Battery thermal field simulation calculation method, device and system and storage medium

Abstract

The invention provides a battery thermal field simulation calculation method and system and a storage medium, and the method comprises the steps: S1, inputting the product parameters and working condition parameters of a battery, constructing a three-dimensional simulation battery, and generating an electrochemical-thermal coupling model; s2, a heat generation source item in the electrochemical-thermal coupling model serves as a heat source, a thermal field model T (lambda0, p, t) is obtained through coupling, and lambda0 represents an initial set value of a thermal diffusion coefficient; s3, carrying out an incubator experiment on the solid battery to obtain a T-t characteristic curve; and S4, fitting a thermal diffusion coefficient of the thermal field model by using the T-t characteristic curve, optimizing to obtain T (lambda, p, t), correspondingly generating a T-t simulation curve in the model function according to the T-t characteristic curve, comparing the two groups of curves, and verifying the simulation precision. A thermal field model is established through a heat production source item, and thermal diffusion coefficient lambda is fitted through incubator experimental data, so that simulation precision is improved, dynamic prediction of the thermal field of the battery is realized, and the method can be used for predicting the temperature of any position of the battery under any time node in the battery operation process under different working conditions.

Description

Technical Field

[0001] The present application relates to the technical field of battery simulation, and particularly to a method, device, system and storage medium for calculating and simulating the thermal field of a battery. Background Art

[0002] The heat generated by a battery during operation is mainly divided into two categories: reversible heat and irreversible heat. Among them, reversible heat, also known as entropy heat, is the heat caused by the entropy change of the electrochemical reaction during the charging and discharging process of the battery. Taking a lithium-ion battery as an example, the insertion / extraction of lithium ions in the positive and negative electrode materials will cause changes in the lattice structure of the materials, thereby triggering an entropy change ΔS. According to the thermodynamic formula: Q 可逆 = TΔS·I / nF, where T is the temperature, I is the current, n is the number of electrons, and F is the Faraday constant. During the discharging process, ΔS is negative, and the battery will release heat. Irreversible heat is the heat generated by irreversible energy loss inside the battery, usually including three categories: ohmic polarization, activation polarization, and concentration polarization. The heat generation mechanism of ohmic polarization is: when current passes through the internal resistance of the battery (the resistance of the electrode, electrolyte, separator, etc.), heat is generated according to Joule's law (Q = I 2 R). The heat generation mechanism of activation polarization is: the heat generated by the additional voltage (overpotential) required to overcome the activation energy barrier of the electrochemical reaction. The heat generation mechanism of concentration polarization is: the heat generated by the additional voltage used to drive ion migration to overcome the concentration gradient between the electrode surface and the bulk phase.

[0003] In the existing simulation methods for the thermal field of a battery, three-dimensional simulation battery modeling is usually performed. After connecting the simulated battery to the electrochemical physical field, an electrochemical-thermal coupling model is generated based on the product parameters and operating conditions of the battery. According to the electrochemical-thermal coupling model, the heat generation of the battery is traced, and a thermal field model that can trace the thermal field state at any position of the battery body at any moment during its charging / discharging process is obtained by coupling. In the thermal field model, the thermal diffusion coefficient used to describe the heat exchange efficiency of the battery has a great influence on the accuracy of tracing and predicting the thermal field. In the prior art, relevant technicians usually adjust and convert the thermal diffusion coefficient of the thermal field model based on the material properties of the battery and empirical values.

[0004] The inventors of the present application found during the implementation process that when using the above thermal field model, the measured battery temperature characteristic curve has a large deviation from the actual result, and the prediction effect of the thermal field of batteries under different operating conditions is also not ideal. Summary of the Invention

[0005] To solve the above problems and improve the accuracy of thermal field simulation, the first aspect of the present application provides a method for calculating and simulating the thermal field of a battery, including the following steps:

[0006] S1: Input the product parameters and operating parameters of the battery, build a three-dimensional simulation battery and generate an electrochemical-thermal coupling model;

[0007] S2: All heat-generating items in the electrochemical-thermal coupling model are combined as heat-generating source items, and the heat-generating source items are used as heat sources. The electrochemical-thermal coupling model is coupled to establish a thermal field model T(λ0,[,t)] with temperature as the characterization item, where p represents any position inside and on the surface of the battery, t represents time, T(λ0,p,t) represents the temperature rise at any position at any time, and λ0 represents the initial setting value of the thermal diffusion coefficient;

[0008] S3: Conducting a temperature chamber test on a physical battery with reference to the product parameters and the operating condition parameters to obtain a Tt characteristic curve showing changes in battery temperature over time;

[0009] S4: Taking the Tt characteristic curve as the optimization target, perform parameter fitting on the thermal diffusion coefficient in the thermal field model T(λ0, p, T), substitute the optimized thermal diffusion coefficient back into the thermal field model to obtain the optimized thermal field model T(λ, p, t), calculate the Tt simulation curve of the model based on the optimized thermal field model T(λ, p, t), and verify the accuracy of the simulation.

