Battery simulation model construction method and device, equipment and medium

Abstract

The application discloses a battery simulation model construction method, device, equipment and medium, and belongs to the technical field of new energy storage. The method comprises the following steps: obtaining cell parameters, charging and discharging data and production line statistical data of a battery; the battery comprises a plurality of single cells connected in series and in parallel; a three-dimensional thermal model and a physical and chemical model of a single cell are established according to the cell parameters; the dynamics parameters in the physical and chemical model of each single cell are calibrated according to the charging and discharging data by using a genetic algorithm, and a plurality of single cell pseudo two-dimensional models are obtained; a multi-cell coupling model is established according to the plurality of single cell pseudo two-dimensional models and the series-parallel connection topological structure of the battery; the multi-cell coupling model is corrected according to the production line statistical data by using a Monte Carlo simulation method, and a multi-cell electrochemical model of the battery is obtained; and the multi-cell electrochemical model and the three-dimensional thermal model are coupled to obtain a simulation model of the battery. The application can solve the problem of low precision of existing cell simulation.

Description

Technical Field

[0001] This invention relates to the field of new energy storage technology, and in particular to a method, apparatus, equipment and medium for constructing a battery simulation model. Background Technology

[0002] With the gradual development of the new energy industry, greater attention is being paid to the performance parameters of lithium batteries. During the research and design phase of battery systems, by constructing high-fidelity mathematical models, it is possible to accurately predict and conduct multi-dimensional performance evaluations of the electrochemical behavior, thermal field distribution, structural stress, and aging trends of power battery systems (battery packs) under different operating conditions before the manufacture of physical prototypes.

[0003] Currently, in order to improve computational efficiency, methods are used to greatly simplify the cell model into an equivalent circuit model or an empirical model. However, this simplification leads to a decrease in the accuracy of cell simulation, making it impossible to accurately evaluate the overall performance of the battery. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for constructing a battery simulation model, aiming to solve the problem of low accuracy in existing battery cell simulations.

[0005] A first aspect of the present invention provides a method for constructing a battery simulation model, comprising: Obtain battery cell parameters, charge / discharge data, and production line statistics; the battery comprises multiple individual cells connected in series and parallel. Based on the cell parameters, a three-dimensional thermal model and a physicochemical model of a single cell were established. Using a genetic algorithm, the kinetic parameters in the physicochemical model of each single cell are calibrated based on the charge and discharge data to obtain multiple pseudo-two-dimensional models of single cells; Based on the pseudo-two-dimensional models of the multiple single cells and the series-parallel topology of the battery, a multi-cell coupling model is established. Using the Monte Carlo simulation method, the multi-cell coupling model is corrected based on the production line statistics to obtain the multi-cell electrochemical model of the battery. The multi-cell electrochemical model and the three-dimensional thermal model are coupled to obtain the simulation model of the battery.

[0006] A second aspect of the present invention provides an apparatus for constructing a battery simulation model, the apparatus comprising: The acquisition module is used to acquire the battery cell parameters, charge and discharge data, and production line statistics; the battery includes multiple single cells connected in series and parallel. The first modeling module is used to establish a three-dimensional thermal model and a physicochemical model of a single cell based on the cell parameters. The calibration module is used to calibrate the kinetic parameters in the physicochemical model of each single cell based on the charge and discharge data using a genetic algorithm, thereby obtaining multiple pseudo-two-dimensional models of single cells. The second modeling module is used to establish a multi-cell coupling model based on the multiple single-cell pseudo-two-dimensional models and the series-parallel topology of the battery. The correction module is used to correct the multi-cell coupling model based on the production line statistics using the Monte Carlo simulation method, so as to obtain the multi-cell electrochemical model of the battery. The third modeling module is used to couple the multi-cell electrochemical model and the three-dimensional thermal model to obtain the simulation model of the battery.

[0007] A third aspect of the present invention provides an electronic device comprising: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a method for constructing a battery simulation model as described in any of the foregoing embodiments.

[0008] A fourth aspect of the present invention provides a readable storage medium that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute any of the aforementioned battery simulation model construction methods.

[0009] In this invention, the physicochemical model of a single battery cell is calibrated using a genetic algorithm, which not only reduces the manpower and time costs of modeling but also improves the accuracy of the pseudo-two-dimensional model of the single battery cell. Secondly, production line statistical data is introduced, and the multi-cell coupling model is corrected using the Monte Carlo simulation method, so that the multi-cell coupling model can truly reflect the manufacturing consistency differences of the battery cells, thus improving the accuracy of the multi-cell electrochemical model. Finally, by coupling the thermal model and the electrochemical model, the interaction between temperature and electrical performance can be accurately predicted, thus improving the accuracy of the simulation model. Attached Figure Description

[0010] Figure 1 This is a flowchart of a method for constructing a battery simulation model provided in an embodiment of this application.

[0011] Figure 2 This is a flowchart illustrating the specific steps of a method for constructing a battery simulation model provided in an embodiment of this application.

[0012] Figure 3 This is a schematic diagram of a series-parallel topology of a battery provided in an embodiment of this application.

[0013] Figure 4 This is a schematic diagram of the three-dimensional temperature distribution of a battery provided in an embodiment of this application.

[0014] Figure 5 This is a schematic diagram of a model coupling process provided in an embodiment of this application.

[0015] Figure 6 This is a voltage curve of a battery cell output from a battery simulation model provided in an embodiment of this application.

[0016] Figure 7 This is a state-of-charge curve output by a battery simulation model provided in this application embodiment.

[0017] Figure 8 This is a schematic diagram of another method for constructing a battery simulation model provided in an embodiment of this application.

[0018] Figure 9 This is a block diagram of a battery simulation model construction device provided in an embodiment of this application.

[0019] Figure 10 This is a block diagram of an electronic device provided in an embodiment of this application.

[0020] Figure 11 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0022] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0023] First, let's describe one application scenario related to this invention.

[0024] With the advancement of global energy structure adjustment and carbon neutrality goals, electric vehicles and large-scale energy storage systems are experiencing explosive growth. As their core power source, the performance, safety, and lifespan of lithium-ion power battery systems directly determine the vehicle's range, reliability, and user experience. A power battery system (battery pack) is a complex electro-thermal-mechanical multi-physics coupled system. It is not a simple stacking of individual cells, but rather an assembly containing hundreds or even thousands of cells, and integrating a Battery Management System (BMS), a Battery Thermal Management System (BTMS), electrical connection modules, and mechanical structures.

