the polarization characteristic matrix to obtain time domain charge mapping data.

4. The method for constructing a lithium-ion battery equivalent circuit model according to claim 3, characterized in that: Step S23 includes the following steps: Extracting impedance components from the charge directional distribution data to obtain impedance component data; identifying nonlinear features from the impedance component data to generate nonlinear feature data; Performing parasitic impedance screening on the charge directional distribution data based on the nonlinear characteristic data to generate parasitic impedance data; performing impedance purification processing on the parasitic impedance data to obtain impedance purification data; Perform optional hysteresis point detection on the impedance purification data to obtain candidate hysteresis points; perform limit hysteresis screening on the candidate hysteresis points to generate limit hysteresis points; Performing hysteresis time calibration on the limit hysteresis point to obtain time calibration data; performing hysteresis amount calculation on the time calibration data to generate hysteresis amount data; Matrix construction is performed based on the lagged data to generate an extreme value characteristic matrix.

5. The method for constructing a lithium-ion battery equivalent circuit model according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Capture the non-uniform charge and discharge segments of the time domain charge mapping data to obtain charge and discharge segment data; perform transient voltage cross-locking on the charge and discharge segment data to generate voltage distribution data; Step S32: performing impedance gradient hedging simulation on the voltage distribution data to generate an impedance hedging matrix; performing equivalent capacitance-resistance coupling extraction on the impedance hedging matrix to obtain capacitance-impedance mapping data; Step S33: performing nonlinear charge expansion fitting on the capacitance impedance mapping data to obtain equivalent circuit element parameter data; Step S34: performing topological mapping conversion on the equivalent circuit element parameter data based on the capacitance-impedance mapping data to obtain initial equivalent circuit topological data.

6. The method for constructing a lithium-ion battery equivalent circuit model according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: acquiring lithium-ion battery temperature data; performing circuit temperature distribution analysis on the lithium-ion battery temperature data to generate circuit temperature distribution data; Step S42: performing thermal coupling modal decomposition on the initial equivalent circuit topology data according to the circuit temperature distribution data to obtain a temperature distribution matrix; reorganizing the temperature distribution matrix into heat conduction paths to generate heat flow distribution data; Step S43: performing electric field-temperature field multi-dimensional cross-locking on the initial equivalent circuit topology data based on the heat flow distribution data to obtain the temperature influence coefficient; performing circuit dynamic correction on the initial equivalent circuit topology data according to the temperature influence coefficient to generate corrected equivalent circuit topology data; Step S44: reconstructing the circuit model of the modified equivalent circuit topology data to generate a modified equivalent circuit model.

7. The method for constructing a lithium-ion battery equivalent circuit model according to claim 6, characterized in that: Step S43 includes the following steps: Decompose the heat flow distribution data in the time domain to obtain the time series heat flow characteristics; construct a vector matrix based on the time series heat flow characteristics to generate a heat flow characteristic matrix; Reorganize the heat conduction path based on the heat flow characteristic matrix to generate heat flow distribution data; The electric field distribution of the initial equivalent circuit topology data is analyzed to obtain the electric field distribution characteristics; the corresponding relationship between the heat flow distribution data and the electric field distribution characteristics is analyzed to generate a field mapping matrix; The electric field and temperature field multi-dimensional cross-locking of the initial equivalent circuit topology data is performed according to the field mapping matrix to obtain the temperature influence coefficient; The circuit parameters of the initial equivalent circuit topology data are mapped according to the temperature influence coefficient to obtain the temperature influence parameters; the topology structure of the initial equivalent circuit topology data is updated based on the temperature influence parameters to generate the modified equivalent circuit topology data.

8. The method for constructing a lithium-ion battery equivalent circuit model according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing meshing processing on the modified equivalent circuit model to obtain mesh model data; performing state space discretization on the mesh model data to generate state space data; Step S52: performing circuit parameter mapping on the state space data to obtain circuit simulation data; performing model response simulation on the circuit simulation data based on the modified equivalent circuit model to obtain simulation circuit operation data; Step S53: extracting cycle life characteristics from the simulation circuit operation data to obtain life characteristic data; tracing the capacity attenuation path of the life characteristic data to generate attenuation trajectory data; Step S54: performing parameter attenuation mapping on the attenuation trajectory data to obtain a battery attenuation coefficient; and performing model parameter correction on the modified equivalent circuit model based on the battery attenuation coefficient to generate a lithium-ion battery equivalent circuit model.

9. A system for constructing a lithium-ion battery equivalent circuit model, characterized in that: A method for constructing a lithium-ion battery equivalent circuit model according to claim 1, wherein the system for constructing the lithium-ion battery equivalent circuit model comprises: The electrochemical decoupling module is used to collect the initial battery electrochemical parameters; perform charge density gradient decoupling on the initial battery electrochemical parameters to obtain ion migration characteristic data; perform polarization potential field reconstruction on the ion migration characteristic data to generate the lithium ion battery charge and discharge curve; The potential mapping module is used to perform potential migration analysis on the charge and discharge curves of lithium-ion batteries to generate charge directional distribution data; perform impedance characteristic mapping on the charge directional distribution data to obtain time domain charge mapping data; A topology reconstruction module is used to capture transient voltage of time-domain charge mapping data to obtain voltage distribution data; based on the voltage distribution data, the circuit topology is reconstructed to generate initial equivalent circuit topology data; Thermoelectric correction module is used to obtain circuit temperature distribution data; based on the circuit temperature distribution data, the initial equivalent circuit topology data is cross-locked by electric field and temperature field to obtain the temperature influence coefficient; based on the temperature influence coefficient, the initial equivalent circuit topology data is subjected to thermoelectric joint correction modeling to generate a corrected equivalent circuit model; The aging simulation module is used to perform charge and discharge simulation processing on the modified equivalent circuit model to obtain simulation circuit operation data; based on the simulation circuit operation data, the aging factor is embedded in the modified equivalent circuit model to generate a lithium-ion battery equivalent circuit model.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the method for constructing a lithium-ion battery equivalent circuit model as claimed in any one of claims 1 to 8 is implemented.

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