Parameter identification method for electrochemical-thermal-aging coupled battery simulation model, apparatus, medium and device

Abstract

A parameter identification method for an electrochemical-thermal-aging coupled battery simulation model, comprising: on the basis of the actual structure of a battery, constructing an electrochemical-thermal-aging coupled battery simulation model (S110); acquiring experimental data of the battery at different rates, the experimental data comprising charge and discharge voltage data, temperature data and aging data (S111); and, on the basis of the experimental data and a genetic algorithm, identifying target parameters of the electrochemical-thermal-aging coupled battery simulation model, so as to obtain optimal values of the target parameters (S112).

Description

A method, apparatus, medium, and equipment for parameter identification of an electrochemical-thermal-aging coupled battery simulation model. Cross-references to related applications This application claims priority to Chinese patent application No. 202411726144.5, filed on November 28, 2024, the entire contents of which are incorporated herein by reference. Technical Field This disclosure relates to the field of battery model simulation technology, and in particular to a method, apparatus, medium and equipment for parameter identification of an electrochemical-thermal-aging coupled battery simulation model. Background Technology With the widespread adoption of electric vehicles, the demand for power batteries in the new energy industry is increasing daily. In order to better design power batteries and ensure their quality, it is necessary to first establish a battery model and simulate it (this type of simulated battery model is also called a battery simulation model) to describe the internal reaction process and external characteristics of the power battery, and then use the simulation results to guide the optimized design of the power battery. Power batteries typically use lithium-ion batteries. However, lithium-ion batteries are complex electrochemical systems, but existing lithium-ion battery simulation models cannot accurately reflect the performance state changes of lithium-ion batteries. Therefore, when using existing lithium-ion battery simulation models for parameter identification, the accuracy of the identification of each parameter of the lithium-ion battery model cannot be guaranteed, thus affecting the quality of lithium-ion batteries. Summary of the Invention This disclosure provides a method, apparatus, medium, and equipment for parameter identification of an electrochemical-thermal-aging coupled battery simulation model, in order to solve or partially solve the technical problem in related technologies where the accuracy of parameter identification of battery simulation models cannot be guaranteed, thereby affecting the quality of lithium-ion batteries. A first aspect of this disclosure provides a parameter identification method for an electrochemical-thermal-aging coupled battery simulation model, the method comprising: An electrochemical-thermal-aging coupled battery simulation model was constructed based on the actual battery structure. Acquire experimental data of the battery at different rates; the experimental data includes charge / discharge voltage data, temperature data, and aging data; and Based on the experimental data and the genetic algorithm, the target parameters of the electrochemical-thermal-aging coupled battery simulation model are identified, and the optimal values ​​of each target parameter are obtained. A second aspect of this disclosure provides a parameter identification device for an electrochemical-thermal-aging coupled battery simulation model, the device comprising: Building blocks are used to construct electrochemical-thermal-aging coupled battery simulation models based on the actual battery structure; The acquisition unit is used to acquire experimental data of the battery at different rates; the experimental data includes charge / discharge voltage data, temperature data, and aging data; and The identification unit is used to identify the target parameters of the electrochemical-thermal-aging coupled battery simulation model based on the experimental data and the genetic algorithm, and to obtain the optimal values ​​of each target parameter. A third aspect of this disclosure provides a computer-readable storage medium including a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects. A fourth aspect of this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of any of the methods in the first aspect. Attached Figure Description Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this disclosure. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 shows a flowchart illustrating a parameter identification method for an electrochemical-thermal-aging coupled battery simulation model according to an embodiment of the present disclosure; Figure 2 shows a schematic diagram of the open-circuit voltage (OCV) curve of the negative electrode of a half-cell under 0.01C conditions according to an embodiment of the present disclosure; Figure 3 shows a schematic diagram of the open-circuit voltage curve of the positive electrode of a half-cell under 0.01C conditions according to an embodiment of the present disclosure; Figure 4 shows a schematic diagram of the open-circuit voltage curve of a full cell under 0.01C conditions according to an embodiment of the present disclosure; Figure 5 shows a schematic diagram of the reference voltage curve of the charge-discharge experiment under 0.05C conditions and the simulated voltage curve of the charge-discharge simulation process according to an embodiment of the present disclosure. Figure 6a shows a schematic diagram of the simulated temperature curve and reference temperature curve of constant current charge and discharge under 0.5C conditions according to an embodiment of the present disclosure; Figure 6b shows a schematic diagram of the simulated temperature curve and reference temperature curve of constant current charge and discharge under 1C condition according to an embodiment of the present disclosure. Figure 6c shows a schematic diagram of the simulated temperature curve and reference temperature curve of constant current charge and discharge under 1.5C conditions according to an embodiment of the present disclosure. Figure 6d shows a schematic diagram of the simulated temperature curve and reference temperature curve of constant current charge and discharge under 2C conditions according to an embodiment of the present disclosure. Figure 7a shows a schematic diagram of the simulated voltage curve and reference voltage curve of constant current charge and discharge under 0.5C conditions according to an embodiment of the present disclosure; Figure 7b shows a schematic diagram of the simulated voltage curve and reference voltage curve of constant current charge and discharge under 1C condition according to an embodiment of the present disclosure; Figure 7c shows a schematic diagram of the simulated voltage curve and reference voltage curve of constant current charge and discharge under 1.5C conditions according to an embodiment of the present disclosure; Figure 7d shows a schematic diagram of the simulated voltage curve and reference voltage curve of constant current charge and discharge under 2C conditions according to an embodiment of the present disclosure. Figure 8 shows a schematic diagram of the simulated capacity retention rate and reference capacity retention rate curves of a battery under constant current charge and discharge conditions according to an embodiment of the present disclosure in 2C condition; Figure 9 shows a schematic diagram of the parameter identification device structure of an electrochemical-thermal-aging coupled battery simulation model according to an embodiment of the present disclosure. Embodiments of the present invention This disclosure provides a parameter identification method (hereinafter referred to as the parameter identification method) for an electrochemical-thermal-aging coupled battery simulation model, as shown in Figure 1. The method includes the following steps: S110 is a simulation model of an electrochemical-thermal-aging coupled battery based on the actual structure of the battery. In order to accurately describe the internal reaction process and external characteristics of the battery, this disclosure takes into account the coupling effect between the electrochemical field, the thermal field and the aging reaction, and therefore constructs an electrochemical-thermal-aging coupled battery simulation model to accurately simulate the electrochemical reaction, ion and electron transfer process inside the battery. In some implementations, an electrochemical-thermal-aging coupled battery simulation model is constructed, including: A three-dimensional electrochemical model of a single battery cell at the mesoscale is constructed, and the corresponding first governing equation is set for the three-dimensional electrochemical model; Based on the three-dimensional electrochemical model, a solid electrolyte interface film growth mechanism model for the battery negative electrode is constructed, and a corresponding second governing equation is set for the solid electrolyte interface film growth mechanism model. Construct a three-dimensional thermal model of the battery at a macroscopic scale, and set the corresponding third governing equation for the three-dimensional thermal model; The electrochemical reaction output from the first governing equation is thermally coupled to the third governing equation, and the average temperature output from the third governing equation is coupled to the first governing equation. The local current density on the particle surface output from the first governing equation is coupled to the second governing equation, and the parasitic reaction current output from the second governing equation is coupled to the first governing equation, thus obtaining the electrochemical-thermal-aging coupled battery simulation model. In some implementations, since the battery comprises multiple individual battery cells, when constructing the solid electrolyte interface film growth mechanism model for the battery negative electrode, a corresponding solid electrolyte interface film growth mechanism model can also be constructed for each individual battery cell. Similarly, when constructing the three-dimensional thermal model of the battery, a corresponding three-dimensional thermal model can also be constructed for each individual battery cell. In some implementations, constructing an electrochemical-thermal-aging coupled battery simulation model may further include: A three-dimensional electrochemical model of a single battery cell at the mesoscale is constructed, and the corresponding first governing equation is set for the three-dimensional electrochemical model; Based on the three-dimensional electrochemical model, a solid electrolyte interface film growth mechanism model for the negative electrode of a single battery cell is constructed, and a corresponding second governing equation is set for the solid electrolyte