Battery simulation model construction method based on electrolyte intrinsic ion transmission characteristic parameter optimization and battery simulation model

Abstract

The invention relates to the technical field of batteries, in particular to a battery simulation model construction method based on electrolyte intrinsic ion transmission characteristic parameter optimization and a battery simulation model. Comprising the following steps: 1, acquiring intrinsic ion transmission characteristic parameters of the electrolyte, and constructing an extrapolation function; 2, obtaining battery positive and negative electrode materials, battery process and structural related parameters, and establishing an electrochemical-thermal coupling model; 3, introducing intrinsic ion transmission characteristic parameters of the electrolyte into the electrochemical-thermal coupling model; 4, the model is coupled with actual charging and discharging data, and precise modeling and charging and discharging curve optimization in the battery charging process are achieved. According to the technical scheme, the electrochemical-thermal coupling behavior of the battery in the actual operation process can be truly reflected, the accuracy and applicability of simulation prediction are improved, the method is suitable for charging strategy design and performance evaluation of different types of batteries, and the high-precision analogue simulation requirement of the high-power-density and high-energy-density lithium ion battery is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery modeling, in particular to a battery simulation model construction method based on electrolyte intrinsic ion transport characteristic parameter optimization and a battery simulation model. BACKGROUND

[0002] Lithium-ion batteries have been widely used in new energy vehicles, energy storage power stations and consumer electronics due to their high energy density and long cycle life. With the increasing complexity of battery systems, how to accurately simulate the electrochemical and thermal response behavior during charging has become an important means for battery design and management strategy optimization.

[0003] Currently, the mainstream battery simulation modeling method mainly adopts a pseudo two-dimensional model (P2D model) or a multi-physical field coupling model. Among them, the multi-physical field simulation method (such as the simulation software based on COMSOL) has become a powerful tool for studying the internal mechanism of the battery because it can consider multiple coupling factors such as electric field, concentration field and temperature field at the same time.

[0004] However, in the existing multi-physical field modeling practice, many models simplify the electrolyte parameters and only set them as fixed constants or empirical values, ignoring their variation characteristics under different temperature and concentration conditions. As the key medium for ion transport in the battery, the transport characteristics (such as ion conductivity, lithium ion transference number and liquid phase diffusion coefficient) of the electrolyte have a significant impact on voltage response, polarization phenomenon and heat source distribution. If the electrolyte parameters are not accurately set, it is easy to cause deviations between the simulation results and the actual test data, especially under complex working conditions such as fast charging and high rate charging, the error is more obvious.

[0005] Therefore, there is still a lack of a simulation modeling method based on the actual electrolyte ion transport characteristic parameters, which solves the problem of the lack of electrolyte parameter variation characteristics in the simulation model. SUMMARY

[0006] The present application provides a battery simulation model construction method based on electrolyte intrinsic ion transport characteristic parameter optimization and a battery simulation model, which aims to solve the problem of inaccurate electrolyte parameter setting in the existing battery simulation model and the inability to truly reflect the electrolyte transport behavior. The intrinsic ion transport characteristic parameters of the electrolyte are introduced into the multi-physical field model, which can realize the true reduction of the charging process and the optimization of the simulation curve by coupling with the experimental data, and improve the prediction accuracy and actual applicability of the model.

[0007] In order to achieve the above purpose, the present application provides a battery simulation model construction method based on electrolyte intrinsic ion transport characteristic parameter optimization, which comprises the following steps:

[0008] S1, obtain electrolyte intrinsic ion transport characteristic parameters, the electrolyte intrinsic ion transport characteristic parameters include: ion conductivity parameters, lithium ion transference number parameters and liquid phase ion diffusion coefficient; adopt polynomial equation and Arrhenius equation to fit, construct the extrapolation function of ion conductivity parameters and temperature and lithium salt concentration, adopt polynomial equation to fit, construct the extrapolation function of lithium ion transference number parameters and temperature and lithium salt concentration;

[0009] S2, obtain the battery geometry, electrochemical parameters and battery structural related parameters of the target battery;The electrochemical parameters include positive and negative electrode material parameters, separator parameters, copper and aluminum foil parameters and battery process parameters;

[0010] S3, construct an electrochemical-thermal coupling simulation model based on the electrochemical parameters, battery geometry and battery structural related parameters, and output the traditional simulation model charging curve;

[0011] S4, introduce the extrapolation function of ion conductivity parameters and temperature and lithium salt concentration, the extrapolation function of lithium ion transference number parameters and temperature and lithium salt concentration and liquid phase ion diffusion coefficient, optimize the traditional simulation model charging curve, and obtain the battery simulation model based on electrolyte intrinsic ion transport characteristic parameter optimization.

[0012] The ion conductivity in the electrolyte ion transport characteristic parameter represents the ion migration behavior in the mass transfer process, and represents the ability of lithium ions (Li + ) and anions in the electrolyte to conduct current under the driving of electric field. Ion conductivity will change with the change of lithium salt concentration and temperature in electrolyte, so the relationship between ion conductivity parameters and electrolyte lithium salt concentration and its relationship with temperature need to be obtained when obtaining parameters.

[0013] The lithium ion transference number in the electrolyte ion transport characteristic parameter mainly describes the proportion of charge transmission borne by lithium ions under unit current, reflects the division of labor of lithium ions and anions in current conduction, and has important influence on electrode concentration polarization and lithium dendrite formation.

[0014] Preferably, the electrolyte intrinsic ion transport characteristic parameters in step S1 include: using ion conductivity meter to measure the change rule experimental data of ion conductivity with temperature and lithium salt concentration, and using constant potential polarization method to measure the change rule experimental data of lithium ion transference number with temperature and lithium salt concentration.

[0015] Preferably, the method for determining the variation of ion conductivity with temperature and lithium salt concentration specifically comprises: placing the electrolyte to be measured in a clean and dry test tube, inserting a pre-calibrated electrode probe into the test tube containing the electrolyte, applying a low-amplitude alternating voltage to the sample using an ion conductivity meter, measuring the current passing through the electrolyte, and calculating the ion conductivity of the electrolyte.

