A battery parameter determination method, computer device and storage medium

Abstract

The present application discloses a battery parameter determination method, computer equipment, computer-readable storage medium, and computer program product. The method sets a target model, uses temperature as the link data for coupled calculation of multiple sub-models in the target model, performs cyclic initial temperature parameter calculation and cyclic target temperature parameter calculation according to initial simulation parameters and target parameters, and adds a cutoff condition for the cyclic calculation in the target model. When it is determined that the cyclic calculation meets the cutoff condition, the cyclic calculation of the second temperature parameter after the target battery is charged and discharged is stopped, and when the calculation is stopped, the life of the target battery is predicted based on the total number of initial temperature parameter calculations and target temperature parameter calculations. The battery parameter determination method for predicting battery life provided by the present application predicts the life of the target battery through model calculation, which greatly improves the preset efficiency compared to manual calculation.

Description

Technical Field

[0001] The present disclosure generally relates to the field of lithium battery technology, and more particularly to a method for determining battery parameters, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] As an important battery energy storage technology today, lithium-ion batteries have been widely used in the field of new energy. An important indicator for measuring the performance of lithium-ion batteries is the cycle life of lithium-ion batteries.

[0003] At present, most people rely on manual measurement of the cycle life of lithium-ion batteries to obtain the cycle life indicator of lithium-ion batteries.

[0004] However, the above methods require a lot of time, manpower, material and financial resources, which greatly increases the cost of lithium-ion battery research and development. Therefore, it is particularly important to provide a new method to quickly predict the cycle life of lithium-ion batteries for lithium-ion battery research and development. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a battery parameter determination method, a computer device, a computer-readable storage medium, and a computer program product for quickly predicting the life of a lithium-ion battery.

[0006] A first aspect provides a method for determining battery parameters, comprising:

[0007] Constructing an initial model according to a first sub-model, a second sub-model, and a coupling relationship between the first sub-model and the second sub-model, wherein the first sub-model is used to describe a primary cell reaction of the battery during a charge and discharge process, and the second sub-model is used to describe heat generation of the battery during the charge and discharge process;

[0008] Calculating initial temperature parameters based on initial simulation parameters and the initial model to obtain first temperature parameters, wherein the initial simulation parameters include a preset initial temperature, a first model parameter of the first sub-model, and a second model parameter of the second sub-model;

[0009] Constructing a target model according to the third sub-model, the fourth sub-model, the first coupling relationship, the second coupling relationship, the third coupling relationship, and the initial model, wherein the first coupling relationship is a coupling relationship between the third sub-model and the first sub-model and the second sub-model, the second coupling relationship is a coupling relationship between the fourth sub-model and the first sub-model and the second sub-model, and the third coupling relationship is a coupling relationship between the third sub-model and the fourth sub-model, the third sub-model is used to describe side reactions of the battery during a single charge and discharge process, and the fourth sub-model is used to monitor whether the target model meets a cutoff condition for cycle calculation;

[0010] Calculating a target temperature parameter based on the target parameter and the target model to obtain a second temperature parameter, the target parameter including the first temperature parameter, the first model parameter, the second model parameter, the third model parameter of the third sub-model, and the fourth model parameter of the fourth sub-model;

[0011] During the calculation of the second temperature parameter, monitoring, by the fourth sub-model, whether the target temperature parameter calculation satisfies a cutoff condition, the cutoff condition being used to characterize a critical side reaction parameter of a side reaction during the charge and discharge process of the battery that affects the battery life;

[0012] If not, the target parameter is updated until the target temperature parameter calculation satisfies the cutoff condition, and the life of the target battery is determined based on the total number of the initial temperature parameter calculation and the target temperature parameter calculation performed when the target temperature parameter calculation satisfies the cutoff condition.

[0013] A second aspect provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method provided in the first aspect when executing the computer program.

[0014] According to a third aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method provided in the first aspect is implemented.

[0015] In a fourth aspect, a computer program product is provided, comprising a computer program, which implements the method provided in the first aspect when executed by a processor.

[0016] The present application provides a battery parameter determination method, computer device, computer-readable storage medium, and computer program product. This method addresses the problem that the cycle life of lithium-ion batteries is currently obtained by manually measuring the cycle life of lithium-ion batteries, which results in a large amount of time, manpower, material resources, and financial resources, and greatly increases the cost of lithium-ion battery research and development. The method provides a new method for predicting the cycle life of lithium-ion batteries. The method sets a target model, uses temperature as link data for coupled calculations of multiple submodels in the target model, calculates the initial temperature parameters of the cycle and the target temperature parameters of the cycle based on the initial simulation parameters and the target parameters, respectively, and adds a cutoff condition for the cycle calculation to the target model. When it is determined that the cycle calculation meets the cutoff condition, the cycle calculation of the second temperature parameter after the target battery is charged and discharged is stopped. When the calculation is stopped, the life of the target battery is predicted based on the total number of initial temperature parameter calculations and target temperature parameter calculations. The battery parameter determination method for predicting battery life provided in the present application does not require manual real-time measurement of various parameters of the target battery throughout the process. Multiple sub-models are coupled to each other, and the output of one sub-model is used as the input of another sub-model. The parameters generated by the battery after each charge and discharge are cyclically calculated, and the usage of the target battery is monitored in real time during the cyclic calculation process. When the parameters related to the battery life are monitored to determine whether they meet the cutoff conditions, the target temperature parameter calculation is stopped in time, and the prediction result of the target battery life is output. Compared with manual calculation, the preset efficiency and accuracy of predicting the target battery life through model calculation will be greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0018] Figure 1 A flowchart of a battery parameter determination method provided in this application;

[0019] Figure 2 A flowchart of another method for determining battery parameters provided in this application;

[0020] Figure 3 A flowchart of another method for determining battery parameters provided in this application;

[0021] Figure 4 A flowchart of another method for determining battery parameters provided in this application;

[0022] Figure 5 A flowchart of another method for determining battery parameters provided in this application;

[0023] Figure 6 A battery life prediction result diagram provided for this application;

[0024] Figure 7 Another battery life prediction result graph provided for this application;

[0025] Figure 8 A battery voltage prediction result diagram provided by this application;

[0026] Figure 9 Another battery voltage prediction result diagram provided by this application;

[0027] Figure 10 A battery capacity loss prediction result diagram provided in this application;

[0028] Figure 11 This is a schematic diagram of the structure of a battery parameter determination device provided in this application. DETAILED DESCRIPTION

[0029] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0030] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0031] As an important battery energy storage technology today, lithium-ion batteries have been widely used in the field of new energy. An important indicator for measuring the performance of lithium-ion batteries is the cycle life of lithium-ion batteries.

[0032] At present, most people rely on manual measurement of the cycle life of lithium-ion batteries to obtain the cycle life indicator of lithium-ion batteries.

[0033] However, the above methods require a lot of time, manpower, material and financial resources, which greatly increases the cost of lithium-ion battery research and development. Therefore, it is particularly important to provide a new method to quickly predict the cycle life of lithium-ion batteries for lithium-ion battery research and development.