interface film growth mechanism model. A three-dimensional thermal model of a single battery cell at a macroscopic scale is constructed, and a corresponding third governing equation is set for the three-dimensional thermal model; The electrochemical reaction output from the first governing equation is thermally coupled to the third governing equation, and the average temperature output from the third governing equation is coupled to the first governing equation. The local current density on the particle surface output from the first governing equation is coupled to the second governing equation, and the parasitic reaction current output from the second governing equation is coupled to the first governing equation, thus obtaining the electrochemical-thermal-aging coupled battery simulation model. In some implementations, the battery simulation model needs to be constructed according to the actual structure of a real battery. A three-dimensional electrochemical model is used to simulate the three-dimensional geometry of a battery cell formed by stacking current collectors, positive and negative electrode active layers, and separators. The three-dimensional electrochemical model also includes an additional dimension, which is the radius dimension of the electrode particles, used to simulate the ideal spherical electrode particles in the electrode active layer. The three-dimensional thermal model mainly considers the convective heat transfer loss on the battery surface and calculates the battery temperature and heat source distribution. The aging model is primarily used to characterize the aging behavior of lithium-ion batteries, manifested as capacity decay, increased impedance, and reduced actual power. Aging behavior significantly impacts battery lifespan. The main causes of battery aging include the growth of a solid electrolyte interphase (SEI) at the negative electrode interface, the deposition of metallic lithium at the negative electrode, and the loss of active materials. Among these, the growth of the SEI film is the primary aging side reaction; therefore, the aging model only considers the influence of SEI film growth. The aging model is built upon a three-dimensional electrochemical model, assuming that SEI film formation is limited by kinetics and diffusion processes, and that aging slows down with increasing SEI film thickness. Furthermore, the expansion of graphite electrode particles during insertion into the negative electrode can cause SEI film rupture, accelerating aging. Assuming the SEI film formation reaction is a reduction reaction, the reaction rate is faster at lower potentials (i.e., battery state of charge). The SEI film growth side reaction will be coupled into the three-dimensional electrochemical model as parasitic reaction currents, which will affect the current density in the three-dimensional electrochemical model. In this disclosure, the three-dimensional electrochemical model, the three-dimensional thermal model, and the aging model each have a corresponding governing equation, and the coupling between the various models is achieved through each governing equation. In some embodiments, the first governing equations include: a lithium-ion concentration equation for the solid electrode particles, a current density equation for the solid electrode particles, a current density equation for the electrolyte liquid phase, and a local current density equation for the electrode particle surface: wherein, The equation for lithium-ion concentration in solid-phase electrode particles is: (1) The current density equation for solid electrode particles is: (2) The equation for the current density in the liquid phase of the electrolyte is: (3) The local current density equation on the surface of the electrode particles is: (4) (5) (6) Let be the lithium-ion concentration in the solid electrode particles, and r be the radius of the solid electrode particles. Let be the solid-phase diffusion coefficient. The current density of the solid electrode particles is given. The conductivity of the solid-phase electrode particles. For gradient operators, For solid-state potential, This represents the current density in the liquid phase of the electrolyte. Liquid phase conductivity, Let R be the liquid phase potential, T be the ideal gas constant, T be the temperature of the electrochemical-thermal-aging coupled battery simulation model, and F be the Faraday constant. This represents the lithium-ion transference number. The average molar activity coefficient of the electrolyte. This represents the local current density on the surface of the solid electrode particles. For reference exchange current density, The transfer coefficient of the negative electrode (cathode). The transfer coefficient of the positive electrode (positive electrode). This refers to the interfacial overpotential generated by the electrochemical reaction. This is the electrode equilibrium potential. The reaction rate constant is the one at the positive or negative electrode. This refers to the concentration of lithium ions in the liquid phase. The lithium-ion concentration is used as a reference in the liquid phase. This represents the lithium-ion concentration on the solid-phase surface. This represents the maximum lithium-ion concentration in the solid phase. The current density of the solid electrode particles and the current density of the electrolyte liquid phase satisfy the charge conservation law: (7) The current density of solid electrode particles and the local current density on the surface of the electrode particles (also known as the solid-liquid interface reaction current density) satisfy the following relationship: (8) In some embodiments, the second governing equation includes: the parasitic reaction kinetic equation, the overpotential equation, and the membrane resistance equation of the solid electrolyte interface membrane; wherein, The kinetic equation for the parasitic reaction is: (9) The overpotential equation is: (10) The membrane resistance equation for a solid electrolyte interface membrane is: (11) The growth rate equation for solid electrolyte interfacial films is: (12) Let be the local current density on the particle surface of the negative electrode in the battery simulation model corresponding to the parasitic reaction kinetics, HK be the dimensionless graphite expansion factor function, J be the dimensionless exchange current density of the parasitic reaction, and a be the transfer coefficient of the electrochemical reduction reaction. The local current density at a 1C rate, The overpotential generated by the parasitic reaction kinetics To represent the localized charge accumulation caused by the formation of the solid electrolyte interface film, R is the ideal gas constant, T is the temperature of the electrochemical-thermal-aging coupled battery simulation model, F is the Faraday constant, and f is a lumped dimensionless parameter based on the properties of the solid electrolyte interface film. For solid-state potential, The liquid phase potential, This represents the local current density on the surface of the solid electrode particles. represents the specific surface area of ​​the solid electrode particles. The membrane resistance of the solid electrolyte interface membrane. This represents the equilibrium potential for the solid electrolyte interfacial film growth reaction. This represents the volume fraction of the solid electrolyte interfacial membrane. Let be the thickness of the solid electrolyte interface film, and K be the conductivity of the solid electrolyte interface film. In some implementations, the third governing equation is: the overall heat production equation, the Ohmic heat equation, the polarization heat equation, the electrochemical reaction heat equation, the Joule heat equation, and the temperature determination equation; wherein, The overall heat production equation is: (13) The Ohm's heat equation is: (14) The polarization heat equation is: (15) The electrochemical reaction heat equation is: (16) The Joule equation is: (17) The equation for determining temperature is: (18) Q h The total heat generated in the electrochemical-thermal-aging coupled battery simulation model. For Ohm heat, It is the heat of polarization. It is the heat of electrochemical reaction. It is Joule heat. The current density of the solid electrode particles is given. This represents the current density in the liquid phase of the electrolyte. For solid-state potential, The liquid phase potential, For gradient operators, represents the specific surface area of ​​the solid electrode particles. The value represents the local current density on the surface of the solid electrode particles, and T represents the temperature of the electrochemical-thermal-aging coupled battery simulation model. I represents the electrode equilibrium potential, and I represents the total current through the electrode tabs in the electrochemical-thermal-aging coupled battery simulation model. The resistance of the tab is [value]. The volume of the pole ear, For the density of the electrochemical-thermal-aging coupled battery simulation model, The constant-pressure heat capacity is given by the electrochemical-thermal-aging coupled battery simulation model, where m is the thermal conductivity. Let be the convective heat transfer coefficient of the surface of the electrochemical-thermal-aging coupled battery simulation model, and A be the surface area of ​​the electrochemical-thermal-aging coupled battery simulation model that participates in heat transfer. This is a reference temperature. The parameter identification method also includes step S111, which involves acquiring experimental data of the battery at different rates; the experimental data includes charge and discharge voltage data, temperature data, and aging data. After the above electrochemical-thermal-aging coupled battery simulation model is built, it is necessary to obtain experimental data of the battery at different rates. The experimental data includes charge and discharge voltage data, temperature data, and aging data, which will provide a data foundation for the subsequent construction of the objective function required by the genetic algorithm. Battery rate refers to the current required for a battery to discharge its rated capacity within a specified time, usually represented by the letter C. 1C means that the battery is charged and discharged at a current equal to its rated capacity. For example, if the battery capacity is 2Ah (ampere-hours), then discharging it with a current of 2A (amps) results in a discharge rate of 1C; if it is discharged with a current of 4A, the discharge rate is 2C. The parameter identification method also includes step S112, which identifies each target parameter of the electrochemical-thermal-aging coupled battery simulation model based on the experimental data and the genetic algorithm, and obtains the optimal value of each target parameter. After obtaining the experimental data, the target parameters of the electrochemical-thermal-aging coupled battery simulation model can be identified based on the experimental data and the genetic algorithm to obtain the optimal values ​​of each target parameter, including: The target parameters of the three-dimensional electrochemical model are encoded using a preset encoding method to generate a first initial population. The first initial population contains multiple first individuals, each representing a set of solutions for the target parameters of the three-dimensional electrochemical model. In each iteration, a first objective function is constructed based on the experimental data and simulation data of the three-dimensional electrochemical model. The fitness value of each first individual is calculated using the first objective function. Parents are selected based on the fitness value of each first individual, and crossover operations are performed on the parents to generate new offspring, resulting in a new population. The above iterative process is repeated until the iteration conditions are met, and the optimal first individual is output. The optimal first individual is the optimal solution for each target parameter of the three-dimensional electrochemical model. The optimal solutions of each objective parameter of the three-dimensional electrochemical model are substituted into the three-dimensional electrochemical model. The objective parameters of the three-dimensional thermal model are encoded using a preset encoding method to generate a second initial population. The second initial population contains multiple second individuals, each representing a set of solutions for the objective parameters of the three-dimensional thermal model. In each iteration, a second objective function is constructed based on the experimental data and simulation data of the three-dimensional thermal model. The fitness value of each second individual is calculated using the second objective function. Parents are selected based on the fitness value of each second individual, and crossover operations are performed on the parents to generate new offspring, resulting in a new population. The iteration process is repeated until the iteration conditions are met, and the optimal second individual is output. The optimal second individual is the optimal solution for each objective parameter of the three-dimensional thermal model. The optimal solutions of each objective parameter of the 3D thermal model are substituted into the 3D thermal model. The objective parameters of the aging model are encoded using a preset encoding method to generate a third initial population. The third initial population contains multiple third individuals, each representing a set of solutions for the objective parameters of the aging model. In each iteration, a third objective function is constructed based on the experimental data and simulation data of the aging model. The fitness value of each third individual is calculated using the third objective function. Parents are selected based on the fitness value of each third individual, and crossover operations are performed on the parents to generate new offspring, resulting in a new population. The above iterative process is repeated until the iteration conditions are met, and the optimal third individual is output. The optimal third individual is the optimal solution for each objective parameter of the aging model. In some embodiments, since the electrochemical-thermal-aging coupled battery simulation model includes many parameters to be identified, this disclosure first identifies the target parameters of the three-dimensional electrochemical model, then substitutes the identified parameter values ​​into the three-dimensional electrochemical model, then identifies the target parameters of the three-dimensional thermal model, substitutes the identified parameters into the three-dimensional thermal model, and finally identifies the target parameters of the aging model. When using genetic algorithms to identify the target parameters of each model, the identification steps are the same, except that the experimental data and objective functions required are different. Taking a three-dimensional electrochemical model as an example, the target parameters in a three-dimensional electrochemical model include many. To ensure identification accuracy and improve identification efficiency, this disclosure classifies the target parameters of the three-dimensional electrochemical model into three categories based on their sensitivity to different operating conditions: Category I parameters, Category II parameters, and Category III parameters. Category I parameters are lithium intercalation parameters, including: maximum lithium intercalation at the battery positive electrode, maximum lithium intercalation at the battery negative electrode, minimum lithium intercalation at the battery positive electrode, and minimum lithium intercalation at the battery negative electrode. Category II parameters are electrode capacity parameters, including: volume fraction of the battery positive electrode, maximum lithium-ion concentration of the battery positive electrode, volume fraction of the battery negative electrode, and maximum lithium-ion concentration of the battery negative electrode. Category III parameters are battery impedance influence parameters, including: electrolyte conductivity, diffusion coefficient, and electrode exchange current density. In some implementations, a first objective function is constructed based on experimental data and simulation data from a three-dimensional electrochemical model, including: Construct the first objective function corresponding to the first type of parameters according to formula (19): (19) Construct the first objective function corresponding to the second type of parameters according to formula (20): (20) Construct the first objective function corresponding to the third type of parameters according to formula (21): (twenty one) in, For the first type of parameter, This represents the maximum lithium intercalation at the positive electrode of the battery. This represents the maximum lithium intercalation at the positive electrode of the battery. The minimum lithium intercalation point for the positive electrode of the battery. The minimum lithium insertion point for the negative electrode of the battery is represented by N, where N is the number of data points and s is the data point number. This is the reference voltage corresponding to a 0.01x charging rate. The simulated voltage corresponds to a charging rate of 0.01. This is the reference voltage corresponding to a 0.01x discharge rate. The simulated voltage is given under a 0.01x discharge rate condition. For the second type of parameter, This represents the volume fraction of the battery's positive electrode. This represents the volume fraction of the battery's positive electrode. This represents the maximum lithium-ion concentration at the positive electrode of the battery. This represents the maximum lithium-ion concentration at the negative electrode of the battery. This is the reference charging voltage corresponding to a 0.05x rate operating condition. The simulated voltage corresponds to a charging rate of 0.05. This is the reference voltage corresponding to a 0.05x discharge rate. This represents the simulated voltage under a 0.05x discharge rate condition. For the third type of parameter, The electrolyte conductivity, The diffusion coefficient is... This represents the exchange current density at the positive electrode of the battery. This refers to the exchange current density at the negative electrode of the battery. This is the reference voltage corresponding to a 0.5x charging rate. This is the simulated voltage corresponding to a 0.5x charging rate. This is the reference voltage corresponding to a 0.5x discharge rate. This is the simulated voltage corresponding to a 0.5x discharge rate. This is the reference voltage corresponding to a 2x charging rate. This is the reference voltage corresponding to a 2x discharge rate. This is the simulated voltage under a 2x charging rate condition. This represents the simulated voltage under a 2x discharge rate condition; "full" represents the full-cell mode. This represents any coordinate point in the battery simulation model in full-battery mode. in, include , include , include . In some implementations, for lithium-ion batteries, the open-circuit voltage (OCV) reaches the lower cutoff voltage at state of charge (SOC) = 0 and the upper cutoff voltage at SOC = 1. The OCV curve in a three-dimensional electrochemical model is typically expressed as a function of the state-of-lithiation (SOL) of the electrode. After the electrode materials are fabricated into a finished battery, only a portion of the battery's theoretical capacity is utilized. To prevent lithium plating, the negative electrode graphite is usually not fully intercalated; similarly, the positive electrode material is not fully intercalated due to the formation of the SEI film. Furthermore, the electrode materials are thermodynamically unstable in the low-intercalation state, and therefore do not completely deintercalate. Therefore, the actual OCV range of the electrode is not the theoretical range obtained through half-cell measurements, but rather requires re-identifying the electrode OCV operating range when simulating a full cell, using a multi-objective genetic algorithm to identify the first type of parameter. The steps for identifying the first type of parameters using a multi-objective genetic algorithm are as follows: Step (1): A half-cell experiment was conducted to obtain the open-circuit voltage (OCV) curves of the positive and negative electrodes. The negative electrode used in the experiment was a silicon-carbon composite electrode. The presence of silicon caused voltage hysteresis in the composite electrode during charging and discharging. The positive electrode OCV curve is shown in Figure 2, and the negative electrode OCV curve is shown in Figure 3. In Figure 2, label 21 represents the OCV curve under negative electrode charging conditions, and label 22 represents the OCV curve under negative electrode discharging conditions. Figure 3 shows the OCV curve under positive electrode discharging conditions. In Figures 2 and 3, the horizontal axis represents the state of charge (SOC), and the vertical axis represents the battery voltage. For the first data point, This is the 101st data point. Step (2) involves conducting a 0.01C constant current charge-discharge experiment on the full battery, and the resulting OCV curve is shown in Figure 4. The charge-discharge data at this time includes: the reference charging voltage under the 0.01C charging condition. Discharge voltage under 0.01C discharge condition . In Figure 4, label 41 represents the OCV curve under full battery charging conditions, and label 42 represents the OCV curve under full battery discharging conditions. Using the open-circuit voltage (OCV) curves of the positive and negative terminals determined in step (1), the following can be calculated: and : (twenty two) (twenty three) in, This is the simulated voltage at the positive terminal of the battery. The simulated charging voltage for the negative terminal of the battery. Simulated discharge voltage of the battery negative terminal. Step (3) specifies For the