[0016] The method for determining the variation of ion conductivity with temperature and lithium salt concentration specifically comprises: placing the electrolyte to be measured in a clean and dry test tube, inserting a pre-calibrated electrode probe into the test tube containing the electrolyte, applying a low-amplitude alternating voltage to the sample using an ion conductivity meter, measuring the current passing through the electrolyte, and calculating the ion conductivity of the electrolyte.

[0017] The method for determining the variation of ion conductivity with temperature and lithium salt concentration specifically comprises: placing the electrolyte to be measured in a clean and dry test tube, inserting a pre-calibrated electrode probe into the test tube containing the electrolyte, applying a low-amplitude alternating voltage to the sample using an ion conductivity meter, measuring the current passing through the electrolyte, and calculating the ion conductivity of the electrolyte.

[0018] Preferably, the method for determining the variation of ion conductivity with temperature and lithium salt concentration specifically comprises: placing the electrolyte to be measured in a clean and dry test tube, inserting a pre-calibrated electrode probe into the test tube containing the electrolyte, applying a low-amplitude alternating voltage to the sample using an ion conductivity meter, measuring the current passing through the electrolyte, and calculating the ion conductivity of the electrolyte.

[0019] The method for determining the variation of ion conductivity with temperature and lithium salt concentration specifically comprises: placing the electrolyte to be measured in a clean and dry test tube, inserting a pre-calibrated electrode probe into the test tube containing the electrolyte, applying a low-amplitude alternating voltage to the sample using an ion conductivity meter, measuring the current passing through the electrolyte, and calculating the ion conductivity of the electrolyte.

[0020] (1);

[0021] wherein, is the lithium ion transference number, and is the initial and steady-state interfacial impedance, obtained by equivalent circuit fitting of electrochemical impedance spectroscopy, with units of Ω, and is the initial and steady-state interfacial impedance, obtained by equivalent circuit fitting of electrochemical impedance spectroscopy, with units of Ω, is the initial and steady-state interfacial impedance, obtained by equivalent circuit fitting of electrochemical impedance spectroscopy, with units of Ω.

[0022] Preferably, the liquid phase diffusion coefficient in step S1 represents the rate of ion diffusion driven by concentration gradient under the condition of no external current, and its expression is obtained according to the Walden rule, as shown in formula (2):

[0023] (2);

[0024] wherein, D is the liquid diffusion coefficient, with the unit of m 2 / s, σ is the equivalent conductivity, with the unit of S m 2 / mol, R is the gas constant, with the unit of J / (mol K), T is the temperature of the electrolyte, with the unit of K, z is the charge number of the ion, F is the Faraday constant, with the unit of C / mol;

[0025] The equivalent conductivity represents the total conductive capacity generated in the solution, considering the charge number, migration speed and dissociation degree of the ion, and can be converted with the ion conductivity, and the conversion formula is shown in formula (3):

[0026] (3);

[0027] wherein, σ is the ion conductivity, with the unit of S / m, C is the concentration of the lithium salt of the electrolyte, with the unit of mol / m 3 According to the unit conversion of formula (3), the equivalent conductivity can be obtained, and by combining formula (3) with formula (2), the expression of the liquid diffusion coefficient can be obtained, as shown in formula (4):

[0028] (4);

[0029] Preferably, in the step S1, the polynomial equation and the Arrhenius equation are used for fitting to construct the extrapolation function of the ion conductivity parameter and the temperature and the lithium salt concentration, and the specific steps include the following steps:

[0030] Based on the experimental data of the variation law of the ion conductivity with the temperature and the lithium salt concentration, the function relationship between the ion conductivity and the concentration is fitted by the polynomial fitting model, and the relationship between the ion conductivity and the temperature is fitted by the Arrhenius equation;

[0031] The relationship between the temperature and the ion conductivity fitted by the Arrhenius equation is shown in formula (5):

[0032] (5);

[0033] wherein, σ is the ion conductivity, with the unit of S / m, A is the pre-exponential factor, with the unit of S / m, E is the activation energy, with the unit of J / mol, reflecting the potential barrier to be overcome by the ion movement; R is the gas constant, with the unit of J / (mol K), which is the Boltzmann constant, T is the temperature of the electrolyte, in K.

[0034] The ion conductivity of the electrolyte shows a typical exponential growth trend with temperature change in a wide temperature range, and compared with other empirical polynomial fitting models, the Arrhenius equation has fewer parameters, clear structure, and parameters with clear physical meaning, facilitating result interpretation.

[0035] Preferably, the step S1 of fitting by using a polynomial equation to construct an extrapolation function of the lithium ion transference number parameter and temperature and lithium salt concentration comprises: constructing a bivariate coupled empirical function model of the lithium ion transference number parameter and temperature and lithium salt concentration by using a polynomial equation, and the expression is as shown in formula (6): (6);

[0036] wherein, , , , , , , , , , , , , , , is a polynomial coefficient, is the concentration of the lithium salt of the electrolyte, in mol / m 3 , T is the temperature of the electrolyte, in K.

[0037] The construction of the lithium ion transference number extrapolation function is that the lithium ion transference number is simultaneously affected by temperature and lithium salt concentration, and it is a bivariate coupled relationship. In a certain temperature or concentration range, the measured data of the electrolyte ion transport characteristic parameter usually does not strictly obey the exponential growth law. Therefore, a polynomial fitting model containing multiple order terms and cross terms is used to characterize the local nonlinear variation of the parameter with temperature and concentration and the coupling relationship between them. The polynomial fitting has the advantages of high fitting flexibility, simple numerical implementation (easy to input and derive in simulation software), and clear application interval, so as to obtain higher fitting accuracy and stability in the target working interval; at the same time, the model form is simple, easy to program and realize and numerical optimization.

