Battery physical property parameter identification method and device and storage medium

Abstract

The invention relates to a battery physical property parameter identification method. The method comprises the following steps: acquiring temperature response measured data of a battery; performing battery temperature response simulation analysis to obtain temperature response simulation data of the battery; the physical property parameters of the battery are used as optimization parameters, the goodness of fit between the temperature response measured data and the temperature response simulation data meets a preset condition as an optimization target, the temperature response measured data and the temperature response simulation data are used as input parameters, and iterative calculation is performed by adopting a swarm intelligence algorithm to obtain a temperature response simulation result. And updating the temperature response simulation data in the input parameters of the next iterative calculation by adopting a result of each iterative calculation until a global optimal solution is found or a preset number of iterations is reached. The invention further provides a battery physical property parameter identification device and a storage medium. According to the method, the mutual influence of various physical property parameters of the battery is considered, the battery physical property parameters more conforming to the actual situation can be obtained, optimization is carried out by adopting the swarm intelligence algorithm, the consumed time is shorter, and the precision is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of power batteries, and in particular to a method, device and storage medium for identifying battery physical property parameters. Background Art

[0002] The safety assessment of power batteries generally adopts test methods or simulation methods. The simulation method has the following advantages over the test method: lower cost, more test conditions, and more in-depth characterization analysis. For the safety simulation of power batteries, the input of power battery physical parameters has a significant impact on the accuracy of the power battery simulation model, so it is particularly important to accurately determine the physical parameters of the power battery.

[0003] The test methods for the specific heat capacity of power battery materials include natural cooling method, mixed cooling method and other methods, or use differential scanning calorimeter, accelerated adiabatic calorimeter, heat flow calorimeter and other instruments for measurement. The measurement methods of thermal conductivity of power battery materials can be divided into steady-state method and non-steady-state method. The principle of steady-state method and non-steady-state method is to locally heat the material, measure the temperature difference or temperature change of the object, and use the heat conduction equation to calculate the thermal conductivity of the material. When the power battery is thermally runaway, violent chemical reactions occur inside, and the internal materials and structures of the power battery undergo great changes, resulting in corresponding changes in the physical parameters of the power battery. At the same time, some structural parts in the power battery are very complex (such as the battery core body). In the simulation process, these structural parts need to be equivalently simplified and regarded as an integrated equivalent component. The physical parameters of the equivalent component are uncertain and do not present a superimposed linear relationship. Therefore, it is difficult to determine the physical parameters of the power battery, especially the physical parameters of the equivalent component.

[0004] There are some methods for identifying the physical parameters of power batteries in the prior art, which can determine the physical parameters of power batteries to a certain extent. However, the identification methods for the physical parameters of power batteries in the prior art usually have the following problems: the physical parameters of the power battery are measured separately, without comprehensively considering the influence between the physical parameters, and there are large differences with the actual physical parameters of the power battery. When using the identification model to optimize the physical parameters of the power battery, it is necessary to constantly manually adjust the parameters of the identification model. The optimization process is time-consuming and there is no guarantee that the optimization result is the global optimal solution. Summary of the invention

[0005] The purpose of the present invention is to provide a battery physical property parameter identification method, device and storage medium to alleviate or eliminate at least one of the above-mentioned technical problems.

[0006] A battery physical property parameter identification method according to the present invention comprises the following steps:

[0007] Obtain the measured data of the battery's temperature response;

[0008] Use the battery temperature response simulation model to perform battery temperature response simulation analysis to obtain battery temperature response simulation data;

[0009] The physical properties of the battery are used as optimization parameters, and the degree of agreement between the temperature response measured data and the temperature response simulation data that meets the preset conditions is used as the optimization target. The temperature response measured data and the temperature response simulation data are used as input parameters, and a swarm intelligence algorithm is used for iterative calculation. The result of each iterative calculation is used to update the temperature response simulation data in the input parameters of the next iterative calculation until the global optimal solution is found or the preset number of iterations is reached.