parameters to be identified, , As a reference charging and discharging voltage , As the simulated charging and discharging voltage, the first objective function in formula (19) is constructed. The error between the reference charging and discharging voltage data and the simulated charging and discharging voltage data is used as the objective function. The first type of parameter is used as the variable. The genetic algorithm is used to continuously iterate and calculate to obtain the specific value of the first parameter corresponding to the minimum value of the first objective function, and to complete the identification of the first type of parameter. Once the first type of parameter identification is completed, the identified parameters will be... The specific values ​​are taken as known values, equivalent to the known electrode operating range. Then, a single-objective genetic algorithm is used to identify the second type of parameters under low-magnification (0.5C) conditions. The steps are as follows: Step (1): Conduct a 0.05C constant current charge-discharge experiment on the full battery, using the charge-discharge voltage data under 0.05C conditions as the reference charge-discharge voltage. The reference voltage curve during the charge-discharge experiment and the simulated voltage curve during the charge-discharge simulation are shown in Figure 5. In Figure 5, mark 51 represents the simulated charge voltage curve, and mark 52 represents the reference voltage curve. The reference voltage curve refers to the reference voltage curve corresponding to the charge-discharge experiment on the battery under 0.05C conditions, and the simulated voltage curve refers to the simulated voltage curve corresponding to the charge-discharge experiment on the battery under 0.05C conditions. In Figure 5, the horizontal axis represents time, and the vertical axis represents voltage. The upward-trending curve in Figure 5 is the voltage curve under charging conditions, and the downward-trending curve is the voltage curve under discharging conditions. Step (2) specifies For the parameters to be identified, and As a reference charging and discharging voltage, and As a simulated charge and discharge voltage. Using the error between the reference voltage data and the simulated voltage data under the 0.05C constant current charge and discharge condition in full battery mode as the objective function, and the second type of parameter as the variable, the genetic algorithm is used to iteratively calculate and obtain the specific value of the second type of parameter corresponding to the minimum value of the first objective function, thus completing the identification of the second type of parameter. After the second type of parameter identification is completed, the identified parameters will be... Using the specific value as a known value, we continue to identify the third type of parameter, following these steps: Step (1): Perform charge and discharge experiments on the full battery at 0.5C and 2C constant current, and use the charge and discharge voltage data under 0.5C and 2C conditions as reference charge and discharge voltages. Step (2) specifies For the parameters to be identified, , As a reference charging voltage and As a reference discharge voltage, and As a simulated charging voltage and As a simulated discharge voltage. Using the error between the reference voltage data and the simulated voltage data under 0.5C and 2C constant current charge and discharge conditions in full battery mode as the objective function, and the third type of parameter as the variable, the genetic algorithm is used to iteratively calculate and obtain the specific value of the third type of parameter corresponding to the minimum value of the first objective function, thus completing the identification of the third type of parameter. After the parameters to be identified in the three-dimensional electrochemical model are identified, the target parameters of the three-dimensional thermal model are then identified. In some implementations, the target parameters of the three-dimensional thermal model include the overall entropy heat coefficient and the convective heat transfer coefficient. A second objective function is constructed based on the experimental data and simulation data of the three-dimensional thermal model, including: Construct the second objective function according to formula (24): (twenty four) The target parameters for the three-dimensional thermal model, The overall entropy heat coefficient, Here, N is the convective heat transfer coefficient, N is the number of data points, and s is the data point number. This is the reference temperature corresponding to a 0.5x charging rate. The simulated temperature corresponds to a 0.5x charging rate. This is the reference temperature corresponding to a 2x charging rate. This is the reference temperature corresponding to a 2x charging rate. This is the reference temperature corresponding to a 0.5x discharge rate. The simulated temperature corresponds to a 0.5x discharge rate. This is the reference temperature corresponding to a 2x discharge rate. This represents the simulated temperature under a 2x discharge rate condition. include . Then, following the same method as above, a multi-objective genetic algorithm is used to identify the target parameters of the three-dimensional thermal model. The temperature error under 0.5C and 2C constant current charge and discharge conditions in full battery mode is used as the objective function (the temperature error is the error between the reference temperature data and the simulated voltage and temperature). The target parameters of the three-dimensional thermal model are used as variables. The multi-objective genetic algorithm is used to iteratively calculate and obtain the specific value of the target parameter corresponding to the minimum value of the second objective function, thus completing the identification of the target parameters of the three-dimensional thermal model. After the target parameters of the three-dimensional thermal model are identified, the target parameters of the aging model are further identified. In some implementations, the target parameters of the aging model include the overall entropy heat coefficient and the convective heat transfer coefficient; a third objective function is constructed based on the experimental data and simulation data of the aging model, including: Construct the third objective function according to formula (25): (25) The target parameters for the aging model are... The thickness of the solid electrolyte interface film. Here, represents the local current density on the surface of the solid electrode particles, N is the number of data points, and s is the data point number. This represents the reference solid electrolyte interface film resistance under a 0.5x charging rate. The simulated solid electrolyte interface film resistance is given under a 0.5x charging rate. This represents the reference solid electrolyte interfacial film resistance under 0.5x discharge conditions. The simulated solid electrolyte interface film resistance is given under a 0.5x discharge rate condition. This represents the reference solid electrolyte interface film resistance under a 2x charging condition. The simulated solid electrolyte interface film resistance is given under a 2x charging condition. This represents the reference solid electrolyte interface film resistance under 2x discharge conditions. This represents the simulated solid electrolyte interface film resistance under 2x discharge conditions. include . Then, following the same method described above, a multi-objective genetic algorithm is used to identify the target parameters of the three-dimensional thermal model. The solid electrolyte interface film resistance error under 0.5C and 2C constant current charge and discharge conditions in full-cell mode is used as the objective function (the solid electrolyte interface film resistance error is the error between the reference solid electrolyte interface film resistance and the simulated solid electrolyte interface film resistance). The target parameters of the aging model are used as variables. The multi-objective genetic algorithm is used to iteratively calculate and obtain the specific value of the target parameter corresponding to the minimum value of the third objective function, thus completing the identification of the target parameters of the aging model. To verify the accuracy of the electrochemical-thermal-aging coupled battery simulation model after parameter identification, this disclosure also verifies the identified electrochemical-thermal-aging coupled battery simulation model at various rates. The validation curves for the three-dimensional electrochemical model are shown in Figures 6a to 6d. In Figure 6a, 61 represents the simulated temperature curve, and 62 represents the reference temperature curve. In Figure 6b, 63 represents the simulated temperature curve, and 64 represents the reference temperature curve. In Figure 6c, 65 represents the simulated temperature curve, and 66 represents the reference temperature curve. In Figure 6d, 67 represents the simulated temperature curve, and 68 represents the reference temperature curve. In Figures 6a to 6d, the horizontal axis represents time, and the vertical axis represents temperature, in K. Figure 6a represents the reference temperature and the simulated temperature under 0.5C charge / discharge conditions; Figure 6b represents the reference temperature and the simulated temperature under 1C charge / discharge conditions; Figure 6c represents the reference temperature and the simulated temperature under 1.5C charge / discharge conditions; and Figure 6d represents the reference temperature and the simulated temperature under 2C charge / discharge conditions. In Figures 6a to 6d, from left to right, the first segment of the curve with an upward trend is the temperature curve under charging conditions, the second segment of the curve with a downward trend is the temperature curve under static conditions, and the third segment of the curve with an upward trend is the temperature curve under discharging conditions. As can be seen from Figures 6a to 6d, the temperature curves fit well at each rate. The maximum error occurs in the initial stages of charging and discharging. This is because the experimental negative electrode material is silicon-carbon, and the OCV curve exhibits voltage hysteresis. The root mean square error (RSME) of the voltage at each rate (the error between the reference voltage and the simulated voltage) is shown in Table 1. Table 1 In Table 1, the root mean square error of the voltage at each multiplier meets the error requirements. The verification curves for the three-dimensional thermal model are shown in Figures 7a to 7d. At low rates (e.g., 0.5C and 1C), the temperature curves fluctuate significantly, while at high rates (1.5C and 2C), the curves fit better. The maximum error occurs at the end of charging or discharging. The RSME error at each rate is shown in Table 2. The maximum RSME error occurs at the 2C rate, at 1.52℃, which also meets the error requirements.