[0010] In some optional embodiments, the establishment of the thermal field model further includes the following steps:

[0011] S21: The thermal diffusion coefficient λ is divided into a thermal conductivity coefficient k and a heat transfer coefficient h for separate calculations, wherein the thermal conductivity coefficient is used to describe the internal thermal conductivity efficiency of the battery, and the heat transfer coefficient is used to describe the boundary convection heat transfer efficiency between the battery and the external environment;

[0012] S22: Define the boundary convection heat transfer calculation formula as: q0 = -h(TT amb ), where h is the heat transfer coefficient, W / (m 2 ·K), T represents the battery surface temperature, T amb represents the ambient temperature, and the heat transfer coefficient has an initial setting value h0;

[0013] S23: The thermal conductivity k is further divided into radial thermal conductivity k r and axial thermal conductivity k z , the p should also be decomposed into: p(r, z), the corresponding thermal field model is: T[(k r ,r);(k z ,z);h;t)];

[0014] Wherein, T represents absolute temperature, K; r represents radial coordinate, m; z represents axial coordinate, m; the radial thermal conductivity k rWith the axial thermal conductivity k z There is an initial set value With

[0015] In some alternative embodiments, the outer surface of the battery is divided into a side surface and an end surface for welding with an external metal connector. The heat transfer coefficient h is only used to describe the boundary convective heat transfer of the side surface, and the boundary convection condition of the end surface is equivalent to the solid heat conduction condition between the end surface and the external metal connector. The equivalent heat transfer coefficient k of the end surface is obtained by conversion according to the material properties of the external metal connector m 。

[0016] In some alternative embodiments, the calculation of the heat source term includes the following steps:

[0017] S24: Use the lumped parameter method to describe the dynamic electrochemical process. According to the SOC dynamic equation:

[0018]

[0019] Integrate to define the average SOC as: Define the voltage equation: Among them, τ represents the diffusion time constant, s; η IR Represents the ohmic overpotential, V; η act Represents the activation overpotential, V; η conc Represents the concentration overpotential, V; E OCV Represents the open circuit voltage of the battery, V;

[0020] S25: Establish an equation according to Fourier's law of heat conduction:

[0021]

[0022] Among them, ρ represents the density, kg / m 3 ; C p Represents the constant pressure heat capacity, J / (kg·K); u represents the velocity vector, m / s; k represents the thermal conductivity, W / (m·K); Q h Represents the heat source term, W / m 3 ;

[0023] S26: Couple the electrochemical equation in S24 to obtain the relationship of the heat source term:

[0024] Among them, Represents the entropy heat coefficient, V / K; Q conc Represents the heat generation power contributed by the concentration overpotential, W.

[0025] In some alternative embodiments, the ηIR Obtained from the equation:

[0026] ;

[0027] Said η act Obtained from the equation: ;

[0028] Wherein, R represents the universal gas constant, J / (mol·K); F represents the Faraday constant, C / mol; I cell represents the actual working current of the battery, A; I 1C,cell represents the 1C discharge current of the battery; J0 represents the dimensionless charge exchange current.

[0029] In some alternative embodiments, said η conc satisfies: Combined with the SOC dynamic equation, said Q conc satisfies:

[0030]

[0031] Wherein, E OCV represents the open circuit voltage, V; Q cell represents the battery capacity, A·h.

[0032] In some alternative embodiments, the heat generation source term Q h should also include the heat generation power Q contributed by the current collectors of the positive and negative electrodes of the battery and their welding resistances ohm , said Q ohm Obtained from the equation: Wherein, I cell represents the actual working current of the battery, R ohm represents the current collectors of the positive and negative electrodes of the battery and their welding resistances.

[0033] In some alternative embodiments, the incubator experiment further includes the following steps:

[0034] S31: Place the battery meeting the product parameters in an incubator at 25±0.5°C, and perform constant current discharge on the battery with the operating condition parameters, so that its SOC discharges from 1 to 0;

[0035] S32: Monitor and record the change in the temperature at any position p0 on the battery during the discharge stage, and at the end of the discharge, p0 reaches the highest temperature, and record the T-t temperature rise curve in the discharge stage of the T-t characteristic curve;

[0036] S33: After the battery discharge is completed, disconnect the battery circuit, monitor and record the change in the temperature of p0 during the heat exchange stage until its temperature drops to the same as that of the incubator, and record the T-t heat exchange curve in the heat exchange stage in the T-t characteristic curve.

[0037] In some alternative embodiments, the parameter fitting of the thermal diffusion coefficient in the thermal field model further includes the following steps:

[0038] S41: Take the discrete point values (T exp , t) measured by the incubator experiment in the T-t heat exchange curve as the optimization objective. According to the product parameters, operating conditions parameters, and the position information p0(r0, z0) of p0, substitute the t values in the discrete point values into the thermal field model one by one to solve for the T sim values corresponding to the t values one by one; and

[0039] S42: Set the objective function: min∑(T sim -T exp ), and use a numerical optimization algorithm for inversion to obtain the optimized h, k 2 and k r . z

[0040] In some alternative embodiments, the numerical optimization algorithm includes any one of the least squares method, bisection method, gradient descent method, Newton method, and conjugate gradient method.

[0041] In some alternative embodiments, the verification of the simulation accuracy further includes the following steps:

[0042] S43: Substitute the optimized h, k r and k z back into the thermal field model to obtain the simulated discrete point values (T' exp , t) corresponding one by one to the discrete point values (T sim , t) measured by the incubator experiment, and form the T-t simulation curve; and

[0043] S44: Compare the experimental result T exp value with the simulation result T' sim value, calculate the simulation error ΔT = |T' sim -T exp |, and statistically calculate the root mean square error of the T exp value and the T' sim value: RMSE(T exp , T' sim ) to verify the simulation accuracy.