[0025] Simulation technology has become an indispensable core tool in the research and design phase of battery systems. By constructing high-fidelity mathematical models, the electrochemical behavior, thermal field distribution, structural stress, and aging trends of battery packs under different operating conditions (such as charging and discharging, static storage, and different ambient temperatures) can be accurately predicted and multi-dimensionally evaluated before the physical prototype is manufactured. This "virtual prototype" technology greatly shortens the development cycle, reduces trial and error costs, and provides key data support for the optimization of BMS control strategies. Among them, models based on electrochemical principles can reveal the internal state of the battery from the microscopic mechanism level. Compared with simple equivalent circuit models, they have higher accuracy and extrapolation capabilities, especially in characterizing complex phenomena such as thermal coupling effects, lithium plating risk, and internal aging unevenness.

[0026] However, despite the significant value of simulation technology, the following technical challenges exist when applying high-precision electrochemical models from the individual cell level to the large-scale battery system (pack) level.

[0027] 1. The trade-off between computational complexity and efficiency: A full-size battery pack contains a large number of cells. If a complex multiphysics coupled electrochemical model is used to simulate each cell, the computational load will increase exponentially, resulting in extremely long simulation times and failing to meet the rapid iterative design requirements in engineering. The enormous consumption of computational resources makes it impractical.

[0028] 2. Trade-off between model simplification and accuracy loss: To improve computational efficiency, existing technologies typically simplify the cell model significantly into an equivalent circuit model (ECM) or an empirical model. However, this simplification sacrifices the model's ability to describe the internal electrochemical mechanisms, resulting in insufficient accuracy in predicting extreme operating conditions, thermal coupling effects, and long-term aging behavior, making it difficult to accurately assess the battery's safety boundaries and lifespan end.

[0029] 3. Lack of Inconsistency Modeling: Due to differences in manufacturing processes, initial conditions, and usage environments, there are inevitable inconsistencies in voltage, capacity, internal resistance, and temperature among the cells within a pack. Existing simulation methods typically assume that all cell parameters are identical, ignoring this inconsistency and its time-varying evolution. This results in an inability to accurately simulate the actual operating conditions of the pack, especially the overall performance degradation and safety risks caused by the "weakest link effect."

[0030] 4. Difficulty in Implementing Fully Coupled Multiphysics Simulation: The performance of a battery system is the result of a high degree of coupling between electrical, thermal, and mechanical fields. For example, temperature distribution affects the electrochemical reaction rate and side reactions, which in turn affect heat generation and aging; structural stress may also change the contact impedance and lithium-ion transport within the cell. Existing technologies lack an efficient and accurate pack-level fully coupled multiphysics simulation framework, making it difficult to reveal system-level behavior under complex interactions.

[0031] To address the aforementioned issues, this disclosure provides a method, apparatus, device, and medium for constructing a battery simulation model. The method for constructing a battery simulation model provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0032] Figure 1 This is a flowchart illustrating the steps of a method for constructing a battery simulation model according to an embodiment of this application. Figure 1 As shown, the method may include the following steps.

[0033] Step 101: Obtain the battery cell parameters, charge / discharge data, and production line statistics.

[0034] The battery consists of multiple individual cells connected in series and parallel.

[0035] In some embodiments, the cell parameters include: geometric parameters, electrochemical parameters, and thermodynamic parameters of the battery, wherein the geometric parameters include the size parameters of the positive electrode current collector, the negative electrode current collector, the positive electrode, the negative electrode, and the separator; the electrochemical parameters include the voltage, current, power, capacity, and energy of a single cell.

[0036] For example, geometric parameters include the length, width, and height of the battery cell, the thickness of the electrode plates, the thickness of the separator, the arrangement of the pack, the heat dissipation structure, and the dimensions of the connectors. Thermodynamic parameters include the thermal conductivity and specific heat capacity of the battery cell, the thermal resistance of the pack casing, and the heat dissipation coefficient.

[0037] In some embodiments, the charge / discharge data includes: direct current resistance (DCR) data, charge / discharge curves of a single cell, internal resistance test data, etc.

[0038] In some embodiments, production line statistics are initial state statistics of the same batch of cells obtained from the production line. The production line statistics include initial open circuit voltage (OCV) distribution, initial capacity distribution, state of charge (SOC) distribution, irreversible capacity decay factor, OCV-SOC mapping function, and decay coefficient (K) distribution.

[0039] In some embodiments, production line statistics are used to reflect consistency differences in the cell manufacturing process.

[0040] In other embodiments, the cell parameters also include positive and negative electrode materials, areal density, porosity, and active material content; for example, the positive and negative electrode materials can be ternary lithium, lithium iron phosphate, etc.

[0041] Step 102: Based on the cell parameters, establish a three-dimensional thermal model and a physicochemical model of a single cell.

[0042] In some embodiments, a three-dimensional thermal model is used to calculate the temperature field of the Pack.

[0043] In some embodiments, the physicochemical model of a single cell can be a pseudo two-dimensional (P2D) model of a single cell, used to describe the electrochemical reactions and mass transfer processes inside the cell.

[0044] In some embodiments, a three-dimensional geometric model of the battery is established based on thermodynamic parameters and geometric structural parameters; preset thermal boundary conditions are input into the three-dimensional geometric model to obtain a three-dimensional thermal model.

[0045] In some embodiments, a physicochemical model of a single cell is established based on geometric and electrochemical parameters.

[0046] Step 103: Using a genetic algorithm, the kinetic parameters in the physicochemical model of each single cell are calibrated based on the charge and discharge data to obtain multiple pseudo-two-dimensional models of single cells.

[0047] In this embodiment, the genetic algorithm is a global optimization algorithm that corrects the dynamic parameters in the P2D model by comparing the charge-discharge curve calculated by the model with the actual test charge-discharge data until the error between the model output and the experimental data is minimized.

[0048] In some embodiments, the kinetic parameters include the negative electrode exchange current density, the positive electrode exchange current density, the negative electrode solid-phase diffusion coefficient, the positive electrode solid-phase diffusion coefficient, and the liquid-phase diffusion coefficient. The units for the negative electrode exchange current density and the positive electrode exchange current density can be amperes per square meter (A / m²). 2The units for the negative electrode solid-phase diffusion coefficient, positive electrode solid-phase diffusion coefficient, and liquid-phase diffusion coefficient can be kilojoules per mole (kJ / mol).

[0049] In some embodiments, a genetic algorithm is used to determine the calibration range based on DC resistance data and obtain the kinetic parameters within the calibration range; the upper and lower limits of the calibration range are preset multiples of the standard deviation under a normal distribution; based on the kinetic parameters within the calibration range, the physicochemical models of multiple single cells are corrected to obtain multiple pseudo-two-dimensional models of single cells.