[0010] Optionally, the swarm intelligence algorithm includes a particle swarm optimization algorithm.

[0011] Optionally, the objective function of the optimization target is: Where n is the number of temperature measurement points in the battery temperature response test, w i is the weight coefficient, Y i is the heating temperature for battery temperature response simulation analysis, T i is the heating temperature of the battery temperature response test, t0 is the start time of the iterative calculation, and t1 is the end time of the iterative calculation; the preset condition is that the objective function converges to the minimum value.

[0012] Optionally, the battery temperature response simulation model includes six one-dimensional models respectively corresponding to six side surfaces of the battery, and heat conduction equations are respectively established between the six one-dimensional models.

[0013] Optionally, the heat conduction equation is:

[0014]

[0015] Where ρ is the density, C p is the specific heat capacity, T is the temperature, t is the time, q v is the body heat source, λ is the thermal conductivity, and x, y, and z are the coordinate directions.

[0016] Optionally, the physical properties of the battery include density, specific heat capacity, thermal conductivity in the thickness direction, thermal conductivity in the length direction, and thermal conductivity in the height direction.

[0017] Optionally, the battery physical property parameter identification method also includes the following steps: performing a battery temperature response test to obtain the temperature response actual measured data; the battery temperature response test includes the following steps: arranging a heating element on one side of the battery to be tested, and arranging temperature sensors on the other five sides of the battery to be tested; using the heating element to heat the battery to be tested, and the temperature sensor to obtain the temperature response actual measured data of the battery to be tested.

[0018] The present invention also proposes a battery physical property parameter identification device, comprising:

[0019] An acquisition module is used to obtain the actual temperature response data of the battery;

[0020] A simulation module is used to: use a battery temperature response simulation model to perform battery temperature response simulation analysis and obtain battery temperature response simulation data;

[0021] The calculation module is used to: use the physical property parameters of the battery as optimization parameters, the degree of agreement between the temperature response measured data and the temperature response simulation data that meets the preset conditions as the optimization target, use the temperature response measured data and the temperature response simulation data as input parameters, use the swarm intelligence algorithm to perform iterative calculations, and use the result of each iterative calculation to update the temperature response simulation data in the input parameters of the next iterative calculation until the global optimal solution is found or the preset number of iterations is reached.

[0022] Optionally, the computing module includes multiple swarm intelligence algorithm modules.

[0023] The present invention also proposes a storage medium on which a program is stored. When the program is executed by a processor, the steps of the battery physical property parameter identification method according to any one of the above items are implemented.

[0024] The present invention comprehensively considers the mutual influence of various physical parameters of the battery during the battery physical parameter identification process, and can obtain battery physical parameters that are more in line with the actual situation. The present invention adopts a swarm intelligence algorithm for optimization, and the optimization process takes less time and has higher accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of a battery physical property parameter identification method described in some embodiments;

[0026] Figure 2 is a schematic diagram of a battery temperature response test described in some embodiments;

[0027] Figure 3 is a schematic diagram of a battery temperature response simulation model described in some embodiments;

[0028] Figure 4 A test heating power curve diagram of a battery temperature response test described in some embodiments;

[0029] Figure 5 is a test temperature response curve diagram of a battery temperature response test described in some embodiments;

[0030] Figure 6is a flow chart of the particle swarm optimization algorithm described in some embodiments;

[0031] Figure 7 A schematic diagram of the coupling relationship between the particle swarm optimization algorithm and the battery temperature response simulation model described in some embodiments;

[0032] Figure 8 This is a comparison chart between the test results and the simulation results.