[0044] In some alternative embodiments, the product parameters include the material physical property parameters and geometric characteristic parameters of the battery; the operating condition parameters include the electrochemical parameters and thermodynamic parameters of the battery.

[0045] The second aspect of the present application provides a battery thermal field simulation and calculation device, which is used to execute the battery thermal field simulation and calculation method according to any one of the above, and the device includes:

[0046] A thermal simulation module, including a data access port, a modeling unit, an electrochemistry-physics field interface, and a thermal analysis unit. The data access port is used to obtain the product parameters and the operating condition parameters. The modeling unit is used to construct a three-dimensional simulation battery based on the product parameters and the operating condition parameters. The electrochemistry-physics field interface is connected to the modeling unit and is used to construct the electrochemistry-thermal coupling model according to the three-dimensional simulation battery. The thermal analysis unit is connected to the electrochemistry-physics field interface and couples the electrochemistry-thermal coupling model to establish a thermal field model T(λ0,p,t);

[0047] An experiment module, connected to the thermal simulation module, including an experiment incubator, a battery test circuit, a temperature sensor for detecting the battery temperature, and a timer connected to the temperature sensor. The experiment incubator determines the experiment conditions according to the product parameters and the operating condition parameters obtained from the thermal simulation module. The temperature sensor and the timer synchronously transmit the temperature and time data of the battery during the test in the test circuit;

[0048] A fitting module, connected to the experiment module and the thermal simulation module, obtains the data transmitted back by the experiment module to generate the T-t characteristic curve, substitutes the transmitted-back data into the thermal field model T(λ0,p,t), and performs parameter fitting on the thermal diffusion coefficient to obtain the optimized thermal field model T(λ,p,t); and

[0049] A verification module, connected to the fitting module and the thermal simulation module, substitutes the T-t characteristic curve back into the optimized thermal field model T(λ,p,t) to obtain a T-t simulation curve of the battery temperature changing with time, and verifies the simulation accuracy.

[0050] The third aspect of the present application provides a battery thermal field simulation and calculation system, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the battery thermal field simulation and calculation method according to any one of the above.

[0051] The fourth aspect of the present application provides a storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute the battery thermal field simulation calculation method according to any one of the above.

[0052] The present application has at least the following technical effects:

[0053] 1) The first aspect of the present application provides a battery thermal field simulation calculation method. By splitting the heat source term in the electrochemical-thermal coupling model as the heat source to establish a thermal field model, and further combining the T-t characteristic curve of the surface temperature changing with time during the battery experiment measured by the incubator experiment, substituting the real experimental data into the thermal field model to optimize the thermal diffusion coefficient λ, the simulation accuracy is improved. Further, the temperature characterization term is coupled to establish a thermal field model to obtain the model function T(λ, p, t). When the heat source term is determined, the model function T(λ, p, t) can be used to predict the temperature at any position inside or on the surface of the battery at any time node during the battery operation under different working conditions. In the production and R & D stage, this method can be used to guide the charge and discharge strategy of the battery to avoid long-term high or low temperature operation, or screen the temperature control decision to evaluate the effects of different heating / cooling means, and can also be used to quantify the internal temperature gradient of the battery to guide the adjustment of parameters such as electrode thickness and porosity to reduce the risk of local overheating; in the application stage, this method can be integrated into the battery management system to dynamically adjust the current limit value through real-time temperature feedback, or be used to identify the abnormal temperature rise trend in real time and trigger the protection mechanism in advance; further, this method can also be integrated into machine learning, or coupled and extended with more physical fields to realize the thermal field prediction for more complex working conditions.

[0054] 2) The second aspect of the present application provides a battery thermal field simulation calculation device, which includes a thermal simulation module, an experimental module connected to the thermal simulation module, a fitting module connected to the experimental module and the thermal simulation module, and a verification module connected to the fitting module and the thermal simulation module. The thermal simulation module constructs a thermal field model according to the product parameters, working condition parameters, and the electrochemical-thermal coupling model. The experimental module is used to conduct experiments on the physical battery according to the product parameters and working condition parameters, monitor the temperature and time data. The fitting module generates the T-t characteristic curve as the optimization target, optimizes the parameters and substitutes them back into the thermal field model. The verification module simulates and calculates the T-t simulation curve of the battery temperature changing with time based on the optimized thermal field model, and compares the T-t characteristic curve to verify the simulation accuracy.

[0055] 3) A third aspect of the present application provides a battery thermal field simulation and calculation system, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. The battery thermal field simulation and calculation method is programmed, and when the processor executes the computer program, the battery thermal field simulation and calculation method described in any one of the above is realized, and the simulation and calculation efficiency is improved by using the computing power of the computer.

[0056] 4) A fourth aspect of the present application provides a storage medium storing a computer program, and when the computer program runs, it controls the device where the storage medium is located to execute the battery thermal field simulation and calculation method described in any one of the above, so as to realize the simulation and calculation of the battery thermal field. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0058] Figure 1 It is a step flow block diagram of an optional embodiment of a battery thermal field simulation and calculation method proposed by the present application;

[0059] Figure 2 It is a logical block diagram of parameter fitting of an optional embodiment of a battery thermal field simulation and calculation method proposed by the present application;

[0060] Figure 3 It is a structural principle block diagram of an optional embodiment of a battery thermal field simulation and calculation device proposed by the present application;

[0061] Figure 4 It is a comparison and verification diagram of the simulation results and experimental results of Embodiment 1 and Embodiment 2 of the present application;

[0062] Figure 5 It is a comparison diagram of the T-t simulated heat transfer curve and the T-t characteristic curve in Embodiment 2 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The embodiments of the present embodiment will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present embodiment and should not be construed as a limitation of the present embodiment.