[0034] Please refer to Figure 1 , Figure 1 This is a flowchart of the steps of a battery parameter determination method provided in this application, which is illustrated by taking the application of this method to a server, terminal device, etc. as an example:

[0035] Step S20: constructing an initial model based on the first sub-model, the second sub-model, and the coupling relationship between the first sub-model and the second sub-model, wherein the first sub-model is used to describe the primary cell reaction of the battery during the charging and discharging process, and the second sub-model is used to describe the heat generation of the battery during the charging and discharging process.

[0036] The coupling relationship between the first sub-model and the second sub-model may be pre-stored in a computer device and, when the battery life of an electric vehicle needs to be predicted and an initial model needs to be constructed, may be retrieved from the computer device. The coupling relationship between the first sub-model and the second sub-model may be determined by model parameters common to both the first and second sub-models, which is not limited in this application.

[0037] The first sub-model is used to describe the galvanic cell reaction of the battery during the charging and discharging process, so the galvanic cell reaction parameters generated after the galvanic cell reaction occurs during the charging and discharging process can be calculated through the first sub-model. The galvanic cell reaction parameters refer to the parameters generated by the reactions that can occur in the target battery itself during the charging and discharging process, and these reactions will not affect the life of the battery. These reactions include, for example, intercalation and deintercalation reactions, solid-phase charge conservation, liquid-phase charge conservation, lithium ion diffusion inside the battery, lithium ion diffusion in the electrolyte, etc. Exemplarily, the galvanic cell parameters are, for example, the current density of the lithium intercalation and deintercalation reaction, the solid-phase current density, the liquid-phase current density, the lithium concentration in the solid phase, and the liquid-phase volume fraction.

[0038] The five galvanic cell reactions listed above can be described by five equations:

[0039] The first relationship is used to describe the deintercalation reaction of the battery, and the first relationship is related to the current density of the deintercalation reaction, the anode transfer coefficient of the battery, the cathode transfer coefficient of the battery, the exchange current density of the deintercalation reaction, the Faraday constant, the gas constant, the overpotential of the deintercalation reaction, the temperature, the solid phase potential, the liquid phase potential, the equilibrium potential of the positive and negative electrodes of the battery, and the potential drop;

[0040] For example, the expression of the first relation is:

[0041]

[0042] η=Φ s -Φ l -E Eq -U de

[0043] Among them, α a and α care the transfer coefficients of the anode and cathode, j is the current density of the deintercalation reaction, j0 is the exchange current density of the deintercalation reaction (calculated using the reaction rate constant), F is the Faraday constant, R is the gas constant, η is the overpotential of the deintercalation reaction, T is the temperature, Φ s is the solid phase potential, Φ l is the liquid phase potential, E Eq is the equilibrium potential of the positive and negative electrodes, U de is the potential drop caused by all side reactions. Since the loss in the positive electrode is not considered, there is no U in the positive electrode. de produce.

[0044] The second relationship is used to describe the solid-phase diffusion of lithium ions inside the battery. The second relationship is related to the lithium concentration in the solid phase, the active particle radius and the solid-phase effective diffusion coefficient;

[0045] The expression of the second relational expression is, for example:

[0046]

[0047] Among them, c s is the lithium ion concentration in the solid phase, which is a preset value, r is the radius of the active particle, D s is the solid phase effective diffusion coefficient (calculated using Brugg parameter and diffusion activation energy). The positive and negative electrode equilibrium potentials in the first relationship are related to the lithium concentration in the solid phase in the second relationship.

[0048] The third relationship is used to describe the conservation of solid-phase charge during a battery's charge and discharge process. The third relationship is related to the solid-phase current density, solid-phase effective conductivity, and solid-phase potential.

[0049] The expression of the third relational equation is, for example:

[0050]

[0051] Among them, i s is the solid phase current density, σ s is the solid phase effective conductivity, Φ s is the solid phase potential.

[0052] The fourth relationship describes the conservation of liquid phase charge during a battery's charge and discharge cycle. It is related to liquid phase current density, liquid phase effective conductivity, thermodynamic factor, lithium ion transport number, liquid phase concentration, Faraday constant, gas constant, temperature, and liquid phase potential.

[0053] The expression of the fourth relational equation is, for example:

[0054]

[0055] Among them, il is the liquid current density, κ is the liquid effective conductivity, is the thermodynamic factor, which is a constant. is the lithium ion transport number, c l is the lithium ion concentration in the liquid phase, which is a preset value, Φ l is the liquid phase potential, F is the Faraday constant, R is the gas constant, and T is the temperature.

[0056] The fifth equation describes the diffusion of lithium ions in the electrolyte during a single charge / discharge cycle of the target battery. It is related to the liquid volume fraction, current density, liquid effective diffusion coefficient, lithium ion transport number, active particle specific surface area, liquid concentration, and Faraday's law.

[0057] The fifth relational expression is, for example:

[0058]

[0059] Among them, ε l is the liquid volume fraction (or electrolyte volume fraction), j is the current density, D l is the liquid phase effective diffusion coefficient (calculated using Brugg parameter and diffusion activation energy), is the lithium ion transport number, a s is the specific surface area of ​​active particles, c l is the liquid concentration, and F is the Faraday constant.

[0060] The second sub-model is used to describe the heat generated by the battery during the charging and discharging process. The relationship equations of the second sub-model include, for example:

[0061]

[0062] Where ρ is the density of the battery material, C p is the specific heat capacity of the battery material, λ is the thermal conductivity of the battery material, is the heat dissipation power, is the solid phase ohmic heating power and the liquid phase ohmic heating power, is the polarization heat generation power, is the reversible heat generation power ( is experimentally measured), a i Corresponding to the specific surface area of ​​the positive and negative electrode active particles, i i It is related to the current density of the lithium insertion and extraction reaction in the first relationship, the current density of the solid electrolyte interface membrane generation side reaction in the sixth relationship to be described later, the current density of the solid electrolyte interface membrane regeneration side reaction in the seventh relationship, and the current density of the lithium deposition side reaction.

[0063] In another embodiment of the present application, Figure 2 As shown, Figure 2 The method includes the following steps:

[0064] Step S201, constructing a geometric model of the target battery according to the design parameters of the target battery, the geometric model being used to perform charge and discharge simulation on the target battery;

[0065] Among them, the geometric model of the target battery is used to simulate the target battery itself, so the physical parameters of the target battery are required. These physical parameters include, for example, the thickness of the positive electrode of the target battery, the thickness of the negative electrode of the target battery, the thickness of the diaphragm of the target battery, the particle size of the positive electrode active material of the target battery, the particle size of the negative electrode active material of the target battery, the porosity of the diaphragm of the target battery, the solid phase volume fraction of the positive electrode of the target battery, the liquid phase volume fraction of the positive electrode of the target battery, the solid phase volume fraction of the negative electrode of the target battery, the liquid phase volume fraction of the negative electrode of the target battery, etc.

[0066] Based on the physical parameters of the target battery itself, a geometric model of the target battery can be constructed, so that the actual charging and discharging process of the target battery can be simulated through computer equipment, and there is no need to manually perform continuous charging and discharging operations on the target battery.

[0067] Since the target battery will generate some data such as primary battery reaction data, solid phase potential, liquid phase potential, solid phase concentration, liquid phase concentration, current density, battery temperature, etc. during the charging and discharging process, the above data will also be generated when the target battery is charged and discharged by computer equipment. These data are referred to as simulation parameters here.