[0038] The polynomial equation realizes model fitting by polynomial characteristic expansion and least square fitting. Firstly, 15 characteristic values are generated for each group (C, T): 1, C, T, C 2, CT, T 2 , C 3 , C 2 T, CT 2 , T 3 , C 4 , C 3 T, C 2 T 2 , CT 3 , T 4 , 15 characteristic values of each group (C, T) finally generate a matrix, each row of the matrix is a group (C, T) extended polynomial characteristic value, then a linear equation set is constructed according to the characteristic matrix, and the equation set is shown as formula (7);

[0039] AX = B (7);

[0040] Wherein, A is the characteristic matrix, X is the polynomial coefficient to be solved, B is the experimental data of the change rule of lithium ion transference number with temperature and lithium salt concentration, in order to obtain the optimal solution of the linear equation set, so that it can be more accurately matched with the measured experimental data of the change rule of lithium ion transference number with temperature and lithium salt concentration, the least square method is used for fitting, the least square method is to find the best function that can match the data by minimizing the sum of squares of errors, and the expression of the sum of squares of errors is shown as formula (8):

[0041] (8);

[0042] Wherein, is the sum of squares of errors, A is the characteristic matrix, X is the polynomial coefficient to be solved , , , , , , , , , , , , , , , B is the experimental data of the change rule of lithium ion transference number with temperature and lithium salt concentration, and the superscript T represents the transpose of the matrix;

[0043] Through the sum of squares of errors, a quadratic function about the polynomial coefficient X to be solved is obtained, the derivative of the function is taken and the derivative is zero, the minimum point is obtained, so that the optimal polynomial coefficient is obtained, and finally the relative error and the absolute error are used to evaluate the fitting result.

[0044] Preferably, the target battery in step S2 comprises a positive electrode material, a negative electrode material, a separator, an electrolyte, a copper foil and an aluminum foil; the parameters of the positive and negative electrode materials comprise a lithium intercalation amount-potential change curve, a lithium intercalation amount-entropy heat coefficient change curve, a solid-phase lithium ion diffusion coefficient, an electrical conductivity, an exchange current density, a specific heat capacity, a thermal conductivity and a true density of the positive and negative electrode materials; the parameters of the separator comprise a porosity, a thermal conductivity, a specific heat capacity and a true density of the separator; the parameters of the copper and aluminum foils comprise an electrical conductivity, a specific heat capacity, a thermal conductivity and a true density of the copper and aluminum foils; the battery process parameters comprise a surface density, a compaction density, an N / P ratio, and a mass percentage of each of the positive and negative electrode materials, a binder and a conductive agent; and the battery structure-related parameters comprise a length, a height and a thickness of the positive and negative electrode sheets, the separator and the current collector.

[0045] Preferably, the electrochemical-thermal coupling simulation model based on the electrochemical parameters, the battery geometry and the battery structure-related parameters in step S3 comprises:

[0046] The simulation model is a two-dimensional or three-dimensional physical model of a battery cell or a battery module established by COMSOL Multiphysics software; and comprises a multi-physical field coupling solving module of mass conservation, charge conservation and an energy equation.

[0047] The extrapolation functions of the ion conductivity parameter with respect to temperature and lithium salt concentration and the lithium ion transference number parameter with respect to temperature and lithium salt concentration in step S4 comprise:

[0048] The extrapolation functions of the ion conductivity parameter with respect to temperature and lithium salt concentration and the lithium ion transference number parameter with respect to temperature and lithium salt concentration are input simultaneously, and an initial lithium ion concentration corresponding thereto needs to be set, i.e., a lithium salt concentration of a selected electrolyte.

[0049] Under the same technical concept, the application further provides a battery simulation model, which is constructed by using the battery simulation model construction method based on electrolyte intrinsic ion transport characteristic parameter optimization.

[0050] The above scheme of the application has the following advantages:

[0051] (1) In the application, the experimentally measured electrolyte ion conductivity, lithium ion transference number and liquid-phase diffusion coefficient are introduced, and the extrapolation functions of the electrolyte ion conductivity and the lithium ion transference number are generated by polynomial and Arrhenius function fitting, which significantly improves the fitting accuracy of the model to the actual charging curve.

[0052] (2) In the present application, the variation law of the intrinsic ion transport characteristic parameters of the electrolyte with temperature and concentration is considered, the migration and diffusion behavior of lithium ions in the electrolyte is accurately described, the error caused by the constant assumption in the traditional model is avoided, and the simulation result is closer to the actual operation state of the battery;

[0053] (3) In the present application, the parameter extrapolation function established can be adapted to different concentrations and different temperature conditions, has wide applicability, and supports high-precision modeling under various battery systems, electrolyte formulations and working environments;

[0054] (4) In the present application, a complete process from experimental measurement (such as conductivity meter, constant potential polarization test) to mathematical modeling (extrapolation function fitting) and then to multi-physical field modeling (COMSOL implementation) is proposed, and the standardization and systematization of the simulation model are realized. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The initial and steady-state interfacial impedance curves before and after polarization are shown in FIGS. Figure 1 (a) and FIG. Figure 1 (b) respectively.

[0056] Figure 2 FIG. 1 is a flowchart of the battery simulation model construction method based on electrolyte intrinsic ion transport characteristic parameter optimization;

[0057] Figure 3 FIG. 3 shows the results of electrolyte ion conductivity and fitting function in Example 1;

[0058] Figure 4 FIG. 4 shows the results of electrolyte lithium ion transference number and fitting function in Example 1;

[0059] Figure 5 FIG. 5 is a perspective view, a side view and an elevation view of a soft-pack lithium ion battery single piece grid division schematic diagram;

[0060] Figure 6 FIG. 6 shows the comparison of the actual charging curve of the battery, the simulation charging curve of the traditional electrochemical model and the simulation result of the electrolyte ion transport characteristic parameter optimization model introduced in Example 1 under 4C charging rate; wherein the experiment represents the actual charging curve of the battery, the traditional simulation represents the simulation charging curve of the traditional electrochemical model, and the electrolyte parameter simulation represents the simulation result of the electrolyte ion transport characteristic parameter optimization model introduced in Example 1;

[0061] Figure 7For 4C charge rate, the actual temperature change curve of the battery, the simulation temperature change curve of the traditional electrochemical model and the simulation result of the optimization model of the embodiment 1 introducing the ion transport characteristic parameters of the electrolyte are compared; wherein the experiment represents the actual charging curve of the battery, the traditional simulation represents the simulation charging curve of the traditional electrochemical model, and the introduction of the electrolyte parameter simulation represents the simulation result of the optimization model of the embodiment 1 introducing the ion transport characteristic parameters of the electrolyte. DETAILED DESCRIPTION