[0050] Step 104: Based on multiple single-cell pseudo-two-dimensional models and the series-parallel topology of the battery, establish a multi-cell coupling model.

[0051] In the embodiments of this application, the series-parallel topology includes the electrical connection method of the cells within the pack, such as pure series, pure parallel, or mixed series connection. The series-parallel topology affects the voltage and current characteristics of the pack.

[0052] In one possible implementation, electrical constraints are determined based on Kirchhoff's current law, Kirchhoff's voltage law, and series-parallel topology.

[0053] In some embodiments, multiple single-cell P2D models are integrated according to their electrical connection relationships based on the series-parallel topology of the Pack to obtain a multi-cell coupled model.

[0054] In some embodiments, the multi-cell coupling model is used to characterize the voltage and current transmission rules of cells within a pack.

[0055] Step 105: Using the Monte Carlo simulation method, the multi-cell coupling model is corrected based on production line statistics to obtain the multi-cell electrochemical model of the battery.

[0056] In this embodiment, the Monte Carlo simulation method is a random sampling method: based on production line statistics, parameter combinations corresponding to the number of Pack cells are randomly selected from the parameter library and used to replace the parameters of each individual cell in the multi-cell coupling model.

[0057] In some embodiments, production line statistics are initial state statistics of the same batch of cells obtained from the production line. The production line statistics include initial open circuit voltage (OCV) distribution, initial capacity distribution, irreversible capacity decay factor, OCV-SOC mapping function, and decay coefficient (K) distribution.

[0058] In some embodiments, the capacity distribution and charge state distribution of each cell in the battery are obtained by using the Monte Carlo simulation method based on production line statistics; the capacity distribution and charge state distribution are used to correct the pseudo two-dimensional models of multiple single cells in the multi-cell coupling model to obtain the multi-cell electrochemical model of the battery.

[0059] Step 106: Couple the multi-cell electrochemical model and the three-dimensional thermal model to obtain the battery simulation model.

[0060] In some embodiments, the cell heat generation rate output by the multi-cell electrochemical model is input into the three-dimensional thermal model to obtain the temperature field of the pack; the cell temperature calculated by the thermal model is passed back to the electrochemical model, and the kinetic parameters of the electrochemical model are corrected by the Arrhenius formula to obtain the simulation model of the battery.

[0061] In some embodiments, the battery simulation model is used to output the electrical and thermal performance of the pack, enabling mass production consistency and mitigating the effects of multi-field interactions. The electrical performance includes voltage and capacity, while the thermal performance includes temperature distribution.

[0062] In some embodiments, the three-dimensional thermal model includes a heat source term. The average heat generation power is used as the heat source term of the three-dimensional thermal model and coupled into the three-dimensional thermal model to obtain the temperature distribution output by the three-dimensional thermal model. Based on the temperature distribution and the preset Arrhenius formula, the kinetic parameters in the multi-cell electrochemical model are corrected to obtain a set of coupled equations. The set of coupled equations is solved to obtain the simulation model of the battery.

[0063] In summary, in this embodiment, calibrating the physicochemical model of a single cell using a genetic algorithm not only reduces the manpower and time costs of modeling but also improves the accuracy of the pseudo-two-dimensional model of a single cell. Secondly, by introducing production line statistical data and correcting the multi-cell coupling model using Monte Carlo simulation, the multi-cell coupling model can realistically reflect the manufacturing consistency differences of the cells, thus improving the accuracy of the multi-cell electrochemical model. Finally, by coupling the thermal model and the electrochemical model, the interaction between temperature and electrical performance can be accurately predicted, further improving the accuracy of the simulation model.

[0064] Figure 2 This is a flowchart illustrating the specific steps of a method for constructing a battery simulation model according to an embodiment of this application. See also... Figure 2 The method may include the following steps.

[0065] Step 201: Obtain the battery cell parameters, charge / discharge data, and production line statistics.

[0066] The method for this step has been explained in step 101 above, and will not be repeated here.

[0067] Step 202: Based on the cell parameters, establish a three-dimensional thermal model and a physicochemical model of a single cell.

[0068] The method for this step has been explained in step 102 above, and will not be repeated here.

[0069] In some embodiments, step 202 may include: Sub-step 2021: Based on the thermodynamic parameters and geometric parameters, establish a three-dimensional geometric model of the battery.

[0070] Sub-step 2022: Input the preset thermal boundary conditions into the three-dimensional geometric model to obtain the three-dimensional thermal model.

[0071] Sub-step 2023: Based on the geometric and electrochemical parameters, establish a physicochemical model of a single cell.

[0072] In the embodiments of this application, thermodynamic parameters are the thermal properties of the battery, such as the density of the cell and the casing, and the specific heat capacity.

[0073] In one possible implementation, the thermodynamic parameters include: negative electrode active lithium content (Y_host_neg), total active lithium content (Y_Li), positive electrode active lithium content (Y_host_pos), and initial negative electrode SOC (SOC0_neg). For example, the negative electrode active lithium content is 1.15, the total active lithium content is 1.06, the positive electrode active lithium content is 1.05, and the initial negative electrode SOC is 0.01.

[0074] In the embodiments of this application, the geometric parameters are the size of the battery cell, the arrangement of the battery cells in the pack, the shape and size of the outer casing and the heat dissipation structure.

[0075] In one possible implementation, a three-dimensional solid model of the battery pack is built in simulation software based on geometric structural parameters; thermodynamic parameters are assigned to the corresponding components of the three-dimensional solid model to obtain a three-dimensional geometric model, wherein the three-dimensional geometric model is a geometric solid model with physical properties.

[0076] In one possible implementation, the preset thermal boundary condition is the battery's operating environment condition, such as the convective heat transfer coefficient of natural heat dissipation, ambient temperature, etc.

[0077] In one possible implementation, preset thermal boundary conditions are input into a three-dimensional geometric model, and a model is constructed based on the heat conduction equation to obtain a three-dimensional thermal model.

[0078] For example, the preset thermal boundary condition could be: under natural heat dissipation conditions, setting the convective heat transfer coefficient of the outer surface of the pack to approximately 5-10 watts per square meter (W / (m²)). K), and the ambient temperature is set to 25 degrees Celsius (°C).

[0079] In one possible implementation, the mathematical model of Pack is converted into a .stp file format and imported into the calculation software. Based on the heat transfer equation and preset thermal boundary conditions, a three-dimensional thermal model is established.

[0080] For example, the heat transfer equation of the three-dimensional thermal model is as follows: (1) in, It is specific heat capacity. It's density. is the entropy-thermal coefficient, k is the thermal conductivity, T is the temperature, and Q is the heat source.