[0001] In Figure 7a, label 71 represents the reference voltage under 0.5C charge / discharge conditions, and label 72 represents the simulated voltage under 0.5C charge / discharge conditions; in Figure 7b, label 73 represents the reference voltage under 1C charge / discharge conditions, and label 74 represents the simulated voltage under 1C charge / discharge conditions; in Figure 7c, label 75 represents the reference voltage under 1.5C charge / discharge conditions, and label 76 represents the simulated voltage under 1.5C charge / discharge conditions; in Figure 7d, label 77 represents the reference voltage under 2C charge / discharge conditions, and label 78 represents the simulated voltage under 2C charge / discharge conditions. In Figures 7a to 7d, from left to right, the first segment of the curve with an upward trend is the voltage curve under charging conditions, the second segment of the curve with a flat trend is the voltage curve under resting conditions, and the third segment of the curve with a downward trend is the voltage curve under discharging conditions. Table 2 The verification curves for the aging model are shown in Figure 8. The error of the SEI film resistance at 1x rate is shown in Table 3. The maximum root mean square error is 0.005, which meets the error requirements. In Figure 8, the horizontal axis represents the number of cycles, the vertical axis represents the time capacity retention rate, label 81 represents the reference capacity retention rate under 1C discharge condition, and label 82 represents the simulated capacity retention rate under 1C discharge condition. Table 3 In summary, the target parameters identified in this disclosure can meet the accuracy requirements. Based on the same inventive concept as in the foregoing embodiments, this disclosure also provides a parameter identification device for an electrochemical-thermal-aging coupled battery simulation model. As shown in Figure 9, the parameter identification device for the electrochemical-thermal-aging coupled battery simulation model includes: Building unit 91 is used to construct an electrochemical-thermal-aging coupled battery simulation model based on the actual battery structure; Acquisition unit 92 is used to acquire experimental data of the battery at different rates; the experimental data includes charge / discharge voltage data, temperature data, and aging data; and The identification unit 93 is used to identify the target parameters of the electrochemical-thermal-aging coupled battery simulation model based on experimental data and genetic algorithm, and to obtain the optimal values ​​of each target parameter. Since the apparatus described in this disclosure is used for parameter identification of the electrochemical-thermal-aging coupled battery simulation model according to the embodiments of this disclosure, those skilled in the art can understand the specific structure and variations of the apparatus based on the method described in this disclosure, and therefore will not be repeated here. All apparatuses used in the methods of this disclosure fall within the scope of protection of this disclosure. Based on the same inventive concept, this disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any step of the method described above. Based on the same inventive concept, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above. Through one or more embodiments of this disclosure, the present disclosure has the following beneficial effects or advantages: This disclosure provides a method, apparatus, medium, and device for parameter identification of an electrochemical-thermal-aging coupled battery simulation model. The parameter identification method for this electrochemical-thermal-aging coupled battery simulation model includes: An electrochemical-thermal-aging coupled battery simulation model was constructed based on the actual battery structure. Experimental data of the battery at different rates were obtained, including charge / discharge voltage data, temperature data, and aging data. Based on the experimental data and a genetic algorithm, the target parameters of the electrochemical-thermal-aging coupled battery simulation model were identified, and the optimal values ​​of each target parameter were obtained. In this way, because the coupling effect between the electrochemical field, thermal field, and aging reaction is considered, the electrochemical reactions and ion and electron transfer processes inside the battery can be accurately simulated, thus accurately reflecting changes in the battery's performance state. When using the genetic algorithm to identify the parameters of the coupled battery simulation model, the strong global optimization ability of the genetic algorithm ensures the accuracy of the optimal solution, the accuracy of parameter identification in the battery simulation model, and the quality of the battery. Although preferred embodiments of this disclosure have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this disclosure. The above description is merely a preferred embodiment of this disclosure and is not intended to limit the scope of protection of this disclosure. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A parameter identification method for an electrochemical-thermal-aging coupled battery simulation model, comprising: An electrochemical-thermal-aging coupled battery simulation model was constructed based on the actual battery structure. Acquire experimental data of the battery at different rates, including charge / discharge voltage data, temperature data, and aging data. Based on the experimental data and the genetic algorithm, the target parameters of the electrochemical-thermal-aging coupled battery simulation model are identified, and the optimal values ​​of each target parameter are obtained.