[0062] In order to make the technical problems, technical solutions and advantages of the present application clearer, specific embodiments will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0063] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0064] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be a locking connection, or a detachable connection, or an integral connection; it can be a mechanical connection, or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0065] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0066] In view of the problem of low simulation accuracy of the existing electrochemical-thermal coupling model in the charging process, the present application provides a battery simulation model construction method based on optimization of intrinsic ion transport characteristic parameters of electrolyte, and its flow chart is as shown in Figure 2 The present application provides a battery simulation model construction method based on optimization of intrinsic ion transport characteristic parameters of electrolyte, and its flow chart is as shown in

[0067] In some preferred embodiments of the present application, the electrolyte composition includes a lithium salt, a solvent, and an additive. The lithium salt is an active material that provides Li + The source of the active material determines the conductivity, electrochemical window, and corrosiveness of the electrolyte, and is usually selected from lithium hexafluorophosphate (LiPF6), lithium bisfluorosulfonylimide (LiFSI), and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). The solvent is the main body of the electrolyte and determines the solubility of the lithium salt, the ion migration behavior, and the electrochemical window. Common solvents are classified into the following categories: chain carbonates, cyclic carbonates, ether solvents, and other organic solvents. Although the amount of electrolyte additives is less than 5 wt.%, they can significantly improve the interface stability, cycle performance, and safety of the battery. Common additives include vinylene carbonate (VC), fluoroethylene carbonate (FEC), 1,3-propane sultone (PS), and the like.

[0068] The following will take a ternary lithium-ion battery simulation model as an example to describe the technical solutions of the present application in detail.

[0069] Example 1

[0070] The present embodiment provides a battery simulation model construction method based on electrolyte intrinsic ion transport characteristic parameter optimization, which specifically includes the following steps:

[0071] S1, obtain electrolyte intrinsic ion transport characteristic parameters, the electrolyte intrinsic ion transport characteristic parameters include: ion conductivity parameter, lithium ion transference number parameter and liquid phase ion diffusion coefficient; a polynomial equation and an Arrhenius equation are used for fitting to construct an extrapolation function of the ion conductivity parameter and temperature and lithium salt concentration, and a polynomial equation is used for fitting to construct an extrapolation function of the lithium ion transference number parameter and temperature and lithium salt concentration;

[0072] (1) Obtain the ion conductivity parameter, lithium ion transference number variation law, and liquid phase diffusion coefficient of the electrolyte

[0073] The ion conductivity meter is used to measure the variation law of the ion conductivity with temperature and lithium salt concentration:

[0074] Ion conductivity is measured by ion conductivity meter. The measurement method is as follows: the electrolyte to be measured is placed in a clean and dry test tube, and a pre-calibrated electrode probe is inserted into the test tube containing the electrolyte under constant temperature condition (25℃). The ion conductivity meter is used to apply a low amplitude alternating voltage to the sample, measure the current passing through the electrolyte, and calculate the ion conductivity of the electrolyte. To obtain the relationship between ion conductivity and lithium salt concentration and temperature, the control variable method is used to measure the ion conductivity at different temperatures under constant lithium salt concentration and the ion conductivity of different lithium salt concentrations under constant temperature, so as to obtain the variation of ion conductivity with lithium salt concentration and temperature, respectively. The measurement of lithium ion transference number needs to measure the lithium ion transference number at different temperatures under constant lithium salt concentration and the lithium ion transference number at different lithium salt concentrations under constant temperature.

[0075] The experimental data of the variation of lithium ion transference number with temperature and lithium salt concentration are measured by constant potential polarization method:

[0076] The lithium ion transference number of electrolyte can be measured by constant potential polarization method. In the constant potential polarization method, the alternating current impedance and direct current polarization voltage before and after direct current polarization need to be measured respectively. The test method is as follows: first, assemble the Li / electrolyte / Li symmetric battery, then measure the electrochemical impedance spectrum (general condition is 100kH-100mHz, voltage is 10mV), then perform direct current polarization test, condition is: voltage 10mV, time 3600s, after direct current polarization test, perform alternating current impedance test again, lithium ion transference number is calculated by formula (1):

[0077] (1) ;

[0078] Among them, is the lithium ion transference number, and are the initial and steady-state interfacial impedance, which are obtained by equivalent circuit fitting from electrochemical impedance spectrum, unit is Ω, the initial and steady-state interfacial impedance curves before and after polarization are shown in Figure 1 (a), and are the initial current and steady-state current in the direct current polarization test, unit is A, the initial current and steady-state current curves in the polarization test are shown in Figure 1 (b), the current value at 0s is the initial current value, with the test, the current tends to a straight line, reaches steady state, Figure 1 the current value at the end of test (b) is the steady-state current value, is the potential difference in the direct current polarization test, unit is V.

[0079] The liquid phase diffusion coefficient characterizes the rate of ion diffusion driven by the concentration gradient under the condition of no external current. According to the Walden rule, the expression thereof is derived as shown in formula (2):

[0080] (2);

[0081] wherein, is the liquid phase diffusion coefficient, the unit of which is m 2 / s, is the equivalent conductivity, the unit of which is S m 2 / mol, R is the gas constant, the unit of which is J / (mol K), is the temperature of the electrolyte, the unit of which is K, is the charge number of the ion, is the Faraday constant, the unit of which is C / mol;

[0082] The equivalent conductivity represents the total conductive capacity generated in the solution, and takes into account the charge number, migration speed and dissociation degree of the ion, and can be converted with the ion conductivity, and the conversion formula is shown in formula (3):

[0083] (3);

[0084] wherein, is the ion conductivity, the unit of which is S / m, is the concentration of the lithium salt of the electrolyte, the unit of which is mol / m 3 , and according to formula (3), the unit conversion can be performed to obtain the equivalent conductivity. Formula (3) and formula (2) are combined to obtain the expression of the liquid phase diffusion coefficient, as shown in formula (4):

[0085] (4).

[0086] (2) Construction of ion conductivity and lithium ion migration number extrapolation function

[0087] Based on the experimental data of the change rule of the ion conductivity with the temperature and the lithium salt concentration, the function relationship between the ion conductivity and the concentration is fitted by a polynomial fitting model; the Arrhenius equation is used to fit the relationship between the ion conductivity and the temperature;

[0088] The relationship between the temperature and the ion conductivity fitted by the Arrhenius equation is shown in formula (5):

[0089] (5);

[0090] wherein, is the ion conductivity, the unit of which is S / m, is the pre-exponential factor with unit of S / m, is the activation energy with unit of J / mol, reflecting the barrier that ions need to overcome for movement; is the gas constant with unit of J / (mol K), which is the Boltzmann constant, is the temperature of electrolyte with unit of K. The value of is 86.9, is 11339.78, is 8.314.