[0081] In one possible implementation, a pseudo-two-dimensional model is constructed based on geometric and electrochemical parameters, and this pseudo-two-dimensional model serves as the physicochemical model for a single battery cell. The pseudo-two-dimensional model is used to represent the electrochemical reactions, ion transport, and electron transport within the battery cell.

[0082] In one possible implementation, the finite element method is used to solve the governing equations of a pseudo-two-dimensional model of a single cell, and the electrical performance data under specific operating conditions are calculated. The governing equations include charge conservation equations, mass conservation equations, and electrode kinetic equations, while the electrical performance data includes the voltage, current, power, capacity, and energy of the single cell.

[0083] Through the above technical solution, a three-dimensional thermal model and a single-cell physicochemical model are established based on different parameters. The three-dimensional thermal model is used to characterize and describe the heat transfer law of the battery, while the single-cell physicochemical model is used to characterize the electrochemical characteristics of each cell. This is beneficial for obtaining a simulation model through the bidirectional interaction of heat generation and temperature.

[0084] Step 203: Using a genetic algorithm, determine the calibration range based on the DC resistance data and obtain the dynamic parameters within the calibration range.

[0085] In the embodiments of this application, the upper and lower limits of the calibration range are preset multiples of the standard deviation under a normal distribution.

[0086] In some embodiments, DCR statistics of mass-produced batches of cells are obtained, a normal distribution curve of DCR is fitted, and the mean and standard deviation are calculated.

[0087] For example, under a normal distribution, the standard deviation is σ, the upper limit of the calibration range is +3σ, and the lower limit of the calibration range is -3σ. Then the calibration range is (-3σ, +3σ).

[0088] In some embodiments, the calibration range is used as the optimization boundary of the genetic algorithm, and the mass-produced DCR data is input as the target value. The algorithm iteratively optimizes within this range and finally outputs the dynamic parameter values ​​that match the DCR data.

[0089] For example, uncalibrated kinetic parameters are referred to as standard kinetic parameters, which are shown in Table 1.

[0090] Table 1

[0091] For example, the dynamic parameters within the calibration range are referred to as Pack random variation dynamic parameters, which are shown in Table 2.

[0092] Table 2

[0093] In one possible implementation, the battery cell is a 40.5 Ah lithium iron phosphate-graphite prismatic battery with dimensions of 60*280*180 mm. The pack undergoes charge-discharge cycling under natural heat dissipation conditions with a current of 0.33 coulombs (C) and upper and lower cutoff voltages of 3.8 volts (V) and 2.65 V, respectively. The upper and lower limits of the kinetic parameters are established using MATLAB random functions, with upper and lower limits of -3σ and +3σ, respectively, and a data volume of 200,000.

[0094] Step 204: Based on the kinetic parameters within the calibration range, correct the physicochemical models of multiple single cells to obtain multiple pseudo-two-dimensional models of single cells.

[0095] In some embodiments, using the upper and lower limits of the calibration interval as boundaries, uniform sampling is employed to generate multiple sets of kinetic parameter combinations. These multiple sets of kinetic parameter combinations are then paired to form a set of kinetic parameters for each battery cell. The set of kinetic parameters for each battery cell is then input into multiple physicochemical models of individual battery cells, replacing the original standard kinetic parameters in these models, resulting in multiple pseudo-two-dimensional models of individual battery cells. The number of kinetic parameter combinations and sets is the same as the number of physicochemical models for individual battery cells.

[0096] For example, the number of battery cells is 102, and 102 combinations of dynamic parameters are randomly selected from the dynamic parameters within the calibration range; 102 P2D models are established, and the 102 randomly selected combinations of dynamic parameters are input into the 102 P2D models.

[0097] In some embodiments, matching initial conditions are set for each single-cell pseudo-two-dimensional model to ensure that the model can be solved normally.

[0098] Understandably, the physicochemical model of a single cell is an idealized, general model. By substituting kinetic parameters within different calibration ranges, the electrochemical reactions and ion transport characteristics of the model will be adjusted accordingly, thereby matching mass-produced cells with different DCR levels. Each set of calibrated kinetic parameters corresponds to a modified pseudo-two-dimensional model of a single cell. These pseudo-two-dimensional models of single cells are differentiated models that can reflect the consistency differences of mass-produced cells.

[0099] By employing the above technical solutions and using genetic algorithms to improve the prediction accuracy of the model, each corrected pseudo-two-dimensional model can accurately reflect the electrochemical reaction characteristics of different battery cells, thereby improving the accuracy of data input for battery simulation models.

[0100] Step 205: Based on multiple single-cell pseudo-two-dimensional models and the series-parallel topology of the battery, establish a multi-cell coupling model.

[0101] The method for this step has been explained in step 104 above, and will not be repeated here.

[0102] In some embodiments, step 205 may include: Sub-step 2051: Determine the electrical constraints of the multi-cell coupling model based on the series-parallel topology.

[0103] Sub-step 2052: Connect multiple single-cell pseudo-two-dimensional models according to electrical constraints to obtain a multi-cell coupled model.

[0104] In the embodiments of this application, the series-parallel topology includes the electrical connection method of the cells within the pack, such as pure series, pure parallel, or mixed series connection. The series-parallel topology affects the voltage and current characteristics of the pack.

[0105] In one possible implementation, electrical constraints are determined based on Kirchhoff's current law, Kirchhoff's voltage law, and series-parallel topology.

[0106] For example, the electrical constraints corresponding to pure series connection include: the current of all cells is equal, and the total voltage of the pack is equal to the sum of the terminal voltages of all cells; the electrical constraints corresponding to pure parallel connection include: the terminal voltage of all cells is equal, and the total current of the pack is equal to the sum of the currents of all cells; the electrical constraints corresponding to mixed connection include: the current of cells in each series branch is equal, the branch voltage is equal to the sum of the cell voltages in the branch, the branch voltages between parallel branches are equal, and the total current of the pack is equal to the sum of the currents in each branch.

[0107] In one possible implementation, when the series-parallel topology is purely series, multiple single-cell pseudo-two-dimensional models are connected end-to-end to force all models to have the same input current; at the same time, the voltages of all cells are superimposed at the output of the model to obtain the total voltage of the pack.

[0108] For example, the Pack is a high-voltage 102-cell series pack. The series-parallel topology has only one branch, and the current of all P2Ds is equal. The total voltage of the Pack is the sum of the voltages of all cells, which can be expressed by the following formula.

[0109] (2) (3) in, It is the current of each cell. It is the terminal voltage of each battery cell. This indicates the total voltage of the pack.