2. The method as described in claim 1, wherein, The construction of the electrochemical-thermal-aging coupled battery simulation model includes: A three-dimensional electrochemical model of a single battery cell at the mesoscale is constructed, and a corresponding first governing equation is set for the three-dimensional electrochemical model; Based on the three-dimensional electrochemical model, a solid electrolyte interface film growth mechanism model for the battery negative electrode is constructed, and a corresponding second governing equation is set for the solid electrolyte interface film growth mechanism model. A three-dimensional thermal model of the battery at a macroscopic scale is constructed, and a corresponding third governing equation is set for the three-dimensional thermal model; The electrochemical reaction thermal output of the first governing equation is coupled to the third governing equation, and the average temperature output of the third governing equation is coupled to the first governing equation; the local current density of the particle surface output of the first governing equation is coupled to the second governing equation, and the parasitic reaction current output of the second governing equation is coupled to the first governing equation, thus obtaining the electrochemical-thermal-aging coupled battery simulation model.

3. The method as described in claim 2, wherein, The first governing equations include: the lithium-ion concentration equation for the solid electrode particles, the current density equation for the solid electrode particles, the current density equation for the electrolyte liquid phase, and the local current density equation for the electrode particle surface: wherein, The equation for lithium-ion concentration in solid-phase electrode particles is: ； The current density equation for the solid electrode particles is: ； The current density equation for the electrolyte liquid phase is: ； The local current density equation on the surface of the electrode particles is: ;in, The The lithium-ion concentration in the solid electrode particles is denoted by r, where r is the radius of the solid electrode particles. The solid-phase diffusion coefficient is denoted as . The current density of the solid electrode particles is... The conductivity of the solid electrode particles is given by the following formula: For the gradient operator, the For solid-state potential, the The current density of the electrolyte liquid phase, the The liquid phase conductivity, the The liquid phase potential is given by R, the ideal gas constant is given by T, the temperature of the electrochemical-thermal-aging coupled battery simulation model is given by F, and F is the Faraday constant is given by F. The lithium-ion transference number, the The average molar activity coefficient of the electrolyte, the The local current density on the surface of the solid electrode particles, the For reference exchange current density, the The cathode transfer coefficient, the The transfer coefficient of the anode, the The exp is the interfacial overpotential generated by the electrochemical reaction, and exp is an exponential function.