[0091] The construction of lithium ion transference number extrapolation function lies in that the lithium ion transference number is simultaneously affected by temperature and lithium salt concentration, which is a bivariate coupling relationship. The coupling effect of temperature and concentration is considered by using a polynomial fitting model, and an empirical function model is established in a polynomial form, as shown in equation (6): (6);

[0092] wherein, , , , , , , , , , , , , , , are polynomial coefficients, is the concentration of lithium salt in electrolyte with unit of mol / m 3 , is the temperature of electrolyte with unit of K.

[0093] Within a certain temperature or concentration range, the measured data of electrolyte ion transport characteristic parameters usually do not strictly obey the exponential growth rule. Therefore, a polynomial fitting model containing multiple order terms and cross terms is used to characterize the local nonlinear variation of parameters with temperature and concentration and the coupling relationship between them. Polynomial fitting has the advantages of high fitting flexibility, simple numerical implementation (easy to input and derive explicitly in simulation software), and can determine the optimal order through regularization and cross-validation to suppress overfitting and clearly define the applicable interval, thereby obtaining higher fitting accuracy and stability within the target working interval; at the same time, the model form is simple, easy to program and implement and numerical optimization. The polynomial fitting model is mainly realized by polynomial feature expansion and least squares fitting. First, 15 characteristic values are generated for each group (C, T): 1, C, T, C 2 , CT, T 2 , C2 , C 2 T, CT 2 , T 3 , C 4 , C 3 T, C 2 T 2 , CT 3 , T 4 , If C = 600, T = 298.15, a row [1, 600, 298.15, 600*298.15, 600 2 , 298.15, 298.15 2 ,..., 298.15 4 ] will be generated, and finally a matrix is generated, each row of which is the extended polynomial characteristic value of a sample, and then a linear equation set is constructed according to the characteristic matrix, and the equation set is shown in formula (7);

[0094] AX = B (7);

[0095] Wherein, A is the characteristic matrix, X is the polynomial coefficient to be solved, B is the experimental data of the change rule of lithium ion transference number with temperature and lithium salt concentration, in order to obtain the optimal solution of the linear equation set, so that it can be more accurately matched with the measured experimental data of the change rule of lithium ion transference number with temperature and lithium salt concentration, the least square method is used for fitting, the least square method is to find the best function that can match the data by minimizing the sum of squares of errors, and the expression of the sum of squares of errors is shown in formula (8):

[0096] (8);

[0097] Wherein, is the sum of squares of errors, A is the characteristic matrix, X is the polynomial coefficient to be solved , , , , , , , , , , , , , , , B is the experimental data of the change rule of lithium ion transference number with temperature and lithium salt concentration, and the superscript T represents the transpose of the matrix;

[0098] By the error sum of squares, a quadratic function about the polynomial coefficients X to be solved is obtained, the derivative of the function is taken and the derivative is zero, the minimum point is obtained, and the optimal polynomial coefficient is obtained. Finally, the relative error and the absolute error are used to evaluate the fitting results.

[0099] In this example, the coefficients are shown in Table 1, and the relative error and the absolute error are used to evaluate the fitting results. The ion conductivity experimental data, the fitting function results, and the relative error and the absolute error between them are shown in Table 2; the lithium ion transference number experimental data, the fitting function results, and the relative error and the absolute error between them are shown in Table 3.

[0100] Table 1 Coefficients of lithium ion transference number function expression ;

[0101] Table 2 Fitting results of ion conductivity function ;

[0102] Table 3 Fitting results of lithium ion transference number function ;

[0103] S2, obtaining the battery geometry, electrochemical parameters, and battery structural related parameters of the target battery; the electrochemical parameters include positive and negative electrode material parameters, separator parameters, copper and aluminum foil parameters, and battery process parameters;

[0104] (3) Obtain the component materials, battery process, and structural related parameters of the lithium ion soft package battery

[0105] The components of the lithium ion soft package battery include positive electrode material, negative electrode material, separator, electrolyte, copper foil, and aluminum foil. The positive electrode material in this model battery is lithium nickel cobalt manganese oxide, with a ratio of Ni:Co:Mn=7:1:2. The negative electrode material is artificial graphite, and the separator material is a porous polymer material. The obtained positive and negative electrode material parameters include the lithium intercalation amount-potential change curve, the lithium intercalation amount-entropy heat coefficient change curve, the solid phase lithium ion diffusion coefficient, the electrical conductivity, the exchange current density, the specific heat capacity, the thermal conductivity, and the true density. The obtained separator parameters include the separator porosity, the thermal conductivity, the specific heat capacity, and the true density. The obtained copper and aluminum foil parameters include the copper and aluminum foil electrical conductivity, the specific heat capacity, the thermal conductivity, and the true density. The obtained battery process parameters include the area density, the compacted density, the N / P ratio, the mass ratio of the positive and negative electrode materials, the binder, and the conductive agent. The obtained battery structural related parameters include the length, the height, and the thickness of the positive and negative electrode sheets, the positive and negative electrode tabs, the separator, and the current collector.

[0106] S3, constructing an electrochemical-thermal coupling simulation model based on the electrochemical parameters, battery geometry, and battery structural parameters, and outputting a traditional simulation model charging curve;

[0107] (4) Constructing an electrochemical-thermal coupling model

[0108] The construction of the electrochemical-thermal coupling model mainly includes the construction of the electrochemical model, the construction of the thermal model, and the coupling between the two. The model constructed this time adopts a three-dimensional physical model. The electrochemical model mainly includes mass conservation, charge conservation, and energy equation. Among them, the mass conservation mainly includes solid phase mass conservation and liquid phase mass conservation, and the solid phase mass conservation can be described by Fick's second law for lithium ion diffusion in the solid phase, as shown in formula (9):

[0109] (9);

[0110] wherein, is the solid phase lithium ion concentration, unit: mol / m 3 ; is the solid phase lithium ion diffusion coefficient, unit: m 2 / s, is the particle radius, unit: m;

[0111] Lithium ions in the electrolyte mainly transfer in the form of diffusion and migration, and the liquid phase lithium ion transport can be described by Fick's second law, as shown in formula (10).