[0064] In the description of this embodiment, it should be understood that for the orientation description, such as the orientation or positional relationship indicated by up, down, front, back, left, right, etc., it is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing this embodiment and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to this embodiment.

[0065] In the description of this embodiment, the meaning of several is one or more, the meaning of multiple is more than two, greater than, less than, exceeding, etc. are understood as not including the present number, and above, below, within, etc. are understood as including the present number. If there is a description of first and second, it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0066] In the description of this embodiment, unless otherwise clearly defined, words such as setting, installing, connecting, etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meaning of the above words in this embodiment in combination with the specific content of the technical solution.

[0067] See Figure 1 - Figure 2 , the first aspect of this application provides a method for simulating and calculating the thermal field of a battery, including the following steps:

[0068] S1: Input the product parameters and working condition parameters of the battery, construct a three-dimensional simulated battery and generate an electrochemical-thermal coupling model;

[0069] S2: Aggregate all heat generation terms in the electrochemical-thermal coupling model as the heat source term, use the heat source term as the heat source, and couple the electrochemical-thermal coupling model to establish a thermal field model T(λ0, p, t) with temperature as the characterization term, where p represents any position inside and on the surface of the battery, t represents time, T(λ0, p, t) represents the temperature rise at any position at any time, and λ0 represents the initial set value of the thermal diffusion coefficient;

[0070] S3: Refer to the product parameters and the working condition parameters, conduct a temperature chamber experiment on the physical battery, and obtain the T-t characteristic curve of the battery temperature changing with time;

[0071] S4: Use the T-t characteristic curve as the optimization target, perform parameter fitting on the thermal diffusion coefficient in the thermal field model T(λ0, p, t), substitute the optimized thermal diffusion coefficient back into the thermal field model, and thus obtain the optimized thermal field model T(λ, p, t). Calculate the T-t simulation curve of the model based on the optimized thermal field model T(λ, p, t) to verify the accuracy of the simulation.

[0072] Further, see Figure 2 , the establishment of the thermal field model also includes the following steps:

[0073] S21: The thermal diffusion coefficient λ is divided into a thermal conductivity coefficient k and a heat transfer coefficient h for separate calculations, wherein the thermal conductivity coefficient is used to describe the internal thermal conductivity efficiency of the battery, and the heat transfer coefficient is used to describe the boundary convection heat transfer efficiency between the battery and the external environment;

[0074] S22: Define the boundary convection heat transfer calculation formula as: q0 = -h(TT amb ), where h is the heat transfer coefficient, W / (m 2 ·K), T represents the battery surface temperature, T amb represents the ambient temperature, and the heat transfer coefficient has an initial setting value h0;

[0075] S23: The thermal conductivity k is further divided into radial thermal conductivity k r and axial thermal conductivity k z , the p should also be decomposed into: p(r, z), the corresponding thermal field model is: T[(k r ,r);(k z ,z);h;t)];

[0076] Wherein, T represents absolute temperature, K; r represents radial coordinate, m; z represents axial coordinate, m; the radial thermal conductivity k r and axial thermal conductivity k z There is an initial setting value and

[0077] Furthermore, the thermal field model T[(k r ,r);(k z ,z);h;t)] can also be decomposed into radial thermal field sub-model and axial thermal field sub-model:

[0078]

[0079] Further combining the boundary condition q0 with the heat source term Q h , combining the above equations and solving the partial differential equations by numerical methods, we can realize the temperature field T[(k r ,r);(k z ,z);h;t)], and calculate the temperature at any location inside or on the battery surface at any moment, providing a theoretical basis for the design of battery thermal management systems. Specifically, the numerical solution process can be rapidly solved using tools such as COMSOL, combined with the built-in Lumped Battery Interface and Heat Transfer Module.

[0080] It is understandable that the anisotropy of thermal conductivity inside the battery is reflected in the axial and radial directions. Using the coordinate system (rz plane) to simplify the three-dimensional problem into a two-dimensional model can reduce the amount of calculation by taking advantage of the circumferential symmetry. Specifically, taking a cylindrical battery as an example, the electrochemical environment and boundary conditions at various locations that are rotationally symmetric about the central axis of the battery cylinder are relatively consistent, so equivalent calculations can be performed. The axial thermal conductivity k after further correction and optimization based on experimental data is z , radial thermal conductivity k r and the heat transfer coefficient h, to improve the accuracy of the prediction of the battery thermal field distribution.

[0081] The initial setting value of the radial thermal conductivity and It can be calculated based on the weighted average properties of the composite materials of the internal winding structure of the battery (such as electrode layer, diaphragm, etc.). Taking cylindrical batteries as an example, The value range is 0.5-5. The value range of the heat transfer coefficient is 5-15. The initial setting value h0 of the heat transfer coefficient can be obtained through the heat exchange method of simulation measurement. For example, the value range of h0 under natural convection conditions is 5-10, and the value range of h0 under air cooling conditions reaches 20-100. The value range of h0 under the heat exchange conditions set in this application is: 15-50.