4. The method of claim 1, wherein, The electrochemical-thermal-aging coupled battery simulation model includes: a three-dimensional electrochemical model, a three-dimensional thermal model, and an aging model; the identification of each target parameter of the electrochemical-thermal-aging coupled battery simulation model based on the experimental data and a genetic algorithm includes: The target parameters of the three-dimensional electrochemical model are encoded using a preset encoding method to generate a first initial population. This first initial population contains multiple first individuals, each representing a set of solutions for the target parameters of the three-dimensional electrochemical model. In each iteration, a first objective function is constructed based on the experimental and simulation data of the three-dimensional electrochemical model. The fitness value of each first individual is calculated using the first objective function. Parents are selected based on the fitness values ​​of each first individual, and crossover operations are performed on these parents to generate new offspring, resulting in a new population. This iterative process is repeated until the iteration conditions are met, and the optimal first individual is output. The optimal first individual represents the optimal solution for each target parameter of the three-dimensional electrochemical model. The optimal solutions of each objective parameter of the three-dimensional electrochemical model are substituted into the three-dimensional electrochemical model. The objective parameters of the three-dimensional thermal model are encoded using a preset encoding method to generate a second initial population. The second initial population contains multiple second individuals, each representing a set of solutions for the objective parameters of the three-dimensional thermal model. In each iteration, a second objective function is constructed based on the experimental data and simulation data of the three-dimensional thermal model. The fitness value of each second individual is calculated using the second objective function. Parents are selected based on the fitness values ​​of each second individual, and crossover operations are performed on the parents to generate new offspring, resulting in a new population. The above iterative process is repeated until the iteration conditions are met, and the optimal second individual is output. The optimal second individual is the optimal solution for each objective parameter of the three-dimensional thermal model. The optimal solutions of each objective parameter of the three-dimensional thermal model are substituted into the three-dimensional thermal model. The objective parameters of the aging model are encoded using a preset encoding method to generate a third initial population. The third initial population contains multiple third individuals, each representing a set of solutions for the objective parameters of the aging model. In each iteration, a third objective function is constructed based on the experimental data and simulation data of the aging model. The fitness value of each third individual is calculated using the third objective function. Parents are selected based on the fitness values ​​of each third individual, and crossover operations are performed on the parents to generate new offspring, resulting in a new population. The above iterative process is repeated until the iteration conditions are met, and the optimal third individual is output. The optimal third individual is the optimal solution for each objective parameter of the aging model.