[0112] (10);

[0113] wherein is the liquid volume fraction, is the liquid phase lithium ion concentration, unit: mol / m 3 , is the liquid effective diffusion coefficient, unit: m 2 / s, is the specific surface area of the active particle, unit: m -1 , is the ratio of the electrode solution interface area to the porous electrode solid volume, is the lithium ion transference number, is the local current density, unit: A / m 2 . The right side of the equal sign respectively represents the influence of diffusion and electromigration on the liquid phase lithium ion concentration.

[0114] The charge conservation in the lithium ion battery includes the electron conservation inside the active material and the ion conservation in the electrolyte. The lithium ion transport inside the electrode particle satisfies Ohm's law, as shown in formula (11).

[0115] - (11);

[0116] wherein, is the solid phase current density, unit: A / m 2 , is the solid phase conductivity corrected by Bruggeman, unit: S / m.

[0117] The diffusion and migration of lithium ions cause the transfer of electric charge, and the transport of electric charge in the electrolyte conforms to Ohm's law, so the liquid phase current density distribution and potential distribution satisfy Ohm's law. As shown in equation (12).

[0118] (12);

[0119] wherein, is the liquid phase current density, unit: A / m 2 ), is the liquid phase effective ion conductivity, unit: S / m, is the liquid phase potential, is the activity coefficient, is the liquid phase lithium ion concentration, unit: mol / m 3 , is the liquid phase volume fraction, is the thermodynamic temperature, unit: K, is the gas constant, unit: J / (mol K), is the Faraday constant, unit: C / mol.

[0120] The Butler-Volmer equation describes the electrode reaction process as shown in equation (13).

[0121] (13);

[0122] wherein, is the local current density, unit: A / m 2 , is the exchange current density, unit: A / m 2 , is the anode charge transfer coefficient, is the cathode charge transfer coefficient, is the overpotential, unit: V, is the thermodynamic temperature, unit: K, is the Faraday constant, unit: C / mol, is the gas constant, unit: J / (mol K).

[0123] The battery often accompanies heat production and dissipation while the electrochemical reaction occurs. The heat production and dissipation process of the battery is a non-steady-state heat conduction process. The Bernardi equation as shown in formula (14) describes the heat production of the battery.

[0124] (14) ;

[0125] wherein, is reversible heat, unit: W / m 3 , is irreversible heat, unit: W / m 3 , unit: W / m 3 ; is the volume of the battery, unit: m 3 ; is the voltage of the battery, unit: V; is the open circuit potential, unit: V; is the entropy heat coefficient, unit: V / K; is the thermodynamic temperature, unit: K;

[0126] The Newton cooling law as shown in formula (15) describes the heat dissipation of the battery.

[0127] (15) ;

[0128] wherein, is the heat dissipation of the battery, unit: W / m 3 , is the thermal conductivity, unit: W / (m 2 K), is the temperature difference, unit: K, is the outside temperature, unit: K, is the temperature of the battery, unit: K, is the heat exchange coefficient, unit: W / (m 2 K).

[0129] The heat conservation of the battery is the result of the mutual restriction of heat production and dissipation. The heat conservation equations of the battery are shown in formula (16), (17) and (18).

[0130] (16) ;

[0131] (17) ;

[0132] (18) ;

[0133] wherein, is the average density of the battery, unit: kg / m 3 , Cp is the average specific heat capacity, unit: J / (kg K), Tb is the battery temperature, , , Kt is the thermal conductivity of the battery in the x, y and z directions, respectively, unit: W / (m K), , , Kt is the thermal conductivity of the battery in the x, y and z directions, respectively, unit: W / (m K), Qg is the total heat generation of the battery, unit: W / m 3 , Qr is the reversible heat, unit: W / m 3 , Qi is the irreversible heat, unit: W / m 3 , Ql is the heat loss of heat exchange with the outside world, unit: W / m 3 , Qe is the electrochemical reaction heat, unit: W / m 3 Irreversible heat includes ohmic heat and polarization heat.

[0134] (5) Model grid division and setting of charging and discharging conditions and initial conditions

[0135] In the process of constructing a battery electrochemical-thermal coupling simulation model based on the optimization of intrinsic ion transport characteristic parameters of electrolyte, in order to balance the simulation accuracy and calculation efficiency, the model domain needs to be reasonably divided. According to the geometric structure of the battery model, the entire calculation domain is usually divided into multiple sub-regions, including the positive electrode region, the negative electrode region, the separator region, the electrolyte region and the current collector region, etc. Different regions have different physical properties and scale requirements, so a partition encryption strategy needs to be used for non-uniform grid division. Preferably, free tetrahedral elements are used for non-structured grid division, and if necessary, sweep grids are used in the thickness direction of the electrode to ensure the resolution of the interlayer potential / concentration gradient. The battery charging condition is set to constant current and constant voltage charging, the constant current and constant voltage charging boundary condition is that the current is less than 0.4C, and the initial condition needs to set the initial lithium ion concentration, i.e. the lithium salt concentration of the selected different electrolyte. The initial lithium ion concentration plays a key role in the charging simulation process, it can establish the correct initial state and determine the initial state of charge of the battery. If the initial concentration setting is too large, it will cause the output voltage curve of the simulation to deviate from the real battery test data, especially in the early stage of the curve, there will be deviation or deformation. At the same time, the lithium ion concentration of the electrolyte will also affect the local electrical conductivity and ion migration current, further affecting the ohmic potential drop and concentration polarization.

[0136] (6) Test the charging data of the battery studied and modify the related parameters of the model

[0137] The ambient temperature of the battery test is set to 25 DEG C, and the charging rate is 4C. The collected experimental data includes the voltage and surface temperature changes of the battery. Based on the experimental data, by appropriately adjusting and optimizing the lithium ion diffusion coefficient, the electrochemical reaction exchange current density and the initial lithium intercalation amount in the positive and negative electrode materials, the simulation discharge curve of the electrochemical-thermal coupling model is well matched with the experimental data, so as to complete the modification of the model parameters.