[0068] Step S202 : obtaining simulation parameters generated by the geometric model during the charge and discharge simulation of the target battery.

[0069] Among them, since the simulation parameters generated by the geometric model are closely related to the model parameters in each sub-model, after each charge and discharge of the target battery, it is necessary to obtain the simulation parameters generated during the charge and discharge process for use in subsequent calculations of each sub-model.

[0070] After the initial model is constructed using the above method, the following operations need to be completed using the initial model:

[0071] Step S30, calculating initial temperature parameters based on the initial simulation parameters and the initial model to obtain first temperature parameters, where the initial simulation parameters include a preset initial temperature, first model parameters of the first sub-model, and second model parameters of the second sub-model;

[0072] Among them, the preset initial temperature can be determined by measuring the ambient temperature of the target battery when it leaves the factory; it can also be determined by measuring the ambient temperature of the target battery just before the cycle life prediction of the target battery. This application is not limited to this. Using the actual ambient temperature of the target battery as the starting cycle data for the cycle calculation can better simulate the target battery and obtain more accurate prediction results.

[0073] Based on the above description of the various equations for the first sub-model, the first model parameters of the first sub-model include, for example, the anode and cathode transfer coefficients, the lithium ion concentration in the solid phase, the active particle radius, the solid phase potential, the lithium ion concentration in the liquid phase, etc. It should be noted that the first model parameters include both adjustable and non-adjustable parameters.

[0074] Based on the above description of the relationship formula for the second sub-model, the second model parameters of the second sub-model include, for example, battery material density, battery material specific heat capacity, battery material thermal conductivity, and other parameters. Similarly, the second model parameters also include adjustable parameters and non-adjustable parameters.

[0075] Since the second sub-model can calculate and obtain the battery temperature parameter, inputting the initial simulation parameter into the initial model can perform a cyclic calculation of the first temperature parameter, thereby obtaining at least one first temperature parameter.

[0076] It should be noted here that the side reactions during the battery's charge and discharge process will affect the battery's lifespan, so the target battery's lifespan cannot be predicted using only the established initial model. However, if the target model is established directly, it is difficult to calibrate the sub-models within the target model because the target model contains multiple sub-models that are coupled to each other. Therefore, we first establish a simple initial model, perform the first temperature parameter calculation on the initial model, and further calibrate the first and second sub-models based on the initial model's operating conditions, thus obtaining a model with better prediction results.

[0077] Next, if Figure 3 As shown, the calculation of the initial temperature parameters is specifically described. Exemplarily, the process of calculating the initial temperature parameters includes the following steps:

[0078] Step S301, inputting a preset initial temperature, a first model parameter, and a simulation parameter into a first sub-model to obtain a first galvanic cell reaction parameter;

[0079] Step S302: input the first cell reaction parameter, the second model parameter and the simulation parameter into the second sub-model to obtain the first temperature parameter.

[0080] Combined with the above description of the relationship between the first sub-model and the second sub-model, after the preset initial temperature, first model parameters, second model parameters and simulation parameters required for the calculation are input into the first sub-model and the second sub-model, the first temperature parameter can be obtained, and the calculation is cyclic.

[0081] That is, the first initial temperature parameter calculation uses the preset initial temperature as input, the second initial temperature parameter calculation uses the first temperature parameter obtained from the first calculation as input, the second initial temperature parameter calculation again obtains a new first temperature parameter, the third initial temperature parameter calculation uses the new first temperature parameter as input, and so on, which are not listed here one by one. It is understood that other parameters remain unchanged.

[0082] Since the initial temperature parameter calculation is performed for calibrating the initial model, there is no need to perform multiple cyclic calculations. The number of initial temperature parameter calculations can be a preset number, such as 1, 2, 3, etc.

[0083] In another embodiment, the step of obtaining the first cell reaction parameter includes:

[0084] Inputting the preset initial temperature, the first model parameters, and the simulation parameters into the first relational expression of the first sub-model to obtain the lithium intercalation and deintercalation reaction current density, the overpotential, the solid phase potential, and the liquid phase potential of the lithium intercalation and deintercalation reaction, wherein the first relational expression is used to describe the lithium intercalation and deintercalation reaction of the target battery during a single charge and discharge process; the first relational expression is related to the positive and negative electrode equilibrium potentials of the target battery, which are determined by the second relational expression in the first sub-model, and the second relational expression is used to describe the diffusion of lithium ions within the target battery during a single charge and discharge process;

[0085] The solid-phase potential is input into the third relation of the first sub-model to obtain the solid-phase current density. The third relation is used to describe the solid-phase charge conservation of the target battery during a single charge and discharge process.

[0086] The preset initial temperature and liquid phase potential are input into the fourth relationship of the first sub-model to obtain the liquid phase current density. The fourth relationship is used to describe the conservation of liquid phase charge of the target battery during a charge and discharge process. The fourth relationship is related to the liquid phase concentration of the target battery. The liquid phase concentration of the target battery is determined by the fifth relationship in the first model. The fifth relationship is used to describe the diffusion of lithium ions in the electrolyte during a charge and discharge process of the target battery.

[0087] In yet another embodiment, the step of obtaining the first temperature parameter includes:

[0088] The current density and overpotential of the lithium intercalation and deintercalation reaction are input into the second sub-model to obtain the polarization heat generation power and reversible heat generation power;

[0089] Input the solid-phase current density and solid-phase potential into the second sub-model to obtain the solid-phase ohmic heat generation power;

[0090] The liquid phase current density and liquid phase potential are input into the second sub-model to obtain the liquid phase ohmic heat generation power;

[0091] A first temperature parameter is obtained according to the polarization heat generation power, the reversible heat generation power, the solid-phase ohmic heat generation power, the liquid-phase ohmic heat generation power and the second sub-model.

[0092] The above steps are combined with the above description of the expression forms of the first sub-model and the second sub-model to present a concrete expression for calculating the first primary cell reaction parameter and calculating the first temperature parameter, which will not be elaborated here.

[0093] Next, if Figure 4 As shown, how to calibrate the initial model is explained:

[0094] Step S401, determining whether the initial model needs to be calibrated based on a fitting result of at least one of the first temperature parameter and the simulation parameter with the corresponding measured data;

[0095] Step S402: If necessary, calibrate the first model parameters and the second model parameters until the gap between the data in the fitting result is smaller than the gap threshold;

[0096] Step S403 : updating the initial model based on the corrected first model parameters and the corrected second model parameters when the correction is stopped, to obtain an updated initial model.

[0097] Among them, the first temperature parameter may include multiple temperature parameters, and the multiple temperature parameters can be fitted with a temperature curve, so as to combine the results of temperature curve fitting of the temperature parameters obtained by actual measurement of other batteries of the same type, and compare the two curves. The comparison can be a comparison of the slope of the same point, the change trend of the curve, etc. If the comparison result obtained is less than the gap threshold, the initial model does not need to be corrected. On the contrary, if the gap is greater than the gap threshold, the parameters that can be corrected in the first model parameters and the second model parameters are corrected, and then the new initial temperature parameters are calculated using the updated initial model to obtain new first temperature parameters. The above judgment on whether correction is needed is repeated until the gap between the fitting results is less than the gap threshold, then the correction of the first model parameters and the second model parameters is stopped, and the updated initial model is output. That is to say, if the initial model is corrected, then the updated initial model is used when the target model is subsequently constructed.