[0110] In another possible implementation, when the series-parallel topology is in pure parallel, the positive terminals of all single-cell P2D models are connected to the same common positive node, and the negative terminals are connected to the same common negative node, so that the terminal voltages of all cells are equal; the currents of all cells are superimposed to obtain the total pack current.

[0111] For example, see Figure 3 , Figure 3 The diagram shows a series-parallel topology of a battery. "Parallel" indicates the number of parallel circuits, where the total voltage of each parallel branch is equal, the voltage of each P2D is equal to the solid-phase potential in the electrochemical calculation model, and the total current is equal to the sum of the currents in each branch. P2D represents the P2D electrochemical model of a single battery cell, and Rc (Rc_1, Rc_2) represents the connection resistance between cells, including the internal resistance of structural components such as tabs, busbars, and connectors. Each row represents a series branch, indicating that the battery cell and the connecting resistor are connected in series; multiple rows of branches are in parallel. If there are 3 rows (3 parallel), and each row contains 4 "Rc+P2D" (4 series), then the Pack has a series-parallel topology of 3 parallel and 4 series.

[0112] In another possible implementation, multiple P2D models to be connected in series are first connected in series to form a branch; this branch is copied to obtain multiple branches with the same structure; multiple branches are connected in parallel; wherein, the output voltage of multiple branches is constrained to be equal, and the total current is the sum of the currents of multiple branches.

[0113] For example, first connect 51 P2D models in series to form one branch, then copy one branch with the same structure, and connect the two branches in parallel; constrain the output voltage of the two branches to be equal, and the total current is the sum of the currents of the two branches.

[0114] Through the above technical solution, based on the series and parallel topology of the battery, the electrical constraint rules are clarified, and then multiple single-cell P2D models are connected according to the rules to construct a multi-cell coupled model that can reflect the electrical characteristics of the entire pack. The multi-cell coupled model can be interfaced with the three-dimensional thermal model, thereby improving the accuracy of the simulation model.

[0115] Step 206: Using the Monte Carlo simulation method, obtain the capacity distribution and charge state distribution of each cell in the battery based on production line statistics.

[0116] In some embodiments, production line statistics are initial state statistics of the same batch of cells obtained from the production line. The production line statistics include initial open circuit voltage (OCV) distribution, initial capacity distribution, irreversible capacity decay factor, OCV-SOC mapping function, and decay coefficient (K) distribution.

[0117] In some embodiments, a probability distribution model of random variables is established by fitting multiple parameter distributions with a normal distribution function; wherein, the multiple parameter distributions include the initial open-circuit voltage distribution, the initial capacity distribution, the OCV-SOC function distribution, and the attenuation coefficient distribution; a preset number of samples are randomly drawn from the multiple parameter distributions to simulate the actual cell sampling process during packaging; the cell state after storage for a preset time is calculated; and the stored cell state is converted into the SOC of each cell through the OCV-SOC function to obtain the SOC distribution of the entire package of cells.

[0118] For example, using the Matlab normal distribution function, the OCV distribution (OCV1), capacity distribution (Q1), irreversible capacity decay factor α, OCV-SOC function, and K value distribution of a batch of cells are obtained from the production line. Assuming the number of cells in the pack is N, OCV1, Q1, and K are randomly selected N times. Through calculation, it can be found that after M months of storage, the capacity of each cell in the pack before packaging is Q2=Q1*(1-α), OCV2=OCV1-K*M, and the SOC of each cell can be obtained through the OCV-SOC function.

[0119] For example, the normal distribution is a Gaussian distribution, and the SOC distribution and capacity distribution of the Pack cells are shown in Table 3.

[0120] Table 3

[0121] In Table 3, Q(μa, σa) is the normal distribution parameter of the capacity, and SOC(μa, σa) is the normal distribution parameter of the SOC.

[0122] Step 207: Modify the multiple single-cell pseudo-two-dimensional models in the multi-cell coupling model with the capacity distribution and charge state distribution to obtain the multi-cell electrochemical model of the battery.

[0123] In some embodiments, multiple samples are randomly drawn from the capacity distribution and the SOC distribution, respectively; wherein the multiple samples include capacity samples and SOC samples; the initial capacity in the multiple single-cell pseudo two-dimensional models is replaced with capacity samples, and the initial SOC is replaced with SOC samples to obtain the multi-cell electrochemical model of the battery.

[0124] For example, N samples are extracted and assigned one-to-one to the N single-cell P2D models in the multi-cell coupling model. The first cell corresponds to the first group of samples, the second cell corresponds to the second group of samples, and N is a positive integer greater than or equal to 2.

[0125] In some embodiments, the series-parallel topology and electrical constraints of the multi-cell coupling model remain unchanged during the modification process.

[0126] By introducing production line statistics and using the Monte Carlo simulation method, each battery cell is assigned a capacity and initial SOC value that conforms to mass production rules. The pseudo-two-dimensional models of multiple single cells in the multi-cell coupling model are corrected so that the initial state of the corrected pseudo-two-dimensional model of the single cell is consistent with that of the mass-produced battery cell, thereby improving the accuracy of the pseudo-two-dimensional model of the single cell and thus improving the accuracy of the simulation model.

[0127] Step 208: Calculate the average heat generation power of the multi-cell electrochemical model using a preset electrochemical heat generation equation.

[0128] In some embodiments, the preset electrochemical heat generation equation may be the Bernardi equation, which is used to convert the electrical properties of the battery cell into heat generation power.

[0129] In some embodiments, step 208 includes: Sub-step 2081: Based on the electrochemical parameters and the electrochemical heat generation equation, solve for the instantaneous heat generation power of each cell in the battery; Sub-step 2082: Calculate the average value of the instantaneous heat generation power of each cell to obtain the average heat generation power.

[0130] In one possible implementation, the electrochemical parameters include the terminal voltage of each cell in the battery, and the instantaneous heat generation power of each cell in the battery is solved according to the sum of the electrochemical heat generation equations for each cell.

[0131] An example electrochemical heat generation equation is shown below.

[0132] (4) Where I is current, It is the terminal voltage of each battery cell. This indicates the open-circuit voltage of each battery cell. This indicates the heat source for each battery cell. Indicates the initial reference temperature. It represents the entropy heat coefficient.

[0133] In one possible implementation, the current and cell terminal voltage are output in real time by the P2D electrochemical model; the open-circuit voltage is obtained by querying the OCV-SOC curve from the cell's current SOC; the initial reference temperature is the ambient temperature, and subsequent iterations are fed back from the three-dimensional thermal model.

[0134] In one possible implementation, the average heat generation power of the geometric region of each cell is calculated.

[0135] For example, if the cell volume is Then the average heat production power density is .