[0033] In the figure, 10 is a battery to be tested, 20 is a heating plate, 30 is a first thermocouple, 40 is a second thermocouple, 50 is a third thermocouple, 60 is a fourth thermocouple, 70 is a fifth thermocouple, 80 is a battery temperature response simulation model, 801 is a first model unit, 802 is a second model unit, 803 is a third model unit, 804 is a fourth model unit, 805 is a fifth model unit, and 806 is a sixth model unit. DETAILED DESCRIPTION

[0034] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0035] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0036] like Figure 1 A battery physical property parameter identification method shown includes the following steps:

[0037] S100: Acquire actual temperature response data of the battery;

[0038] S200: Performing battery temperature response simulation analysis using a battery temperature response simulation model to obtain battery temperature response simulation data;

[0039] S300: The physical properties of the battery are used as optimization parameters, and the degree of agreement between the temperature response measured data and the temperature response simulation data that meets the preset conditions is used as the optimization target. The temperature response measured data and the temperature response simulation data are used as input parameters, and a swarm intelligence algorithm is used for iterative calculation. The result of each iterative calculation is used to update the temperature response simulation data in the input parameters of the next iterative calculation until the global optimal solution is found or the preset number of iterations is reached.

[0040] By adopting the above-mentioned battery physical property parameter identification method, a swarm intelligence algorithm is used to perform iterative calculations to find the optimal solution, and the battery physical property parameters obtained by each iterative calculation are used to update the battery temperature response simulation model and temperature response simulation data. The optimization process takes less time and is more accurate.

[0041] In specific implementation, a suitable swarm intelligence algorithm can be selected from ant colony algorithm, particle swarm algorithm, artificial fish swarm algorithm, cat colony algorithm, bee colony algorithm and other swarm intelligence algorithms for iterative calculation. As a preferred embodiment, the swarm intelligence algorithm selects particle swarm optimization algorithm.

[0042] In specific implementation, the above-mentioned battery physical property parameter identification method is suitable for identifying the physical property parameters of vehicle power batteries.

[0043] In a specific implementation, the physical parameters of the battery include density, specific heat capacity, thermal conductivity in the thickness direction, thermal conductivity in the length direction, and thermal conductivity in the height direction.

[0044] In some embodiments, the objective function of the optimization objective is: Where n is the number of temperature measurement points in the battery temperature response test, w i is the weight coefficient, which is a preset value, which can be a calibrated value or a value set based on previous simulation experience. i is the heating temperature for battery temperature response simulation analysis, T i is the heating temperature of the battery temperature response test, t0 is the start time of the iterative calculation, and t1 is the end time of the iterative calculation; the preset condition is: the objective function converges to the minimum value. The use of the above objective function and preset conditions can make the temperature response simulation data more consistent with the temperature response measured data, which helps to improve the recognition accuracy of battery physical parameters.

[0045] In some embodiments, the battery temperature response simulation model includes six one-dimensional models corresponding to the six sides of the battery, and the six one-dimensional models have heat conduction equations established between each other. Since the battery has anisotropic thermal conductivity, and the battery temperature response simulation model needs to fit the temperature response of the six sides at the same time, in order to ensure the accuracy of the simulation, one-dimensional models are established for the six sides of the battery during modeling, and corresponding boundary conditions are set for each one-dimensional model. The above-mentioned battery temperature response simulation model takes into account the mutual influence of various physical parameters of the battery, and can obtain physical parameters that are more in line with reality.

[0046] As a specific example, when the battery is locally heated, the temperature field inside the battery changes with time, and the above heat conduction equation is:

[0047]

[0048] Where ρ is the density, C p is the specific heat capacity, T is the temperature, t is the time, q v is the body heat source, λ is the thermal conductivity, and x, y, and z are the coordinate directions.

[0049] By using the above-mentioned battery temperature response simulation model for simulation analysis, the temperature response data of the battery temperature response simulation model, namely the temperature response simulation data, can be obtained. The temperature response simulation data and the temperature response measured data will be used as input data of the swarm intelligence optimization algorithm.

[0050] In specific implementation, the temperature response measured data of the battery is usually measured by a battery temperature response test. Therefore, the battery physical property parameter identification method also includes the following steps: performing a battery temperature response test to measure the temperature response measured data.