[0098] Similarly, at least one data in the simulation parameter may be fitted as described above to obtain a corresponding fitting curve, such as the capacity retention rate and the capacity voltage. The determination method is the same as described above and will not be described in detail here.

[0099] When it is determined by any of the above methods that the first model parameters and the second model parameters need to be corrected, for example, the following parameters can be corrected: at least one of the reaction rate constants of the positive and negative electrodes, the diffusion coefficients of the positive and negative electrodes, the diffusion activation energies of the positive and negative electrodes, the Brugg coefficients at the positive and negative electrodes and the diaphragm is corrected.

[0100] It should be noted here that the above parameters may be provided with an adjustment range and an adjustment step, and at least one of the above parameters may be adjusted up or down according to the adjustment range and the adjustment step, thereby achieving correction of the first model parameter and the second model parameter.

[0101] It is understandable that the target model can also be constructed based on the uncorrected initial model, but the target model is constructed using the updated model, and the obtained target model will be more accurate in predicting the life of the target battery.

[0102] Therefore, next, we need to build a target model to predict the target battery life.

[0103] Step S40: constructing a target model based on the third sub-model, the fourth sub-model, the first coupling relationship, the second coupling relationship, the third coupling relationship, and the initial model, wherein the first coupling relationship is the coupling relationship between the third sub-model and the first sub-model and the second sub-model, the second coupling relationship is the coupling relationship between the fourth sub-model and the first sub-model and the second sub-model, and the third coupling relationship is the coupling relationship between the third sub-model and the fourth sub-model. The third sub-model is used to describe the side reactions of the battery during a single charge and discharge process, and the fourth sub-model is used to monitor whether the target model meets the cutoff condition for the cycle calculation.

[0104] Among them, it can be seen from the above that the side reactions generated by the battery during the charging and discharging process will affect the life of the battery, so this application adds a third sub-model to the initial model or the updated initial model, which is used to describe the side reactions generated by the target battery during the charging and discharging process through the third sub-model, thereby predicting the life of the target battery.

[0105] Therefore, the third sub-model can calculate the side reaction parameters. As the name implies, side reaction parameters refer to the parameters generated by reactions that shorten the target battery life during the charge and discharge process, such as the formation of a solid electrolyte interface film at the battery's negative electrode and the precipitation of lithium ions within the battery. Exemplary side reaction parameters include the current density of the generation side reaction, the current density of the regeneration side reaction, and the current density of the lithium precipitation side reaction.

[0106] The third submodel is used to obtain the battery's side reaction parameters. It consists of three side reactions: the solid electrolyte interface film formation reaction, the solid electrolyte interface film regeneration reaction, and the lithium deposition reaction. These three side reactions can be represented by the sixth, seventh, and eighth equations.

[0107] The sixth relationship is used to describe the side reaction generated by the formation of a solid electrolyte interface film at the negative electrode of the target battery during a single charge and discharge process of the target battery. The sixth relationship is related to the current density of the side reaction, the exchange current density of the side reaction, the transfer coefficient, the equilibrium potential, the potential drop, the Faraday constant, the gas constant, the temperature, and the overpotential of the side reaction.

[0108] The expression of the sixth relational equation is, for example:

[0109]

[0110] η SEI =Φ s -Φ l -E Eq,SEI -U de

[0111] Among them, j SEI Generates the side reaction current density for the solid electrolyte interface film, j 0,SEI Generates the side reaction exchange current density for the solid electrolyte interface film, α c,SEI is the transfer coefficient, E Eq,SEI is the equilibrium potential of the solid electrolyte interface membrane, which can be assigned a value of 0.4 V, U de is the potential drop caused by all side reactions.

[0112] The seventh relationship is used to describe the side reaction generated by the regeneration of the solid electrolyte interface film at the negative electrode of the target battery during a charge and discharge process of the target battery. The seventh relationship is related to the current density of the regeneration side reaction, the exchange current density of the regeneration side reaction, the expansion function, the transfer coefficient, the equilibrium potential, and the potential drop;

[0113] The expression of the seventh relational equation is, for example:

[0114]

[0115] η SEI,re =Φ s -Φ l -E Eq,SEI -U de

[0116] Among them, j SEI,re is the current density of the solid electrolyte interface film regeneration side reaction, j 0,SEI is the exchange current density of the solid electrolyte interface membrane regeneration side reaction, f exp (x) is the expansion function, which is related to the degree of lithium insertion in the particles, α c,SEI is the transfer coefficient, E Eq,SEI is the equilibrium potential of the solid electrolyte interface membrane, which can be assigned a value of 0.4 V, U de is the potential drop caused by all side reactions.

[0117] The eighth relationship is used to describe the side reaction caused by the deposition of metallic lithium at the negative electrode of the target battery during a single charge and discharge process. The eighth relationship is related to the current density of the lithium deposition side reaction, the exchange current density of the lithium deposition side reaction, the transfer coefficient, and the overpotential of the lithium deposition side reaction;

[0118] The expression of the eighth relational equation is, for example:

[0119]

[0120] η lpl =Φ s -Φ l -E Eq,Li -U de

[0121] Among them, j lpl is the current density of the lithium deposition side reaction, j 0,lpl is the exchange current density of the lithium deposition side reaction, α a,lpl With α c,lpl is the transfer coefficient, η lpl is the overpotential of the lithium deposition side reaction, E Eq,Li Take 0V, only when η lpl <0, lithium precipitation reaction will occur.

[0122] In another embodiment, after adding a third sub-model to the initial model or an updated initial model, the third sub-model can also be calibrated based on the operating conditions after the new model is run. For example, the need to calibrate the third sub-model can be determined based on the capacity retention rate and capacity-voltage fitting results. The determination method is the same as the method for determining whether to calibrate the initial model described above and is not further described here.

[0123] If the third sub-model needs to be corrected, at least one parameter among the following, for example, the density and molar mass of SEI, the current density of SEI generation and regeneration reaction, the density and molar mass of Li, the current density of lithium precipitation reaction, SEI membrane conductivity, the equilibrium potential of SEI generation and regeneration reaction, the equilibrium potential of lithium precipitation reaction, and the initial film thickness can be corrected to obtain an updated third sub-model.

[0124] Correcting the above parameters is the process of increasing or decreasing the parameters. Similarly, an adjustment range and an adjustment step may be set for each parameter, and the parameters that need to be adjusted may be corrected based on the adjustment range and the adjustment step.

[0125] Since the first, second, and third sub-models are coupled, they can be used for cyclic calculations, where the cyclic link is still the temperature parameter. However, this cycle also needs to have a cutoff time and cannot continue indefinitely. This will not only fail to achieve the desired prediction effect, but also waste resources. Therefore, when adding the third sub-model, this application also adds a fourth sub-model. The fourth sub-model monitors whether the target temperature parameter calculation meets the cutoff condition. The cutoff condition is used to characterize the critical side reaction parameter of the battery during the charging and discharging process that affects the battery life.