[0136] The above technical solution transforms electrical performance parameters into heat source data for a three-dimensional thermal model, laying the foundation for bidirectional coupling. Since instantaneous heat generation power fluctuates rapidly with current and voltage, calculating the average heat generation power can smooth the heat source data and improve the convergence speed and calculation accuracy of the three-dimensional thermal model.

[0137] Step 209: The average heat generation power is used as the heat source term of the three-dimensional thermal model and coupled into the three-dimensional thermal model to obtain the temperature distribution output by the three-dimensional thermal model.

[0138] In some embodiments, the average heat generation power is used as a heat source term in the three-dimensional thermal model and coupled into the three-dimensional thermal model; the three-dimensional thermal model is solved according to the heat transfer equation of the three-dimensional thermal model to obtain the temperature distribution output by the three-dimensional thermal model.

[0139] For example, the three-dimensional thermal model is solved according to formula (1) and preset thermal boundary conditions to obtain the three-dimensional temperature distribution result of Pack. The three-dimensional temperature distribution result of Pack is as follows: Figure 4 As shown. The color bar on the right is a temperature scale, with blue (36.5℃) to red (44.8℃) representing temperatures from low to high; the color differences on the surface of the battery cell correspond to its actual temperature, with yellow and orange areas having higher temperatures and blue and green areas having lower temperatures. Figure 4 In the diagram, SolutionTime1244(s) represents the temperature distribution result calculated in the simulation up to 1244 seconds; Temperature(°C) indicates that the contour map shows the temperature field. Figure 4 It can be seen that the temperature of the cells in different locations or different areas of the same cell is inconsistent, with some areas approaching 45°C, indicating a risk of local overheating. This is usually the result of uneven heat generation in the cells and uneven heat dissipation structure in the pack.

[0140] In some embodiments, the temperature distribution is a three-dimensional temperature field of the battery, yielding the average temperature of each cell.

[0141] In some embodiments, the average heat generation power set of all battery cells is output as the heat source input of the three-dimensional thermal model, and the average temperature of each battery cell is obtained by solving the three-dimensional thermal model. For example, the average heat generation power set is... .

[0142] Step 210: Based on the temperature distribution and the preset Arrhenius equation, correct the kinetic parameters in the multi-cell electrochemical model to obtain the coupled equation set.

[0143] In some embodiments, the P2D model calculated temperature of the i-th cell is equal to the average temperature of the geometric region of that cell in the thermal model. For example, the geometric region is... The average temperature is .

[0144] In some embodiments, the modified kinetic parameters change the governing equations of the multi-cell electrochemical model; the modified electrochemical model governing equations are combined with the heat transfer equations of the three-dimensional thermal model to form an electrochemical-thermal coupled equation set, wherein the variables of the coupled equation set include cell voltage, current and temperature, and the variables are interrelated.

[0145] For example, the preset Arrhenius formula is shown below.

[0146] (5) (6) (7) (8) (9) Where Ds_neg represents the negative electrode solid-phase diffusion coefficient, Ds_pos represents the negative electrode solid-phase diffusion coefficient, i0_neg represents the negative electrode exchange current density, i0_pos represents the positive electrode exchange current density, and De represents the liquid-phase diffusion coefficient; Ea_Ds_neg, Ea_Ds_pos, Ea_i0_neg, Ea_i0_pos, and Ea_I are the activation energies corresponding to the negative electrode solid-phase diffusion coefficient, positive electrode diffusion coefficient, negative electrode exchange current density, positive electrode exchange current density, and liquid-phase diffusion coefficient, respectively; R is the gas constant; This indicates the average temperature of the battery cell.

[0147] For example, see the model coupling process. Figure 5 The heat source output by the P2D model The temperature is passed to the 3D thermal model as input for temperature calculation; the 3D thermal model calculates the cell temperature. The data is then passed back to the corresponding P2D model of the battery cell to correct its dynamic parameters.

[0148] Step 211: Solve the coupled equations to obtain the simulation model of the battery.

[0149] In some embodiments, the electrochemical model is solved using initial parameters, the heat generation power is calculated and input into the thermal model; the thermal model solves for the temperature distribution and feeds it back to the electrochemical model to correct the kinetic parameters; the electrochemical model is solved again using the corrected parameters, and the above process is repeated; when the iteration result meets the preset convergence condition, the calculation is stopped, and the steady-state solution of the coupled equations of this iteration is used as the simulation model of the battery.

[0150] In some embodiments, the battery simulation model is used to output the voltage, capacity, current distribution, temperature field, temperature rise curve, etc. of the pack.

[0151] For example, see Figure 6 The battery simulation model outputs the cell voltage curve. Figure 6 During the first 0-10000 seconds, the voltage drops rapidly, corresponding to the discharge process; during the second 10000-20000 seconds, the voltage rises rapidly and remains at a high voltage plateau, corresponding to the charging process; during the third 20000-30000 seconds, the voltage drops rapidly again, corresponding to the discharge process. Figure 6 The curves show differences in some stages, indicating that there is inconsistency in the voltage of the cells within the pack.

[0152] For example, Figure 7 This is a graph showing the change in the state of charge (SOC) output from the battery simulation model. Combined with... Figure 6 and Figure 7 It can be seen that the SOC decreases during discharge and increases during charging; in the initial stage, the SOC drops rapidly to around 0, and then rises back to a plateau close to 1 after charging. Figure 7 The curves show significant differences between the initial and final stages, indicating that the SOC deviation of the battery cell changes during the charging and discharging process.

[0153] The above technical solution employs a two-way coupling mechanism, allowing the electrochemical model and the thermal model to feedback each other. The kinetic parameters are dynamically corrected with real-time temperature, avoiding simulation deviations caused by isothermal parameters and improving the accuracy of the simulation model.

[0154] In summary, in the embodiments of this application, firstly, a genetic algorithm is used to quickly match the calculated values ​​of the model with the experimental test values, thereby improving the prediction accuracy of the model and increasing the accuracy of the data input for the battery simulation model; secondly, production line statistical data is introduced, and the Monte Carlo simulation method is used to assign each cell a capacity and initial SOC value that conforms to the mass production pattern, thereby improving the accuracy of the single-cell pseudo-two-dimensional model; finally, a bidirectional coupling mechanism is adopted to allow the electrochemical model and the thermal model to feed back to each other, and the kinetic parameters are dynamically corrected with real-time temperature, avoiding simulation deviations caused by isothermal parameters and improving the accuracy of the simulation model.

[0155] Figure 8 This is a schematic diagram of another method for constructing a battery simulation model provided in an embodiment of this application. For example... Figure 8 As shown, the method may include the following steps.

[0156] Step 301: Establish a pseudo-two-dimensional model of the lithium-ion battery.