[0051] As a specific example, a battery temperature response test generally includes the following steps: a heating element is arranged on one side of the battery to be tested, and temperature sensors are arranged on the other five sides of the battery to be tested; the battery to be tested is heated by the heating element, and the temperature sensor measures the actual temperature response data of the battery to be tested.

[0052] When conducting a battery temperature response test, you need to prepare test equipment such as the battery to be tested, temperature sensors, heating elements, and clamping tools. Paste the heating element on one side of the battery to be tested, and apply thermal grease between the heating element and the side of the battery to be tested to make the thermal contact more complete. Place each temperature sensor on each side of the battery to be tested except the heating surface. After the heating element is turned on, obtain the temperature response data of each side of the battery to be tested through each temperature sensor.

[0053] In specific implementation, the above-mentioned battery to be tested, temperature sensor, heating plate, etc. can be replaced by alternative parts as needed, which is universal. The above-mentioned battery temperature response test scheme is simple, easy to operate and has high measurement accuracy.

[0054] In specific implementation, the heating element may be a small circular heating sheet, and the heating power may be set to 50 W. The temperature sensor may be a thermocouple sensor, and the temperature sensor is usually arranged at the center of the side surface of the battery to be tested except the heating surface.

[0055] When conducting a battery temperature response test, the battery to be tested will be heated, and when the battery to be tested is heated to a certain degree, thermal runaway will occur, violent chemical reactions will occur inside the battery to be tested, and various physical parameters of the battery to be tested will change dramatically. Therefore, when using a heating element to heat the battery to be tested, when the battery to be tested is in a normal state, the heating temperature cannot exceed the self-heating starting temperature of the battery to be tested, and when the battery to be tested is in a thermal runaway state, the heating temperature does not exceed 60°C.

[0056] In specific implementation, in order to ensure the accuracy of the measurement results of the battery temperature response test, each test equipment needs to be placed in an insulating environment for testing.

[0057] In specific implementation, before using the battery temperature response simulation model to perform battery temperature response simulation analysis, it is necessary to establish a battery temperature response simulation model. Therefore, the battery physical property parameter identification method also includes the following steps: establishing a battery temperature response simulation model.

[0058] In specific implementation, the battery temperature response simulation model can be established by referring to the battery parameters of the battery to be tested in the battery temperature response test and the test parameters of the battery temperature response test. Figure 3 As shown, when establishing the battery temperature response simulation model 80, six one-dimensional models can be established for the six sides of the battery to be tested, and the six one-dimensional models are respectively a first model unit 801, a second model unit 802, a third model unit 803, a fourth model unit 804, a fifth model unit 805 and a sixth model unit 806. The first model unit 801, the second model unit 802, the third model unit 803, the fourth model unit 804, the fifth model unit 805 and the sixth model unit 806 are interconnected, and a heat conduction equation is set on the connection.

[0059] In order to better illustrate the battery physical property parameter identification method of the present application, the battery physical property parameter identification method is described below with reference to a specific example.

[0060] like Figure 2As shown, in this specific example, when conducting a battery temperature response test, a circular heating sheet 20 is pasted to the center of the front face of the battery 10 to be tested, and a thermally conductive adhesive is applied to ensure that the heating sheet 20 is in full contact with the surface of the battery 10 to be tested. The first thermocouple 30, the second thermocouple 40, the third thermocouple 50, the fourth thermocouple 60 and the fifth thermocouple 70 are respectively arranged at the center of the other side faces of the battery 10 to be tested except for the heating face, and the temperature of each side face of the battery 10 to be tested is measured. In this specific example, the test heating power curve of the battery temperature response test is shown in FIG. Figure 4 As shown, the test temperature response curve of the battery temperature response test is as follows Figure 5 shown.