[0082] In some optional implementation methods, the outer surface of the battery is divided into a side surface and an end surface for welding to an external metal connector. The heat transfer coefficient h is only used to describe the boundary convection heat transfer of the side surface. The boundary convection condition of the end surface is equivalent to the solid thermal conductivity condition of the end surface and the external metal connector. The equivalent heat transfer coefficient k of the end surface is obtained by conversion based on the material properties of the external metal connector. m , that is, there is a boundary condition q0': q0'=-k m (tT amb ).

[0083] In existing simulation models, the surface contact conditions of the battery during application are usually not taken into account. It is assumed that the heat exchange conditions on the outer surface of the battery are equal everywhere. Therefore, the empirical value of the heat transfer coefficient is obtained by consulting the technical manual according to the environmental conditions, and the model is calculated. However, in the actual implementation process, the two ends of the battery used as external electrodes are usually welded to the external metal connectors. During heat exchange, the heat exchange mode of the battery end face is completely different from the convective heat transfer on the side. The heat exchange model of the battery end face will be closer to the solid heat conduction mode. Therefore, the heat transfer efficiency is higher than the convective heat transfer on the side, which ultimately causes the actual equivalent heat transfer coefficient of the end face to be quite different from the heat transfer coefficient of the side. If the equivalent heat transfer coefficient k is not used mWhen calculating the heat dissipation at the end face of the battery, the error in the temperature measurement of the end face of the battery by the thermal field model will be very large, reducing the accuracy of the thermal field simulation of the battery end face; in this application, the equivalent heat transfer coefficient k of the end face is obtained by converting according to the material properties of the external metal connection part. m , the model is corrected to: T[(k r ,h,r);(k z ,k m ,z_;t)], improving the accuracy of the thermal field simulation of this method.

[0084] In some optional implementation methods, the product parameters include the material physical property parameters and geometric characteristic parameters of the battery; the operating conditions parameters include the electrochemical parameters and thermodynamic parameters of the battery. Further, the material physical property parameters include: the categories or compositions of the materials of structures or substances such as the battery case, the positive and negative electrode foils, the separator, the positive and negative electrode current collectors, the electrolyte, the positive electrode energy storage material, and the negative electrode energy storage material. Further, parameters related to the above material categories can be obtained by referring to technical manuals or relevant databases, such as: relevant parameters such as thermal conductivity, constant pressure heat capacity, density, etc. The geometric characteristic parameters include: the three-dimensional dimensions of the above-mentioned various structures, such as the battery radius, the battery height, the thickness and mass of the positive electrode, the negative electrode sheet, the separator, the positive electrode cover, the positive electrode current collector, and the negative electrode current collector. The electrochemical parameters at least include: the welding resistance value of the positive or negative electrode current collector, the battery capacity, the battery charging or discharging rate, the battery operating current, the open circuit voltage, the SOC value, etc. The thermodynamic parameters at least include: the ambient temperature, the initial temperature, the entropy heat coefficient, the thermal conductivity, the convective heat transfer coefficient, etc. In some optional implementation methods, the calculation of the heat source term includes the following steps:

[0085] S24: Use the lumped parameter method to describe the dynamic electrochemical process. According to the SOC dynamic equation:

[0086]

[0087] Integrate to define the average SOC as: Define the voltage equation: Among them, τ represents the diffusion time constant, s; η IR represents the ohmic overpotential, V; η act represents the activation overpotential, V; b conc represents the concentration overpotential, V; E OCV represents the battery open circuit voltage, V;

[0088] S25: Establish an equation according to Fourier's law of heat conduction:

[0089]

[0090] Among them, ρ represents the density, kg / m3 ; C p represents the isobaric heat capacity, J / (kg·K); u represents the velocity vector, m / s; k represents the thermal conductivity, W / (m·K); Q h represents the heat source term, W / m 3 ; and

[0091] S26: Couple the electrochemical equation in S24 to obtain the relational expression of the heat source term:

[0092] wherein, represents the entropy heat coefficient, V / K; Q conc represents the heat generation power contributed by the concentration overpotential, W.

[0093] In some alternative embodiments, the η IR is obtained from the equation:

[0094] obtained;

[0095] the η act is obtained from the equation: obtained;

[0096] wherein, R represents the universal gas constant, J / (mol·K); F represents the Faraday constant, C / mol; I cell represents the actual working current of the battery, A; I 1C,cell represents the 1C discharge current of the battery; J0 represents the dimensionless charge transfer current.

[0097] In some alternative embodiments, the η conc satisfies: Combined with the SOC dynamic equation, the Q conc satisfies:

[0098]

[0099] wherein, E OCV represents the open circuit voltage, V; Q cell represents the battery capacity, A·h.

[0100] In some alternative embodiments, the heat source term Q h should also include the heat generation power Q ohm contributed by the positive and negative current collectors of the battery and their welding resistances, and the Q ohm is obtained from the equation: obtained, wherein, I cell represents the actual working current of the battery, and R ohm represents the positive and negative current collectors of the battery and their welding resistances.

[0101] In existing simulation models, heat generation such as that of the current collector plate itself and the resistance caused by welding is usually not considered. On the one hand, the current collector plate is a good conductor with a small resistance of its own, which can be ignored. On the other hand, the welding resistance varies with the welding method, wire design, solder joint size and density, making it difficult to measure the resistance value. However, in the specific implementation process, the welding resistance between the positive and negative current collector plates can reach several times that of its own material resistance. The heat generation of the positive and negative current collector plates and their welding resistance is considerable enough and should not be directly ignored. By introducing the accurate welding resistance data of the positive and negative current collector plates and incorporating the corresponding heat generation into the heat source term, more accurate heat source data can be calculated, the thermal field distribution of the battery can be estimated more accurately, and the simulation accuracy of this method can be improved.