5. The method of claim 4, wherein, The target parameters of the three-dimensional electrochemical model include first-type parameters, second-type parameters, and third-type parameters; the construction of the first objective function based on the experimental data and simulation data of the three-dimensional electrochemical model includes: According to the formula Construct the first objective function corresponding to the first type of parameters; According to the formula Construct the first objective function corresponding to the second type of parameters; According to the formula Construct the first objective function corresponding to the third type of parameters; wherein, The For the first type of parameter, the For the maximum lithium intercalation of the battery positive electrode, the For the maximum lithium intercalation of the battery positive electrode, the The minimum lithium intercalation point for the positive electrode of the battery. The minimum lithium intercalation point for the negative electrode of the battery, where N is the number of data points, and s is the data point number. The reference voltage corresponding to the 0.01x charging rate condition is... The simulated voltage corresponding to a 0.01x charging rate is... The reference voltage corresponding to the 0.01x discharge rate condition is... The simulated voltage corresponding to a discharge rate of 0.01 is... For the second type of parameter, the The volume fraction of the positive electrode of the battery, the The volume fraction of the positive electrode of the battery, the The maximum lithium-ion concentration at the positive electrode of the battery, This represents the maximum lithium-ion concentration at the negative electrode of the battery. This is the reference charging voltage corresponding to a 0.05x rate operating condition. The simulated voltage corresponds to a charging rate of 0.

05. This is the reference voltage corresponding to a 0.05x discharge rate. The simulated voltage corresponding to a 0.05x discharge rate is... For the third type of parameter, the The electrolyte conductivity is... The diffusion coefficient is the stated value. The exchange current density at the positive electrode of the battery, This refers to the exchange current density at the negative electrode of the battery. This is the reference voltage corresponding to a 0.5x charging rate. This is the simulated voltage corresponding to a 0.5x charging rate. This is the reference voltage corresponding to a 0.5x discharge rate. This is the simulated voltage corresponding to a 0.5x discharge rate. This is the reference voltage corresponding to a 2x charging rate. This is the reference voltage corresponding to a 2x discharge rate. This is the simulated voltage under a 2x charging rate condition. This represents the simulated voltage under a 2x discharge rate condition; "full" represents the full-cell mode. This represents any coordinate point in the battery simulation model in full-battery mode.

6. The method of claim 4, wherein, The target parameters of the three-dimensional thermal model include the overall entropy heat coefficient and the convective heat transfer coefficient; the construction of the second objective function based on the experimental data and simulation data of the three-dimensional thermal model includes: According to the formula Construct the second objective function; where, The The target parameters of the three-dimensional thermal model are... The overall entropy-heat coefficient, the The convective heat transfer coefficient is given by N, where N is the number of data points and s is the data point number. This is the reference temperature corresponding to a 0.5x charging rate. The simulated temperature corresponds to a 0.5x charging rate. This is the reference temperature corresponding to a 2x charging rate. This is the reference temperature corresponding to a 2x charging rate. This is the reference temperature corresponding to a 0.5x discharge rate. The simulated temperature corresponds to a 0.5x discharge rate. This is the reference temperature corresponding to a 2x discharge rate. This represents the simulated temperature under a 2x discharge rate.

7. The method of claim 4, wherein, The target parameters of the aging model include the overall entropy heat coefficient and the convective heat transfer coefficient; the construction of the third objective function based on the experimental data and simulation data of the aging model includes: According to the formula Construct the third objective function; where, The The target parameters of the aging model are... The thickness of the solid electrolyte interface film, the The local current density on the surface of the solid electrode particles is N, where N is the number of data points and s is the data point number. The reference solid electrolyte interface film resistance is given under a 0.5x charging rate condition. The simulated solid electrolyte interface film resistance under a 0.5x charging rate is given. The reference solid electrolyte interface film resistance is given under a 0.5x discharge rate condition. The simulated solid electrolyte interface film resistance under 0.5x discharge conditions is described below. The reference solid electrolyte interface film resistance under a 2x charging condition is described. The simulated solid electrolyte interface film resistance under a 2x charging condition is described below. The reference solid electrolyte interface film resistance under 2x discharge conditions is described. The simulated solid electrolyte interface membrane resistance is given under a 2x discharge condition.

8. A parameter identification device for an electrochemical-thermal-aging coupled battery simulation model, comprising: Building blocks are used to construct electrochemical-thermal-aging coupled battery simulation models based on the actual battery structure; The acquisition unit is used to acquire experimental data of the battery at different rates; the experimental data includes charge / discharge voltage data, temperature data, and aging data. The identification unit is used to identify the target parameters of the electrochemical-thermal-aging coupled battery simulation model based on the experimental data and the genetic algorithm, and to obtain the optimal values ​​of each target parameter.

9. A computer-readable storage medium comprising a computer program stored thereon, which, when executed by a processor, implements the steps of the method according to any one of claims 1-7.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, performs the steps of the method according to any one of claims 1-7.

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