[0126] The fourth submodel is used to monitor whether the side reaction parameter meets the cutoff condition for calculating the target temperature parameter. Specifically, the fourth submodel is mainly used to monitor the value of the liquid volume fraction. Exemplarily, when the fourth submodel determines that the liquid volume fraction is less than or equal to 0, it can be determined that the side reaction parameter meets the cutoff condition, and the second temperature parameter calculation is stopped.

[0127] The expression of the fourth sub-model is, for example:

[0128]

[0129] ε l =ε0-ε g

[0130] Among them, c SEI is the lithium loss concentration caused by the side reaction of solid electrolyte interface film formation, c SEI,re is the lithium loss concentration caused by the side reaction of solid electrolyte interface film regeneration, c lpl is the lithium concentration lost due to the lithium deposition side reaction, δ film is the film thickness change caused by all side reactions, M SEI is the molar mass of the solid electrolyte interface film, ρ SEI is the density of SEI, M Li is the molar mass of lithium, ρ Li is the density of lithium, εg is the change in liquid phase volume fraction caused by all side reactions, ε0 is the initial liquid phase volume fraction, and ε l is the liquid phase volume fraction.

[0131] Step S50, calculating a target temperature parameter based on the target parameter and the target model to obtain a second temperature parameter, where the target parameter includes the first temperature parameter, the first model parameter, the second model parameter, the third model parameter of the third sub-model, and the fourth model parameter of the fourth sub-model;

[0132] The first temperature parameter may be the last first temperature parameter output when the initial temperature parameter is calculated using the initial model.

[0133] According to the above description of the various relationships of the third sub-model, the third model parameters of the third sub-model include, for example, the density and molar mass of SEI, the current density of SEI formation and regeneration reaction, the density and molar mass of Li, the current density of lithium precipitation reaction, SEI film conductivity, the equilibrium potential of SEI formation and regeneration reaction, the equilibrium potential of lithium precipitation reaction, initial film thickness, etc. It should be noted that the third model parameters include both adjustable parameters and non-adjustable parameters.

[0134] Based on the above description of the relationship equation for the fourth sub-model, the fourth model parameters of the fourth sub-model include, for example, the concentration of lithium loss caused by the solid electrolyte interface film formation side reaction, the density of the SEI, the density of lithium, and the change in liquid phase volume fraction caused by all side reactions. Similarly, the fourth model parameters also include adjustable parameters and non-adjustable parameters.

[0135] Since the second sub-model can calculate and obtain the battery temperature parameter, inputting the target parameter into the target model can perform a cyclic calculation of the second temperature parameter, thereby obtaining multiple second temperature parameters.

[0136] The following combination Figure 5 , the target temperature parameter calculation is specifically described. Exemplarily, the process of calculating the initial temperature parameter includes the following steps:

[0137] Step S501, inputting the first temperature parameter, the corrected first model parameter and the simulation parameter into the first sub-model to obtain the second galvanic cell reaction parameter;

[0138] Step S502, inputting the first temperature parameter, the third model parameter and the simulation parameter into the third sub-model to obtain the side reaction parameter;

[0139] Step S503 , inputting the second cell reaction parameter, the side reaction parameter, the corrected second model parameter and the simulation parameter into the second sub-model to obtain the second temperature parameter.

[0140] Among them, combined with the above description of the relationship between the first sub-model, the second sub-model and the third sub-model, after the first temperature parameter, the corrected first model parameter, the corrected second model parameter, the third model parameter and the simulation parameter required for the calculation are input into the first sub-model, the second sub-model and the third sub-model, the second temperature parameter can be obtained, and the calculation is cyclic.

[0141] That is, the first temperature parameter is input when calculating the target temperature parameter for the first time, the second temperature parameter obtained by the first calculation is input when calculating the target temperature parameter for the second time, a new second temperature parameter is obtained when calculating the target temperature parameter for the second time, and the new second temperature parameter is input when calculating the target temperature parameter for the third time, and so on. It is understood that other parameters remain unchanged.

[0142] Based on the above descriptions of the expressions of the first sub-model, the second sub-model, and the third sub-model, the steps for calculating the second galvanic cell reaction parameter, the side reaction parameter, and the second temperature parameter are described below:

[0143] First, calculating the reaction parameters of the second primary cell includes the following steps:

[0144] Inputting the first temperature parameter, the first model parameter, and the simulation parameter into a first relational expression of the first sub-model to obtain a lithium intercalation and deintercalation reaction current density, an overpotential, a solid phase potential, and a liquid phase potential, wherein the first relational expression is used to describe the lithium intercalation and deintercalation reaction of the target battery during a single charge and discharge process; the first relational expression is related to the positive and negative electrode equilibrium potentials of the target battery, which are determined by a second relational expression in the first sub-model, and the second relational expression is used to describe the diffusion of lithium ions within the target battery during a single charge and discharge process;

[0145] Inputting the solid-phase potential into a third relational expression of the first sub-model to obtain a solid-phase current density, wherein the third relational expression is used to describe the solid-phase charge conservation of the target battery during a single charge and discharge process;

[0146] The first temperature parameter and the liquid phase potential are input into the fourth relationship of the first sub-model to obtain the liquid phase current density, the fourth relationship is used to describe the conservation of liquid phase charge of the target battery during a charge and discharge process, the fourth relationship is related to the liquid phase concentration of the target battery, and the liquid phase concentration of the target battery is determined by the fifth relationship in the first model, and the fifth relationship is used to describe the diffusion of lithium ions in the electrolyte during a charge and discharge process of the target battery.

[0147] Second, calculate the side reaction parameters, including the following steps:

[0148] Inputting the first temperature parameter, the third model parameter, and the simulation parameter into a sixth relational equation of the third sub-model to obtain a generated side reaction current density, wherein the sixth relational equation is used to describe a side reaction generated by formation of a solid electrolyte interface film at the negative electrode of the target battery during a single charge and discharge process of the target battery;

[0149] Inputting the first temperature parameter, the third model parameter, and the simulation parameter into a seventh relational equation of the third sub-model to obtain a regeneration side reaction current density, wherein the seventh relational equation is used to describe a side reaction generated by regeneration of a solid electrolyte interface film at the negative electrode of the target battery during a charge and discharge process of the target battery;

[0150] The first temperature parameter, the third model parameter and the simulation parameter are input into the eighth relational equation of the third sub-model to obtain the lithium plating side reaction current density, and the eighth relational equation is used to describe the side reaction caused by the deposition of metallic lithium at the negative electrode of the target battery during a charge and discharge process of the target battery.

[0151] Third, calculating the second temperature parameter includes the following steps:

[0152] Inputting the lithium insertion and deintercalation reaction current density, the overpotential of the lithium insertion and deintercalation reaction, the generation side reaction current density, the regeneration side reaction current density, and the lithium deposition side reaction current density into the second sub-model to obtain polarization heat generation power and reversible heat generation power;

[0153] Inputting the solid-phase current density and the solid-phase potential into the second sub-model to obtain the solid-phase ohmic heat generation power;

[0154] Inputting the liquid phase current density and the liquid phase potential into the second sub-model to obtain liquid phase ohmic heat generation power;

[0155] The second temperature parameter is obtained according to the polarization heat generation power, the reversible heat generation power, the solid-phase ohmic heat generation power, the liquid-phase ohmic heat generation power and the second sub-model.