[0138] S4, introducing the extrapolation functions of ion conductivity parameters and temperature and lithium salt concentration, lithium ion transference number parameters and temperature and lithium salt concentration and liquid phase ion diffusion coefficient, optimizing the charging curve of the traditional simulation model, and obtaining the battery simulation model based on the optimization of the intrinsic ion transport characteristic parameters of the electrolyte.

[0139] (7) Introducing electrolyte fitting function to optimize the electrochemical-thermal coupling model for result verification

[0140] The ion conductivity fitting function, lithium ion transference number fitting function and liquid phase diffusion coefficient calculation formula obtained above are introduced into the preliminary modified lithium ion battery electrochemical-thermal coupling model. Then three types of comparison curves are drawn respectively: (a) the results of the traditional electrochemical model; (b) the results of the model optimized by introducing the fitting function of the electrolyte ion transport characteristic parameters; (c) the measured battery charging voltage curve and temperature change curve. By comparing the above curves, the role and effect of the fitting function of the electrolyte ion transport characteristic parameters in improving the precision and prediction ability of the electrochemical-thermal coupling model are evaluated.

[0141] Through the above optimization model method, the fitting function and the charging curve and temperature change before and after charging optimization are plotted.

[0142] Figure 3 The results of the measured electrolyte ion conductivity and fitting function of Example 1.

[0143] Figure 4 The results of the measured electrolyte lithium ion transference number and fitting function of Example 1.

[0144] Figure 5 The oblique view, side view and front view of the soft package lithium ion battery single piece grid division schematic diagram when the simulation discharge curve of the electrochemical-thermal coupling model is constructed in Example 1.

[0145] Figure 6 The actual battery charging curve, the traditional electrochemical model simulation charging curve and the simulation results of the optimization model of Example 1 by introducing the electrolyte ion transport characteristic parameters are compared under the charging rate of 4C.

[0146] Figure 7For 4C charging rate, the actual temperature change curve of the battery, the simulation curve of the traditional electrochemical model and the simulation result of the model of Example 1 introducing the ion transport characteristic parameter optimization of the electrolyte are compared.

[0147] It can be seen that the embodiment provides a battery simulation model construction method based on electrolyte intrinsic ion transport characteristic parameter optimization, and constructs a battery simulation model based on electrolyte intrinsic ion transport characteristic parameter optimization. Around the three key transmission parameters of ion conductivity, lithium ion transference number and liquid phase diffusion coefficient in the electrolyte, a complete technical path of experimental measurement, mathematical fitting, function extrapolation and model fitting is systematically established. By introducing the concentration and temperature dependence of the parameters into the COMSOL multi-physical field simulation software, and combining the battery structure, electrode material and boundary working condition information, the fitting accuracy and physical authenticity of the model to the actual charging curve are significantly improved.

Claims

1. A method for constructing a battery simulation model based on the optimization of intrinsic ion transport characteristic parameters of the electrolyte, characterized in that, Includes the following steps: S1. Obtain intrinsic ion transport characteristic parameters of the electrolyte, including: ionic conductivity parameter, lithium ion transport number parameter, and liquid phase ion diffusion coefficient; construct an extrapolation function of ionic conductivity parameter with temperature and lithium salt concentration by fitting polynomial equations and Arrhenius equations; construct an extrapolation function of lithium ion transport number parameter with temperature and lithium salt concentration by fitting polynomial equations. S2. Obtain the target battery's geometric structure, electrochemical parameters, and battery structural parameters; the electrochemical parameters include positive and negative electrode material parameters, separator parameters, copper and aluminum foil parameters, and battery process parameters; S3. Construct an electrochemical-thermal coupling simulation model based on the electrochemical parameters, battery geometry, and battery structural parameters, and output the charging curve of the traditional simulation model; S4. By introducing extrapolation functions of ionic conductivity parameter with temperature and lithium salt concentration, extrapolation functions of lithium ion transport number parameter with temperature and lithium salt concentration, and liquid phase ion diffusion coefficient, the charging curve of the traditional simulation model is optimized to obtain a battery simulation model based on the optimization of intrinsic ion transport characteristic parameters of electrolyte.

2. The battery simulation model construction method based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in claim 1, characterized in that, The acquisition of intrinsic ion transport characteristics parameters of the electrolyte in step S1 specifically includes: measuring experimental data on the variation of ion conductivity with temperature and lithium salt concentration using an ion conductivity meter, and measuring experimental data on the variation of lithium ion transport number with temperature and lithium salt concentration using a constant potential polarization method.

3. The battery simulation model construction method based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in claim 2, characterized in that, The method of measuring the variation of ionic conductivity with temperature and lithium salt concentration using an ionic conductivity meter specifically includes: placing the electrolyte to be tested in a clean, dry test tube, inserting a pre-calibrated electrode probe into the test tube containing the electrolyte, applying a low-amplitude AC voltage to the sample using an ionic conductivity meter, measuring the current passing through the electrolyte, and calculating the ionic conductivity of the electrolyte. The determination was performed using the controlled variable method, measuring the ionic conductivity at different temperatures under constant lithium salt concentration, and measuring the ionic conductivity at different lithium salt concentrations under constant temperature.

4. The battery simulation model construction method based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in claim 2, characterized in that, The experimental data for determining the variation of lithium-ion transference number with temperature and lithium salt concentration using the potentiostatic polarization method specifically include: determining the lithium-ion transference number at different temperatures under constant electrolyte concentration, and determining the lithium-ion transference number at different electrolyte concentrations under constant temperature. The constant potential polarization method specifically includes: first assembling a Li / electrolyte / Li symmetric cell, performing an electrochemical AC impedance test, then performing a DC polarization test, and finally performing another electrochemical AC impedance test after the DC polarization test. The lithium-ion transport number is calculated using formula (1). （1）； in, This represents the lithium-ion transference number. and The initial and steady-state interfacial impedances are obtained by fitting the electrochemical impedance spectroscopy through an equivalent circuit, and the units are Ω. and These are the initial and steady-state currents in the DC polarization test, expressed in amperes (A). This represents the potential difference in a DC polarization test, expressed in volts (V).