[0102] In some alternative implementation methods, the incubator experiment further includes the following steps:

[0103] S31: Place the battery that meets the product parameters in an incubator at 25 ± 0.5 °C, and perform constant current discharge on the battery with the working condition parameters so that its SOC discharges from 1 to 0;

[0104] S32: Monitor and record the change in the temperature at any position p0 on the battery during the discharge stage, and at the end of the discharge, p0 reaches the highest temperature, and record the T-t temperature rise curve in the discharge stage of the T-t characteristic curve; and

[0105] S33: After the battery discharge is completed, disconnect the battery circuit, monitor and record the change in the temperature of p0 during the heat exchange stage until its temperature drops to the same as that of the incubator, and record the T-t heat exchange curve in the heat exchange stage of the T-t characteristic curve.

[0106] In the specific implementation process, any point on the circumference of the cross-section passing through the axial center on the side of the battery is selected as p0. Taking a cylindrical battery as an example, the radius of the cylindrical battery is r cell , and the height is H. The position coordinates of p0 correspond to (r cell , H / 2).

[0107] In some alternative implementation methods, the parameter fitting of the thermal diffusion coefficient in the thermal field model further includes the following steps:

[0108] S41: Take the discrete point values (T exp , t) measured by the incubator experiment in the T-t heat exchange curve as the optimization objective. According to the product parameters, working condition parameters and the position information of p0, substitute the t values in the discrete point values into the formulas in S22 and S23 one by one, and solve for the T sim values corresponding to the t values one by one; and

[0109] S42: Set the objective function: min∑(T sim -T exp ) 2 , and use a numerical optimization algorithm for inversion to obtain the optimized h and k r and k z .

[0110] In some alternative implementation methods, the numerical optimization algorithm includes any one of the least squares method, the bisection method, the gradient descent method, the Newton method, and the conjugate gradient method. Using the above algorithms can all obtain the optimal values of h and k r and k z . In the specific implementation process, the SNOPT algorithm in the Newton method can be used to optimize the parameters h and k r and k z .

[0111] In some alternative implementation methods, the confirmation of the simulation accuracy further includes the following steps:

[0112] S43: Substitute the optimized h and k r and k z back into the thermal field model to obtain the simulated discrete point values (T' exp , t) corresponding one-to-one to the discrete point values (T sim , t) measured in the incubator experiment, and form the T-t simulation curve; and

[0113] S44: Compare the experimental result T exp value with the simulation result T' sim value, calculate the simulation error ΔT = |T' sim -T exp |, and statistically calculate the root mean square error of the T exp value and the T' sim value: RMSE(T exp , T' sim ) to verify the simulation accuracy.

[0114] The second aspect of the present application provides a battery thermal field simulation and calculation device, see Figure 3 , which is used to execute the battery thermal field simulation and calculation method described in any one of the above, and the device includes:

[0115] The thermal simulation module includes a data access port, a modeling unit, an electrochemistry physical field interface, and a thermal analysis unit. The data access port is used to obtain the product parameters and the working condition parameters. The modeling unit is used to construct a three-dimensional simulation battery based on the product parameters and the working condition parameters. The electrochemistry physical field interface is connected to the modeling unit and is used to construct the electrochemistry-thermal coupling model according to the three-dimensional simulation battery. The thermal analysis unit is connected to the electrochemistry physical field interface and couples the electrochemistry-thermal coupling model to establish a thermal field model T(λ0,p,t).

[0116] The experimental module is connected to the thermal simulation module and includes an experimental incubator, a battery test circuit, a temperature sensor for detecting the battery temperature, and a timer connected to the temperature sensor. The experimental incubator determines the experimental conditions based on the product parameters and the working condition parameters obtained from the thermal simulation module. The temperature sensor and the timer synchronously transmit the temperature and time data of the battery during the test in the test circuit.

[0117] The fitting module is connected to the experimental module and the thermal simulation module, obtains the data transmitted back by the experimental module to generate the T-t characteristic curve, substitutes the transmitted back data into the thermal field model T(λ0,p,t), performs parameter fitting on the thermal diffusion coefficient, and obtains the optimized thermal field model T(λ,p,t); and

[0118] The verification module is connected to the fitting module and the thermal simulation module, substitutes the T-t characteristic curve back into the optimized thermal field model T(λ,p,t), obtains a T-t simulation curve of the battery temperature changing with time, and verifies the simulation accuracy.

[0119] A third aspect of the present application provides a battery thermal field simulation and calculation system. The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the battery thermal field simulation and calculation method described in any one of the above is implemented. Program the battery thermal field simulation and calculation method, and use the processor to execute the computer program to implement the battery thermal field simulation and calculation method described in any one of the above, and utilize the computer computing power to improve the simulation and calculation efficiency.

[0120] A fourth aspect of the present application provides a storage medium. The storage medium includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute the battery thermal field simulation and calculation method described in any one of the above to realize the simulation and calculation of the battery thermal field.

[0121] The technical solutions of the present application will be described below with reference to Examples 1-2.