[0156] The above steps are a concrete expression of the calculation of the second primary cell reaction parameters, side reaction parameters and second temperature parameters, presented in combination with the above description of the expression forms of the first sub-model, the second sub-model and the third sub-model, and will not be repeated here.

[0157] Step S60, during the calculation of the second temperature parameter, monitoring, by the fourth sub-model, whether the target temperature parameter calculation satisfies a cutoff condition, the cutoff condition being used to characterize a critical side reaction parameter of a side reaction during the charge and discharge process of the battery that affects the battery life;

[0158] Among them, the cutoff condition is used to limit the number of cycles of calculation of the above-mentioned target temperature parameter. As long as the target temperature parameter calculation does not meet the cutoff condition, the calculation process of the target temperature parameter calculation will continue to loop, that is, continuously repeating steps S501-S503. During the loop, the second temperature parameter obtained by this calculation will be used as the new input of the first sub-model and the third sub-model in the next calculation to repeat the above-mentioned calculation process to obtain the second temperature parameter of the target battery in a new charge and discharge process. Then, a second temperature parameter can be calculated for each charge and discharge of the target battery. As long as the target temperature parameter calculation does not meet the cutoff condition, the second temperature parameter will be continuously updated.

[0159] The cutoff condition provided in this application can be determined by the data of the liquid phase volume fraction of the target battery. For example, when the liquid phase volume fraction of the target battery is less than or equal to 0, it is determined that the side reaction parameter meets the cutoff condition. The liquid phase volume fraction of the target battery can also be called the volume fraction of the target battery electrolyte. For lithium-ion batteries, the commonly used electrolyte is an organic solvent electrolyte, which can provide high ion mobility and good chemical stability, and has high output power and low internal resistance. At the same time, the organic solvent electrolyte also has high conductivity and good mechanical properties, which can better enhance battery performance. The liquid phase volume fraction of the target battery is less than or equal to 0, which means that the target battery electrolyte may have been exhausted. As a substance that can directly affect the battery life, the exhaustion of the electrolyte means that the battery can no longer be used. Therefore, based on this data, the target temperature parameter calculation can be monitored to see whether it meets the cutoff condition.

[0160] Based on the above description of the fourth sub-model, it can be seen that the present application can continuously calculate the liquid phase volume fraction data of the target battery through the fourth sub-model.

[0161] Step S70: If not, update the target parameters until the target temperature parameter calculation meets the cutoff condition, and determine the target battery life based on the total number of initial temperature parameter calculations and target temperature parameter calculations performed when the target temperature parameter calculation meets the cutoff condition.

[0162] The computer device may be provided with a counter, which can count the total number of temperature parameter calculations performed. When the target temperature parameter calculation stops, the counter can output the number of times and determine the target battery life based on the number of times.

[0163] Below, the battery parameter determination method provided in this application is exemplified by taking a battery cell whose positive electrode material is lithium iron phosphate, whose negative electrode material is graphite, and whose electrolyte uses LiPF6 solvent material as a target battery, and performing cyclic charge and discharge of the target battery at constant power as an example:

[0164] First, a geometric model is constructed based on the physical parameters of the target battery.

[0165] Among them, the physical parameters of the target battery are negative electrode thickness 56um, diaphragm thickness 15um, positive electrode thickness 73um, negative electrode particle diameter 11.1um, positive electrode particle diameter 1.17um, negative electrode solid phase volume fraction 0.66, positive electrode solid phase volume fraction 0.63, negative electrode liquid phase volume fraction 0.27, positive electrode liquid phase volume fraction 0.32, diaphragm porosity 0.42, negative electrode maximum lithium insertion concentration 30555mol / m^3, positive electrode maximum lithium insertion concentration 23830mol / m^3.

[0166] Second, set the cycle conditions in the model as follows:

[0167] (1) Charge to 3.65V at a constant power of 408W;

[0168] (2) Stand for 1800 seconds;

[0169] (3) Discharge to 2.5V at a constant power of 408W.

[0170] Third, set the cutoff condition to liquid volume fraction ≤ 0;

[0171] Fourth, the initial temperature is set to 25 ° C, and together with the negative electrode reaction rate constant of 1.1e-11m / s, the positive electrode reaction rate constant of 1e-11m / s, the negative electrode diffusion coefficient of 9e-14m^2 / s, the positive electrode diffusion coefficient of 6.5e-17m^2 / s, the negative electrode diffusion activation energy of 88000J / mol, the positive electrode diffusion activation energy of 88000J / mol, the negative electrode Brugg coefficient of 1.65, the positive electrode Brugg coefficient of 1.65, the separator Brugg coefficient of 1.65, the negative electrode conductivity of 333S / m, the positive electrode conductivity of 3.3S / m, the initial concentration of the electrolyte of 1000mol / m^3, the negative electrode density of 2200kg / m^3, the positive electrode density of 3600kg / m^3, the separator density of 1210kg / m^3, the negative electrode thermal conductivity of 2.6W / ( m·K), positive electrode thermal conductivity 1.5W / (m·K), membrane thermal conductivity 1.1W / (m·K), negative electrode specific heat capacity 881J / (kg·K), positive electrode specific heat capacity 1071J / (kg·K), membrane specific heat capacity 1978J / (kg·K), SEI film density 2690kg / m^3, SEI film molar mass 0.1kg / mol, lithium density 534kg / m^3, lithium molar mass 0.00694kg / mol, SEI formation and SEI regeneration current density 1.32E-8A / m^2, lithium precipitation current density 0.075A / m^2, SEI formation and SEI regeneration equilibrium potential 0.4V, lithium precipitation equilibrium potential 0V, initial film thickness 5nm, SEI film conductivity 5e-6S / m. These parameters are input into the target model together.

[0172] Fifth, stop the loop calculation and obtain multiple temperature parameters as output.

[0173] Sixth, the life of the target battery is predicted based on a plurality of temperature parameters or the number of times the temperature parameters are calculated.

[0174] like Figure 6 and Figure 7 As shown, it is the prediction result of the target battery life through the above process. By fitting the capacity retention rate of 1000 cycles (the maximum error between the prediction and the actual measurement of the present invention is about 0.15%, which ensures a high cycle life prediction accuracy), it can be predicted that the accelerated attenuation node of this lithium iron phosphate battery (that is, the cutoff condition is met) has a cycle number of 6300, and the corresponding capacity retention rate is 71.43%. And the fitting of the simulated voltage and the measured voltage at the 600th cycle is verified (the maximum difference between the simulated voltage and the measured voltage is about 20mV, which ensures a high accuracy in predicting the voltage platform of any number of cycles). The results are shown as follows. Figure 8 shown.

[0175] At the same time, the target model of this application can predict the voltage, rate performance, temperature change and capacity change of any number of cycles in the battery cell cycle process. The voltage comparison results of 600 cycles and 3300 cycles are as follows: Figure 9 shown.

[0176] The temperature change of the target battery during the charging and discharging process is determined based on multiple temperature parameters output by the target model and a preset initial temperature.

[0177] The voltage of the target battery can be calculated based on the solid phase potential of the positive electrode and the solid phase potential of the negative electrode obtained from the above electrochemical equation, heat generation equation, and side reaction equation:

[0178]

[0179] Where V(t) is the output battery voltage, is the solid phase potential of the positive electrode, is the solid phase potential of the negative electrode.