5. The battery simulation model construction method based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in claim 1, characterized in that, The liquid-phase diffusion coefficient mentioned in step S1 characterizes the rate of ion diffusion driven by the concentration gradient under the condition of no applied current. Its expression is derived based on Walden's law, as shown in formula (2): （2）； in, This is the liquid phase diffusion coefficient, in meters (m). 2 / s, Equivalent conductivity, in S m 2 / mol, where R is the gas constant, with units of J / (mol K). The temperature of the electrolyte, in K. The charge number of the ion. This is Faraday's constant, expressed in C / mol. Equivalent conductivity represents the total conductivity generated in a solution, taking into account factors such as the charge number, migration rate, and degree of dissociation of ions. It can be converted to ionic conductivity, and the conversion formula is shown in formula (3): （3）； in, This refers to ionic conductivity, measured in S / m. This refers to the lithium salt concentration in the electrolyte, expressed in mol / m³. 3 Based on the unit conversion of formula (3), the equivalent conductivity can be obtained. Combining formula (3) with formula (2), the expression for the liquid phase diffusion coefficient can be obtained, as shown in formula (4): （4）。 6. The method for constructing a battery simulation model based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in claim 2 or 3, characterized in that, Step S1 involves fitting polynomial equations and the Arrhenius equation to construct an extrapolation function of ionic conductivity parameters with respect to temperature and lithium salt concentration. This specifically includes the following steps: Based on experimental data on the variation of ionic conductivity with temperature and lithium salt concentration, a polynomial fitting model was used to fit the functional relationship between ionic conductivity and concentration; the Arrhenius equation was used to fit the relationship between ionic conductivity and temperature. The relationship between the fitted temperature and ionic conductivity of the Arrhenius equation is shown in Equation (5): （5）； in, This refers to ionic conductivity, measured in S / m. The exponential factor is expressed in units of S / m. The activation energy is expressed in J / mol and reflects the potential barrier that ions need to overcome to move. This is the gas constant, expressed in J / (mol K), also known as the Boltzmann constant. The temperature of the electrolyte is expressed in Kelvin (K).

7. The battery simulation model construction method based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in claim 2 or 4, characterized in that, The step S1, which involves fitting a polynomial equation to construct an extrapolation function of lithium-ion transport number parameter with respect to temperature and lithium salt concentration, specifically includes: constructing a bivariate coupled empirical function model of lithium-ion transport number parameter with respect to temperature and lithium salt concentration using a polynomial equation, the expression of which is shown in formula (6): （6）； in, , , , , , , , , , , , , , , For polynomial coefficients, This refers to the lithium salt concentration in the electrolyte, expressed in mol / m³. 3 , The temperature of the electrolyte, expressed in K. The polynomial equations are fitted using polynomial feature extension and least squares fitting. First, 15 feature values ​​are generated for each pair (C, T): 1, C, T, C 2 CT, T 2 C 3 C 2 T, CT 2 T 3 C 4 C 3 T, C 2 T 2 CT scan 3 T 4 The 15 eigenvalues ​​of each group (C, T) eventually generate a matrix. Each row of the matrix is ​​a set of polynomial eigenvalues ​​after expansion of (C, T). Then, a system of linear equations is constructed based on the eigenma, as shown in formula (7). AX = B (7); Where A is the characteristic matrix, X is the polynomial coefficients to be solved, and B is the experimental data on the variation of lithium ion transport number with temperature and lithium salt concentration. In order to find the optimal solution of this linear equation system and make it more accurately match the experimental data on the variation of lithium ion transport number with temperature and lithium salt concentration, the least squares method is used for fitting. The least squares method finds the best function that matches the data by minimizing the sum of squares of the errors. The expression of the sum of squares of the errors is shown in formula (8): （8）； in, Let A be the sum of squared errors, A be the characteristic matrix, and X be the coefficients of the polynomial to be solved. , , , , , , , , , , , , , , B represents experimental data on the variation of lithium-ion transference number with temperature and lithium salt concentration, and the superscript T indicates the transpose of the matrix. By using the sum of squared errors, a quadratic function of the polynomial coefficients X to be solved is obtained. The derivative of this function is taken and set to zero to find the minimum point, thus obtaining the optimal polynomial coefficients. Finally, relative error and absolute error are used to evaluate the fitting results.

8. The method for constructing a battery simulation model based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in claim 1, characterized in that, The target battery in step S2 includes a positive electrode material, a negative electrode material, a separator, an electrolyte, copper foil, and aluminum foil. The parameters of the positive and negative electrode materials include the lithium intercalation-potential variation curve, the lithium intercalation-entropy thermal coefficient variation curve, the solid-phase lithium-ion diffusion coefficient, electrical conductivity, exchange current density, specific heat capacity, thermal conductivity, and true density. The parameters of the separator include the porosity, thermal conductivity, specific heat capacity, and true density. The parameters of the copper and aluminum foil include the electrical conductivity, specific heat capacity, thermal conductivity, and true density. The battery process parameters include areal density, compaction density, N / P ratio, and the mass percentage of the positive and negative electrode materials, binder, and conductive agent. The battery structural parameters include the length, height, and thickness of the positive and negative electrode sheets, separator, and current collector.

9. The method for constructing a battery simulation model based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in claim 1, characterized in that, Step S3, which involves constructing an electrochemical-thermal coupled simulation model based on the electrochemical parameters, battery geometry, and battery structural parameters, specifically includes: The simulation model is a two-dimensional or three-dimensional physical model of a battery cell or battery module built using COMSOL Multiphysics software; it includes a multi-physics field coupling solution module for mass conservation, charge conservation, and energy equations. The extrapolation functions of ionic conductivity parameter with temperature and lithium salt concentration, lithium ion transport number parameter with temperature and lithium salt concentration, and liquid-phase ion diffusion coefficient introduced in step S4 specifically include: When inputting the extrapolation functions of ionic conductivity parameter with temperature and lithium salt concentration, and lithium ion transport number parameter with temperature and lithium salt concentration, it is necessary to set the corresponding initial lithium ion concentration, which is the lithium salt concentration of the selected electrolyte.

10. A battery simulation model, characterized in that, The battery simulation model is constructed using the battery simulation model construction method based on the optimization of intrinsic ion transport characteristic parameters of electrolyte as described in any one of claims 1-9.

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