[0122] Example 1:

[0123] See Figure 4 , Example 1 includes the following parameters:

[0124]

[0125]

[0126] According to the parameters in the above table and the material information pre-stored in the electrochemistry physical field, an electrochemistry-thermal coupling model is established to obtain the heat source term:

[0127]

[0128] Furthermore, set the preset value of the thermal conductivity: And The preset value of the heat transfer coefficient: h0 and k m , The coordinate of the position to be predicted: p0(r0, z0), establish the relationship: See Figure 4 for the T-t characteristic curve in (see Figure 4 for the solid line in), substitute the t value of the discrete point value among them into the above relational expression to obtain T sim value, establish the objective function: min∑(T sim -T exp ) 2 , Optimize to get h = 34.519, k r = 1.638 and k z = 12.924, further substitute the optimized values into the thermal field model to get: T[(k r ,h,r);(k z ,k m ,z);t)], substitute the t value of the discrete point value in the T-t characteristic curve into the thermal field model again, verify to obtain the T-t simulation curve (see Figure 4 for the dotted line in), calculate the simulation error ΔT ≤ 4°C, RMSE = 1.499.

[0129] Example 2:

[0130] See Figure 4 and Figure 5 , The difference between Example 2 and Example 1 is only that the heat source term in Example 2 further introduces the welding resistance Q ohm of the positive and negative current collector plates, to obtain the heat source term:

[0131]

[0132] After verification, obtain the T-t simulation curve (see Figure 4 and Figure 5 for the dotted line in), compare with the T-t characteristic curve obtained from the incubator experiment (seeFigure 4 and Figure 5 (the solid line in), the simulation error ΔT < 2.5 °C and RMSE = 1.266 are calculated, which verifies the high accuracy of the temperature prediction of the thermal field model of this application for the interior and surface of the battery.

[0133] In the description of this specification, reference terms such as "some embodiments", "an embodiment" or similar descriptions mean that the specific features, structures, materials or characteristics described in connection with the embodiment are included in at least one embodiment or example. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0134] Although the embodiments of this embodiment have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and purposes of this embodiment, and the scope of this embodiment is defined by the claims and their equivalents.

Claims

1. A battery thermal field simulation and measurement method, characterized in that: The following steps are involved: S1: Input the product parameters and operating parameters of the battery, build a three-dimensional simulation battery and generate an electrochemical-thermal coupling model; S2: All heat-generating items in the electrochemical-thermal coupling model are combined as heat-generating source items, and the heat-generating source items are used as heat sources. The electrochemical-thermal coupling model is coupled to establish a thermal field model T(λ0, p, t) with temperature as the characterization item, where p represents any position inside and on the surface of the battery, t represents time, T(λ0, p, t) represents the temperature rise of any position at any time, and λ0 represents the initial setting value of the thermal diffusion coefficient; S3: Conducting a temperature chamber test on a physical battery with reference to the product parameters and the operating condition parameters to obtain a Tt characteristic curve showing changes in battery temperature over time; S4: Taking the Tt characteristic curve as the optimization target, perform parameter fitting on the thermal diffusion coefficient in the thermal field model T(λ0, p, t), and substitute the optimized thermal diffusion coefficient back into the thermal field model to obtain the optimized thermal field model T(λ, p, t). Based on the optimized thermal field model T(λ, p, t), calculate the Tt simulation curve of the model to verify the accuracy of the simulation.

2. The battery thermal field simulation and measurement method according to claim 1, characterized in that: The establishment of the thermal field model also includes the following steps: S21: The thermal diffusion coefficient λ is divided into a thermal conductivity coefficient k and a heat transfer coefficient h for separate calculations, wherein the thermal conductivity coefficient is used to describe the internal thermal conductivity efficiency of the battery, and the heat transfer coefficient is used to describe the boundary convection heat transfer efficiency between the battery and the external environment; S22: Define the boundary convection heat transfer calculation formula as: q0 = -h(TT amb ), where h is the heat transfer coefficient, W / (m 2 ·K), T represents the battery surface temperature, T amb represents the ambient temperature, and the heat transfer coefficient has an initial setting value h0; S23: The thermal conductivity k is further divided into radial thermal conductivity k r and axial thermal conductivity k z , the p should also be decomposed into: p(r, z), the corresponding thermal field model is: T[(k r ,r);(k z ,z);h;t)]; Wherein, T represents absolute temperature, K; r represents radial coordinate, m; z represents axial coordinate, m; the radial thermal conductivity k r The axial thermal conductivity k z There is an initial setting value and 3. The battery thermal field simulation and measurement method according to claim 2, characterized in that: The outer surface of the battery is divided into the side surface and the end surface for welding with the external metal connector. The heat transfer coefficient h is only used to describe the boundary convection heat transfer of the side surface. The boundary convection condition of the end surface is equivalent to the solid thermal conductivity condition of the end surface and the external metal connector. The equivalent heat transfer coefficient k of the end surface is obtained by conversion based on the material properties of the external metal connector. m .

4. The battery thermal field simulation and measurement method according to claim 2, characterized in that: The calculation of the heat source term includes the following steps: S24: The lumped parameter method is used to describe the dynamic electrochemical process. According to the SOC dynamic equation: The integral defines the average SOC as: Define the voltage equation: Where τ represents the diffusion time constant, s; η IR represents the ohmic overpotential, V; η act represents the activation overpotential, V; η conc represents the concentration overpotential, V; E OCV Indicates the battery open circuit voltage, V; S25: Establish the equation based on Fourier's heat transfer law: Where ρ represents density, kg / m 3 ; C p represents the constant pressure heat capacity, J / (kg·K); u represents the velocity vector, m / s; k represents the thermal conductivity, W / (m·K); Q h Represents the heat source term, W / m 3 ; S26: Couple the electrochemical equation in S24 to obtain the relationship between the heat source term: in, Indicates the entropy thermal coefficient, V / K; Q conc Indicates the heat generation power contributed by the concentration overpotential, W.