[0180] The change in battery capacity can be calculated by integrating the current:

[0181] Q cell =∫I cell dt

[0182] Among them, Q cell is the battery capacity, I cell is the current and t is the time.

[0183] In addition, the target model can also obtain the capacity loss percentage caused by each side reaction during the entire cycle. That is, the rate performance change of the target battery, δ side,i is the percentage of each side reaction loss, Q side,i is the capacity loss of each side reaction (generation side reaction, regeneration side reaction, lithium precipitation side reaction), Q cell is the battery capacity, the result is as follows Figure 10 The linear attenuation stage is mainly due to the attenuation caused by the formation of solid electrolyte interface film. The lithium precipitation reaction occurs in the late cycle, which causes the capacity retention rate to change from linear attenuation to nonlinear attenuation.

[0184] It should be noted that although the operations of the present method are depicted in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in that particular order, or that all illustrated operations must be performed to achieve the desired results. Rather, the steps depicted in the flowcharts may be performed in a different order. For example, ... Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be broken down into multiple steps. For example, ...

[0185] Further references Figure 11 , which shows a structural block diagram of a computer device according to an embodiment of the present application.

[0186] The computer device includes a memory and a processor, and when the computer program is executed by the processor, any one of the above battery parameter determination methods is implemented.

[0187] In particular, according to the embodiments of the present disclosure, the above reference Figure 1-5 The described processes may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising instructions for executing Figure 1-5 The program code of the method.

[0188] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0189] The units or modules involved in the embodiments described in this application may be implemented by software or hardware. The units or modules described may also be provided in a processor. For example, it may be described as follows: a processor includes unit XX, unit YY, and unit ZZ. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves. For example, unit XX may also be described as a "unit for XX."

[0190] As another aspect, the present application further provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus described in the above embodiments, or a separate computer-readable storage medium not incorporated into the apparatus. The computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the formula input method described in the present application.

[0191] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in this application.

Claims

1. A method for determining battery parameters, characterized in that: include: Constructing an initial model according to a first sub-model, a second sub-model, and a coupling relationship between the first sub-model and the second sub-model, wherein the first sub-model is used to describe a primary cell reaction of the battery during a charge and discharge process, and the second sub-model is used to describe heat generation of the battery during the charge and discharge process; Calculating initial temperature parameters based on initial simulation parameters and the initial model to obtain first temperature parameters, wherein the initial simulation parameters include a preset initial temperature, a first model parameter of the first sub-model, and a second model parameter of the second sub-model; Constructing a target model according to the third sub-model, the fourth sub-model, the first coupling relationship, the second coupling relationship, the third coupling relationship, and the initial model, wherein the first coupling relationship is a coupling relationship between the third sub-model and the first sub-model and the second sub-model, the second coupling relationship is a coupling relationship between the fourth sub-model and the first sub-model and the second sub-model, and the third coupling relationship is a coupling relationship between the third sub-model and the fourth sub-model, the third sub-model is used to describe side reactions of the battery during a single charge and discharge process, and the fourth sub-model is used to monitor whether the target model meets a cutoff condition for cycle calculation; Calculating a target temperature parameter based on the target parameter and the target model to obtain a second temperature parameter, the target parameter including the first temperature parameter, the first model parameter, the second model parameter, the third model parameter of the third sub-model, and the fourth model parameter of the fourth sub-model; During the calculation of the second temperature parameter, monitoring, by the fourth sub-model, whether the target temperature parameter calculation satisfies a cutoff condition, the cutoff condition being used to characterize a critical side reaction parameter of a side reaction during the charge and discharge process of the battery that affects the battery life; If not, the target parameter is updated until the target temperature parameter calculation satisfies the cutoff condition, and the target battery life is determined based on the total number of the initial temperature parameter calculation and the target temperature parameter calculation performed when the target temperature parameter calculation satisfies the cutoff condition.

2. The method according to claim 1, characterized in that The method further comprises: Constructing a geometric model of the target battery according to the design parameters of the target battery, wherein the geometric model is used to perform charge and discharge simulation on the target battery; Simulation parameters generated by the geometric model during a charge and discharge simulation of the target battery are obtained.

3. The method according to claim 2, characterized in that The initial simulation parameters also include the simulation parameters, and the initial temperature parameter calculation based on the initial simulation parameters and the initial model to obtain the first temperature parameter includes: Inputting the preset initial temperature, the first model parameters and the simulation parameters into the first sub-model to obtain first galvanic cell reaction parameters; The first cell reaction parameter, the second model parameter and the simulation parameter are input into the second sub-model to obtain the first temperature parameter.

4. The method according to claim 3, characterized in that The method further comprises: Determining whether the initial model needs to be corrected based on a fitting result of at least one of the first temperature parameter and the simulation parameter with corresponding measured data; If it is determined to be necessary, calibrating the first model parameters and the second model parameters until the difference between the fitting results is less than a difference threshold; The initial model is updated based on the corrected first model parameters and the corrected second model parameters when the correction is stopped to obtain an updated initial model.

5. The method according to claim 4, characterized in that The calculating the target temperature parameter based on the target parameter and the target model to obtain the second temperature parameter includes: Inputting the first temperature parameter, the corrected first model parameter, and the simulation parameter into the first sub-model to obtain a second galvanic cell reaction parameter; Inputting the first temperature parameter, the third model parameter and the simulation parameter into the third sub-model to obtain a side reaction parameter; The second primary cell reaction parameter, the side reaction parameter, the corrected second model parameter and the simulation parameter are input into the second sub-model to obtain the second temperature parameter.

6. The method according to claim 3, characterized in that The step of inputting the preset initial temperature, the first model parameter, and the simulation parameter into the first sub-model to obtain the first galvanic cell reaction parameter includes: Inputting the preset initial temperature, the first model parameters, and the simulation parameters into a first relational expression of the first sub-model to obtain a lithium intercalation and deintercalation reaction current density, an overpotential, a solid-phase potential, and a liquid-phase potential, wherein the first relational expression is used to describe the lithium intercalation and deintercalation reaction of the target battery during a single charge and discharge process; the first relational expression is related to the positive and negative electrode equilibrium potentials of the target battery, which are determined by a second relational expression in the first sub-model, and the second relational expression is used to describe the diffusion of lithium ions within the target battery during a single charge and discharge process; Inputting the solid-phase potential into a third relational equation of the first sub-model to obtain a solid-phase current density, wherein the third relational equation is used to describe the solid-phase charge conservation of the target battery during a single charge and discharge process; The preset initial temperature and the liquid phase potential are input into the fourth relationship of the first sub-model to obtain the liquid phase current density, the fourth relationship is used to describe the conservation of liquid phase charge of the target battery during a charge and discharge process, the fourth relationship is related to the liquid phase concentration of the target battery, and the liquid phase concentration of the target battery is determined by the fifth relationship in the first model, and the fifth relationship is used to describe the diffusion of lithium ions in the electrolyte during a charge and discharge process of the target battery.