[0157] In the embodiments of this application, the pseudo-two-dimensional model is the basic framework of the single-cell P2D electrochemical model.

[0158] Step 302: Input cell parameters for a single cell.

[0159] Step 303: Calibrate the dynamic parameters.

[0160] In the embodiments of this application, the calibration range is (-3σ, +3σ), and dynamic parameters of (-3σ, +3σ) are randomly selected.

[0161] Step 304: Output a pseudo-two-dimensional model of a single battery cell.

[0162] Step 305: Connect multiple pseudo-two-dimensional models in series and parallel.

[0163] In this embodiment of the application, the Monte Carlo algorithm is used to simulate the capacity and SOC of each cell in the Pack.

[0164] Step 306: Couple with the three-dimensional thermal model.

[0165] In this embodiment, the resistance of the BMS electrical system is input to make the model more closely resemble actual usage scenarios.

[0166] In summary, this embodiment of the application simultaneously considers multiple practical factors such as differences in cell parameters, initial state deviations, BMS acquisition errors, and uneven temperature distribution, effectively improving the realism of the system-level simulation. This method can more accurately evaluate the performance of the battery pack under actual operating conditions, particularly demonstrating better practicality in predicting system usable capacity, temperature distribution characteristics, and lifespan degradation trends. Furthermore, this model provides a more realistic analysis environment for BMS algorithm verification and thermal management design, helping to identify and resolve potential problems during the design phase, thereby reducing development costs and shortening the R&D cycle.

[0167] This application also provides a block diagram of a device for constructing a battery simulation model, such as... Figure 9 As shown, the battery simulation model building device 400 includes the following modules.

[0168] The acquisition module 401 is used to acquire the battery cell parameters, charge and discharge data and production line statistics; the battery includes multiple single cells connected in series and parallel. The first modeling module 402 is used to establish a three-dimensional thermal model and a physicochemical model of a single cell based on the cell parameters. The calibration module 403 is used to calibrate the kinetic parameters in the physicochemical model of each single cell based on the charge and discharge data using a genetic algorithm, thereby obtaining multiple pseudo-two-dimensional models of single cells. The second modeling module 404 is used to establish a multi-cell coupling model based on multiple single-cell pseudo-two-dimensional models and the series-parallel topology of the battery. The correction module 405 is used to correct the multi-cell coupling model based on production line statistics using the Monte Carlo simulation method to obtain the multi-cell electrochemical model of the battery. The third modeling module 406 is used to couple the multi-cell electrochemical model and the three-dimensional thermal model to obtain the battery simulation model.

[0169] Optionally, the charge / discharge data includes DC resistance data, and the kinetic parameters include negative electrode exchange current density, positive electrode exchange current density, negative electrode solid-phase diffusion coefficient, positive electrode solid-phase diffusion coefficient, and liquid-phase diffusion coefficient; the calibration module 403 includes: The calibration range determination submodule is used to determine the calibration range based on DC resistance data using a genetic algorithm, and to obtain the dynamic parameters within the calibration range; the upper and lower limits of the calibration range are preset multiples of the standard deviation under a normal distribution. The first correction submodule is used to correct the physicochemical models of multiple single cells based on the kinetic parameters within the calibration range, thereby obtaining multiple pseudo-two-dimensional models of single cells.

[0170] Optionally, the correction module 405 includes: The data determination submodule is used to obtain the capacity distribution and charge state distribution of each cell in the battery based on the Monte Carlo simulation method and the statistical data of the production line. The second correction submodule is used to correct the multiple single-cell pseudo-two-dimensional models in the multi-cell coupling model by adjusting the capacity distribution and charge state distribution, so as to obtain the multi-cell electrochemical model of the battery.

[0171] Optionally, the cell parameters include the battery's geometric parameters, electrochemical parameters, and thermodynamic parameters. The geometric parameters include the dimensions of the positive electrode current collector, the negative electrode current collector, the positive electrode, the negative electrode, and the separator. The electrochemical parameters include the voltage, current, power, capacity, and energy of a single cell. The first modeling module 402 includes: The first modeling submodule is used to establish a three-dimensional geometric model of the battery based on thermodynamic parameters and geometric structural parameters; The condition input submodule is used to input preset thermal boundary conditions into the three-dimensional geometric model to obtain a three-dimensional thermal model; The second modeling submodule is used to establish a physicochemical model of a single cell based on geometric and electrochemical parameters.

[0172] Optionally, the three-dimensional thermal model includes heat source terms; the third modeling module 406 includes: The power calculation submodule is used to calculate the average heat generation power of a multi-cell electrochemical model using a preset electrochemical heat generation equation. The model coupling submodule is used to couple the average heat generation power as the heat source term of the three-dimensional thermal model into the three-dimensional thermal model and obtain the temperature distribution output by the three-dimensional thermal model. The third modeling submodule is used to correct the kinetic parameters in the multi-cell electrochemical model based on the temperature distribution and the preset Arrhenius equation, and obtain the coupled equation set. The model solver submodule is used to solve the coupled equations to obtain the simulation model of the battery.

[0173] Optionally, the cell parameters include electrochemical parameters, which include the voltage, current, power, capacity, and energy of a single cell; the power calculation submodule includes: The solution unit is used to solve for the instantaneous heat generation power of each cell in the battery based on electrochemical parameters and electrochemical heat generation equations. The calculation unit is used to calculate the average value of the instantaneous heat generation power of each cell, and obtain the average heat generation power.

[0174] Optionally, the second modeling module 404 includes: The condition determination submodule is used to determine the electrical constraints of the multi-cell coupling model based on the series-parallel topology. The model connection submodule is used to connect multiple single-cell pseudo-two-dimensional models according to electrical constraints to obtain a multi-cell coupled model.

[0175] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0176] like Figure 10 As shown, this application embodiment also provides an electronic device 600, including a processor 601 and a memory 602. The memory 602 stores a program or instructions that can run on the processor 601. When the program or instructions are executed by the processor 601, they implement the various steps of the above-described battery simulation model construction method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0177] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0178] Figure 11 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0179] The electronic device 700 includes, but is not limited to, components such as: radio frequency unit 701, network module 702, audio output unit 703, input unit 704, sensor 705, display unit 706, user input unit 707, interface unit 708, memory 709, and processor 710.

[0180] Those skilled in the art will understand that the electronic device 700 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 710 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 11 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0181] The processor 710 is used to implement each step of the battery simulation model construction method embodiment in the above method embodiment.