5. The battery thermal field simulation and measurement method according to claim 4, characterized in that: The η IR From the equation: to obtain; The η act From the equation: to obtain; Where R is the universal gas constant, J / (mol·K); F is the Faraday constant, C / mol; I cell Indicates the actual working current of the battery, A; I 1C,cell represents the 1C discharge current of the battery; J0 represents the dimensionless charge exchange current.

6. The battery thermal field simulation and measurement method according to claim 4, characterized in that: The η conc satisfy: Combined with the SOC dynamic equation, the Q conc satisfy: Among them, E OCV Indicates open circuit voltage, V; Q cell Indicates the battery capacity, A·h.

7. The battery thermal field simulation and measurement method according to claim 4, characterized in that: The heat source term Q h It should also include the heat generation power Q contributed by the battery positive and negative collector plates and their welding resistance ohm , the Q ohm From the equation: Find, where I cell Indicates the actual working current of the battery, R ohm Indicates the positive and negative current collectors of the battery and their welding resistance.

8. The battery thermal field simulation and measurement method according to claim 2, characterized in that: The incubator experiment further comprises the following steps: S31: placing the battery meeting the product parameters in a temperature box at 25±0.5°C, and performing constant current discharge on the battery at the operating parameters, so that the SOC is discharged from 1 to 0; S32: monitoring and recording the temperature change of any position p0 on the battery during the discharge stage, and recording the maximum temperature of p0 at the end of discharge to obtain a Tt temperature rise curve of the discharge stage in the Tt characteristic curve; S33: After the battery is discharged, the battery path is disconnected, and changes in the temperature of p0 during the heat exchange stage are monitored and recorded until the temperature drops to the same as that of the temperature box, and the Tt heat exchange curve of the heat exchange stage in the Tt characteristic curve is recorded.

9. The battery thermal field simulation and measurement method according to claim 8, characterized in that: The parameter fitting of the thermal diffusion coefficient in the thermal field model further includes the following steps: S41: Take the discrete point value (T exp , t) as the optimization target, according to the product parameters and working condition parameters and the position information p0(r0,z0) of p0, the t values in the discrete point values are substituted into the thermal field model one by one Solve for T that corresponds to the t value one by one sim value; and S42: Set the objective function: min∑(T sim -T exp ) 2 , use numerical optimization algorithm to invert and get the optimized h, k r With k z .

10. The battery thermal field simulation and measurement method according to claim 9, characterized in that: The numerical optimization algorithm includes any one of the least squares method, bisection method, gradient descent method, Newton method and conjugate gradient method.

11. The battery thermal field simulation and calculation method according to claim 9, characterized in that: The verification simulation accuracy further comprises the following steps: S43: The optimized h and k r With k z Substituting back into the thermal field model, we get the discrete point value (T exp , t) one-to-one corresponding simulated discrete point value (T' sim , t), and forming the Tt simulation curve; and S44: Comparative experimental results T exp Value and simulation results T' sim value, calculate the simulation error ΔT=|T' sim -T exp |, Statistics T exp Value and T' sim Root mean square error of the value: RMSE(T exp , T' sim ), verify the simulation accuracy.

12. The battery thermal field simulation and measurement method according to claim 1, characterized in that: The product parameters include the material physical parameters and geometric characteristic parameters of the battery; the operating parameters include the electrochemical parameters and thermodynamic parameters of the battery.

13. A battery thermal field simulation and measurement device, characterized in that: The device is used to execute the battery thermal field simulation and measurement method according to any one of claims 1 to 12, comprising: A thermal simulation module includes a data access port, a modeling unit, an electrochemical physical field interface, and a thermal analysis unit. The data access port is used to obtain the product parameters and the operating condition parameters. The modeling unit is used to construct a three-dimensional simulated battery based on the product parameters and the operating condition parameters. The electrochemical physical field interface is connected to the modeling unit to construct the electrochemical-thermal coupling model based on the three-dimensional simulated battery. The thermal analysis unit is connected to the electrochemical physical field interface and couples the electrochemical-thermal coupling model to establish a thermal field model T(λ0, p, t). an experimental module connected to the thermal simulation module, comprising an experimental incubator, a battery test circuit, a temperature sensor for detecting battery temperature, and a timer connected to the temperature sensor, wherein the experimental incubator determines experimental conditions based on the product parameters and operating condition parameters obtained from the thermal simulation module, and the temperature sensor and the timer synchronously transmit back temperature and time data of the battery during testing in the test circuit; a fitting module, connecting the experimental module and the thermal simulation module, obtaining the return data from the experimental module to generate the Tt characteristic curve, substituting the return data into the thermal field model T(λ0, p, t), performing parameter fitting on the thermal diffusion coefficient, and obtaining the optimized thermal field model T(λ, p, t); and A verification module connects the fitting module and the thermal simulation module, substitutes the Tt characteristic curve back into the optimized thermal field model T(λ, p, t), obtains a Tt simulation curve of the battery temperature changing with time, and verifies the simulation accuracy.

14. A battery thermal field simulation and measurement system, characterized in that: The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the battery thermal field simulation and measurement method according to any one of claims 1 to 12 is implemented.

15. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the battery thermal field simulation and measurement method according to any one of claims 1 to 12.

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