7. The method according to claim 6, characterized in that The step of inputting the first cell reaction parameter, the second model parameter, and the simulation parameter into the second sub-model to obtain the first temperature parameter includes: Inputting the lithium intercalation and deintercalation reaction current density and the lithium intercalation and deintercalation reaction overpotential into the second sub-model to obtain polarization heat generation power and reversible heat generation power; Inputting the solid-phase current density and the solid-phase potential into the second sub-model to obtain the solid-phase ohmic heat generation power; Inputting the liquid phase current density and the liquid phase potential into the second sub-model to obtain liquid phase ohmic heat generation power; The first temperature parameter is obtained according to the polarization heat generation power, the reversible heat generation power, the solid-phase ohmic heat generation power, the liquid-phase ohmic heat generation power and the second sub-model.

8. The method according to claim 5, characterized in that The step of inputting the first temperature parameter, the corrected first model parameter, and the simulation parameter into the first sub-model to obtain the second galvanic cell reaction parameter includes: Inputting the first temperature parameter, the first model parameter, and the simulation parameter into a first relational expression of the first sub-model to obtain a lithium intercalation and deintercalation reaction current density, an overpotential, a solid phase potential, and a liquid phase potential, wherein the first relational expression is used to describe the lithium intercalation and deintercalation reaction of the target battery during a single charge and discharge process; the first relational expression is related to the positive and negative electrode equilibrium potentials of the target battery, which are determined by a second relational expression in the first sub-model, and the second relational expression is used to describe the diffusion of lithium ions within the target battery during a single charge and discharge process; Inputting the solid-phase potential into a third relational equation of the first sub-model to obtain a solid-phase current density, wherein the third relational equation is used to describe the solid-phase charge conservation of the target battery during a single charge and discharge process; The first temperature parameter and the liquid phase potential are input into the fourth relationship of the first sub-model to obtain the liquid phase current density, the fourth relationship is used to describe the conservation of liquid phase charge of the target battery during a charge and discharge process, the fourth relationship is related to the liquid phase concentration of the target battery, and the liquid phase concentration of the target battery is determined by the fifth relationship in the first model, and the fifth relationship is used to describe the diffusion of lithium ions in the electrolyte during a charge and discharge process of the target battery.

9. The method according to claim 8, characterized in that The step of inputting the first temperature parameter, the third model parameter, and the simulation parameter into the third sub-model to obtain the side reaction parameter comprises: Inputting the first temperature parameter, the third model parameter, and the simulation parameter into a sixth relational equation of the third sub-model to obtain a generated side reaction current density, wherein the sixth relational equation is used to describe a side reaction generated by formation of a solid electrolyte interface film at the negative electrode of the target battery during a single charge and discharge process of the target battery; Inputting the first temperature parameter, the third model parameter, and the simulation parameter into a seventh relational equation of the third sub-model to obtain a regeneration side reaction current density, wherein the seventh relational equation is used to describe a side reaction generated by regeneration of a solid electrolyte interface film at the negative electrode of the target battery during a charge and discharge process of the target battery; The first temperature parameter, the third model parameter and the simulation parameter are input into the eighth relational equation of the third sub-model to obtain the lithium plating side reaction current density, and the eighth relational equation is used to describe the side reaction caused by the deposition of metallic lithium at the negative electrode of the target battery during a charge and discharge process of the target battery.

10. The method according to claim 9, characterized in that The step of inputting the second cell reaction parameter, the side reaction parameter, the corrected second model parameter, and the simulation parameter into the second sub-model to obtain the second temperature parameter includes: Inputting the lithium insertion and deintercalation reaction current density, the overpotential of the lithium insertion and deintercalation reaction, the generation side reaction current density, the regeneration side reaction current density, and the lithium deposition side reaction current density into the second sub-model to obtain polarization heat generation power and reversible heat generation power; Inputting the solid-phase current density and the solid-phase potential into the second sub-model to obtain the solid-phase ohmic heat generation power; Inputting the liquid phase current density and the liquid phase potential into the second sub-model to obtain liquid phase ohmic heat generation power; The second temperature parameter is obtained according to the polarization heat generation power, the reversible heat generation power, the solid-phase ohmic heat generation power, the liquid-phase ohmic heat generation power and the second sub-model.

11. The method according to claim 6 or 8, characterized in that The first relationship is related to the current density of the lithium intercalation and deintercalation reaction, the anode transfer coefficient of the target battery, the cathode transfer coefficient of the target battery, the exchange current density of the lithium intercalation and deintercalation reaction, the Faraday constant, the gas constant, the overpotential of the lithium intercalation and deintercalation reaction, the temperature, the solid phase potential, the liquid phase potential, the equilibrium potential of the positive and negative electrodes of the target battery, and the potential drop; The second relationship is related to the lithium concentration in the solid phase, the active particle radius and the solid phase effective diffusion coefficient; The third relationship is related to the solid phase current density, the solid phase effective conductivity and the solid phase potential; The fourth relationship is related to the liquid phase current density, the liquid phase effective conductivity, the thermodynamic factor, the lithium ion transport number, the liquid phase concentration, the Faraday constant, the gas constant, the temperature and the liquid phase potential; The fifth relationship is related to the liquid phase volume fraction, the current density, the liquid phase effective diffusion coefficient, the lithium ion transport number, the active particle specific surface area, the liquid phase concentration and the Faraday equation.

12. The method according to claim 9, characterized in that The sixth relationship is related to the current density of the side reaction, the exchange current density of the side reaction, the transfer coefficient, the equilibrium potential, the potential drop, the Faraday constant, the gas constant, the temperature, and the overpotential of the side reaction; The seventh relationship is related to the current density of the regeneration side reaction, the exchange current density of the regeneration side reaction, the expansion function, the transfer coefficient, the equilibrium potential and the potential drop; The eighth relationship is related to the current density of the lithium deposition side reaction, the exchange current density of the lithium deposition side reaction, the transfer coefficient and the overpotential of the lithium deposition side reaction.

13. The method according to claim 12, characterized in that The monitoring of whether the target temperature parameter calculation satisfies a cutoff condition by the fourth sub-model includes: Determining the liquid phase volume fraction caused by all side reactions of the target battery by the fourth submodel; the side reaction parameters include at least one of the loss lithium concentration caused by the generation side reaction, the loss lithium concentration caused by the regeneration side reaction, the loss lithium concentration caused by the lithium precipitation side reaction, the film thickness change caused by all side reactions, the molar mass of the solid electrolyte interface film, the density of the solid electrolyte interface film, the molar mass of lithium, and the density of lithium; If the difference between the initial liquid phase volume fraction of the target battery and the liquid phase volume fraction is less than or equal to a preset difference, it is determined that the target temperature parameter calculation does not meet the cutoff condition.

14. The method according to claim 1, wherein The method further includes: during the execution of the initial temperature parameter calculation and the target temperature parameter calculation, determining a temperature change of the target battery during the charging and discharging process according to a plurality of temperature parameters output by the target model and the preset initial temperature; determining a battery voltage change output by the target battery according to a difference between the solid phase potential of the positive electrode of the target battery and the solid phase potential of the negative electrode of the target battery output by the target model; determining a battery capacity change output by the target battery according to the current of the target battery output by the target model; The target battery rate performance change is determined based on the lithium loss concentration caused by the solid electrolyte interface membrane generation side reaction, the lithium loss concentration caused by the solid electrolyte interface membrane regeneration side reaction, and the lithium loss concentration caused by the lithium precipitation side reaction output by the target model.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 14 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 14 are implemented.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 14 are implemented.

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