[0182] It should be understood that, in this embodiment, the input unit 704 may include a graphics processing unit (GPU) 7041 and a microphone 7042. The GPU 7041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 706 may include a display panel 7061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 707 includes at least one of a touch panel 7071 and other input devices 7072. The touch panel 7071 is also called a touch screen. The touch panel 7071 may include a touch detection device and a touch controller. Other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (such as volume control buttons, power buttons, etc.), a trackball, a mouse, and a joystick, which will not be described in detail here.

[0183] The memory 709 can be used to store software programs and various data. The memory 709 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs, or instructions required for functions (such as sound playback functions, image playback functions, etc.). Furthermore, the memory 709 may include volatile memory or non-volatile memory, or it may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 709 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0184] Processor 710 may include one or more processing units; optionally, processor 710 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 710.

[0185] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described battery simulation model construction method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0186] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0187] This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described battery simulation model construction method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0188] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system-on-a-chip, chip system, or system-on-chip, etc. The present invention also provides a readable storage medium, wherein when the instructions in the storage medium are executed by the processor of an electronic device, the electronic device is able to perform the steps of the above-described battery simulation model construction method.

[0189] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aforementioned method for constructing a battery simulation model.

[0190] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0191] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0192] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for constructing a battery simulation model, characterized in that, The method includes: Obtain battery cell parameters, charge / discharge data, and production line statistics; the battery comprises multiple individual cells connected in series and parallel. Based on the cell parameters, a three-dimensional thermal model and a physicochemical model of a single cell were established. Using a genetic algorithm, the kinetic parameters in the physicochemical model of each single cell are calibrated based on the charge and discharge data to obtain multiple pseudo-two-dimensional models of single cells; Based on the pseudo-two-dimensional models of the multiple single cells and the series-parallel topology of the battery, a multi-cell coupling model is established. Using the Monte Carlo simulation method, the multi-cell coupling model is corrected based on the production line statistics to obtain the multi-cell electrochemical model of the battery. The multi-cell electrochemical model and the three-dimensional thermal model are coupled to obtain the simulation model of the battery.

2. The method according to claim 1, characterized in that, The charge / discharge data includes DC resistance data, and the kinetic parameters include negative electrode exchange current density, positive electrode exchange current density, negative electrode solid-phase diffusion coefficient, positive electrode solid-phase diffusion coefficient, and liquid-phase diffusion coefficient. A genetic algorithm is used to calibrate the kinetic parameters in the physicochemical model of each single cell based on the charge / discharge data, resulting in multiple pseudo-two-dimensional models of single cells, including: Using the genetic algorithm, a calibration range is determined based on the DC resistance data, and the dynamic parameters within the calibration range are obtained; the upper and lower limits of the calibration range are preset multiples of the standard deviation under a normal distribution. Based on the kinetic parameters within the calibration range, the physicochemical models of the plurality of single cells are modified to obtain the pseudo-two-dimensional models of the plurality of single cells.

3. The method according to claim 1, characterized in that, The method of using Monte Carlo simulation to correct the multi-cell coupling model based on the production line statistics to obtain the multi-cell electrochemical model of the battery includes: Using the Monte Carlo simulation method, the capacity distribution and charge state distribution of each cell in the battery are obtained based on the production line statistics. By modifying the multiple single-cell pseudo-two-dimensional models in the multi-cell coupling model using the capacity distribution and the charge state distribution, a multi-cell electrochemical model of the battery is obtained.

4. The method according to claim 1, characterized in that, The cell parameters include the battery's geometric structure parameters, electrochemical parameters, and thermodynamic parameters. The geometric structure parameters include the dimensions of the positive electrode current collector, the negative electrode current collector, the positive electrode, the negative electrode, and the separator. The electrochemical parameters include the voltage, current, power, capacity, and energy of a single cell. Based on these cell parameters, establishing a three-dimensional thermal model and a physicochemical model of a single cell includes: Based on the thermodynamic parameters and the geometric parameters, a three-dimensional geometric model of the battery is established; The preset thermal boundary conditions are input into the three-dimensional geometric model to obtain the three-dimensional thermal model; Based on the geometric parameters and the electrochemical parameters, a physicochemical model of the single cell is established.

5. The method according to claim 1, characterized in that, The three-dimensional thermal model includes a heat source term; The process of coupling the multi-cell electrochemical model and the three-dimensional thermal model to obtain the simulation model of the battery includes: The average heat generation power of the multi-cell electrochemical model is calculated using a preset electrochemical heat generation equation. The average heat generation power is used as the heat source term of the three-dimensional thermal model and coupled into the three-dimensional thermal model to obtain the temperature distribution output by the three-dimensional thermal model. Based on the temperature distribution and the preset Arrhenius equation, the kinetic parameters in the multi-cell electrochemical model are modified to obtain a set of coupled equations; Solve the system of coupled equations to obtain the simulation model of the battery.

6. The method according to claim 5, characterized in that, The cell parameters include electrochemical parameters, which include the voltage, current, power, capacity, and energy of a single cell. The step of calculating the average heat generation power of the multi-cell electrochemical model using a preset electrochemical heat generation equation includes: Based on the electrochemical parameters and the electrochemical heat generation equation, the instantaneous heat generation power of each cell in the battery is calculated. The average instantaneous heat generation power of each battery cell is calculated to obtain the average heat generation power.

7. The method according to claim 1, characterized in that, The step of establishing a multi-cell coupling model based on the multiple single-cell pseudo-two-dimensional models and the series-parallel topology of the battery includes: Based on the series-parallel topology, determine the electrical constraints of the multi-cell coupling model; The multiple single-cell pseudo-two-dimensional models are connected according to the electrical constraints to obtain the multi-cell coupling model.

8. A device for constructing a battery simulation model, characterized in that, The device includes: The acquisition module is used to acquire the battery cell parameters, charge and discharge data, and production line statistics; the battery includes multiple single cells connected in series and parallel. The first modeling module is used to establish a three-dimensional thermal model and a physicochemical model of a single cell based on the cell parameters. The calibration module is used to calibrate the kinetic parameters in the physicochemical model of each single cell based on the charge and discharge data using a genetic algorithm, thereby obtaining multiple pseudo-two-dimensional models of single cells. The second modeling module is used to establish a multi-cell coupling model based on the multiple single-cell pseudo-two-dimensional models and the series-parallel topology of the battery. The correction module is used to correct the multi-cell coupling model based on the production line statistics using the Monte Carlo simulation method, so as to obtain the multi-cell electrochemical model of the battery. The third modeling module is used to couple the multi-cell electrochemical model and the three-dimensional thermal model to obtain the simulation model of the battery.

9. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for constructing a battery simulation model according to any one of claims 1-7.

10. A readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method for constructing the battery simulation model according to any one of claims 1-7.

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