[0061] like Figure 3 As shown, when establishing the battery temperature response simulation model 80, six one-dimensional models are separately established for each surface of the battery to be tested, and the six one-dimensional models are respectively the first model unit 801, the second model unit 802, the third model unit 803, the fourth model unit 804, the fifth model unit 805 and the sixth model unit 806, and the first model unit 801, the second model unit 802, the third model unit 803, the fourth model unit 804, the fifth model unit 805 and the sixth model unit 806 are connected to each other, and the corresponding heat conduction equation is set on the connection. According to the test conditions of the battery temperature response test, parameters such as heating power and battery size are input into the battery temperature response simulation model. In this specific example, the heating power is 50W, and the battery size parameters are: 148mm (length) × 40mm (width) × 84mm (height).

[0062] In this specific example, the swarm intelligence algorithm used is the particle swarm optimization algorithm, which is a typical swarm intelligence optimization algorithm. It can iteratively find the optimal solution from a specific or random solution and evaluate the quality of the solution through fitness. This algorithm has the characteristics of easy implementation, high accuracy and fast convergence, and has great advantages in solving practical problems.

[0063] More specifically, Figure 6As shown, the iterative calculation using the particle swarm algorithm includes the following steps: S301: initializing parameters, setting the initial value and range of the optimization parameters; S302: initializing particles; S303: calculating fitness, calculating the initial fitness of each particle in the particle swarm; S304: calculating individual extreme values ​​and group extreme values; S305: updating particle speed and position according to the optimal particle parameters in the particle swarm; S306: recalculating particle fitness; S307: calculating individual extreme values ​​and group extreme values; S308: judging whether the global optimal solution is found or the preset number of iterations is reached through the objective function, if the global optimal solution is found or the preset number of iterations is reached, the optimal particle parameters are output, otherwise the particle speed and position are updated, the particle fitness is recalculated, and the individual extreme values ​​and group extreme values ​​are calculated until the global optimal solution is found or the preset number of iterations is reached.

[0064] In this specific example, the coupling relationship between the battery temperature response simulation model and the swarm intelligence algorithm is as follows: Figure 7 As shown. At the beginning of the iterative calculation, the initial values ​​of various optimization parameters are input into the battery temperature response simulation model, and the temperature response simulation data output by the battery temperature response simulation model and the temperature response measured data are input into the particle swarm optimization algorithm for calculation. The parameters of each particle are updated according to the optimal particle parameters in the particle swarm, and the objective function is used to determine whether the global optimal solution is found or the preset number of iterations is reached. If the global optimal solution is not found or the number of iterations is less than the preset number of iterations, the parameters of each particle are brought into the battery temperature response simulation model to update the battery temperature response simulation model, and the updated battery temperature response simulation model is used to continue to simulate and calculate new temperature response simulation data. The new temperature response simulation data are all used as input parameters for the next iterative calculation of the particle swarm optimization algorithm.

[0065] In this specific example, the initial values ​​and ranges of the physical parameters of the battery are shown in the following table:

[0066] Physical properties Initial Value scope Density (kg / m3) 2300 (2200,2400) Specific heat capacity (J / (kg·K)) 1500 (1000,2000) Thermal conductivity in thickness direction (W / (m·K)) 20.3 (15,25) Thermal conductivity in length direction (W / (m·K)) 0.6 (0.1,2) Thermal conductivity in height direction (W / (m·K)) 20.1 (15,25)

[0067] In this specific example, the comparison between the experimental results and the simulation results is as follows: Figure 8 As shown. Figure 8 The simulation results and the test results have a good overlap, and the physical property parameters obtained by identification are universal. The battery physical property parameter identification method proposed in this application can automatically find the global optimal solution without manual optimization. The identification process takes less time and the identification results have higher accuracy.

[0068] The present invention also proposes a battery physical property parameter identification device, comprising:

[0069] An acquisition module, used to obtain the measured temperature response data of the battery;

[0070] A simulation module is used to: use a battery temperature response simulation model to perform battery temperature response simulation analysis and obtain battery temperature response simulation data;

[0071] The calculation module is used to: use the physical properties of the battery as optimization parameters, the degree of agreement between the temperature response measured data and the temperature response simulation data that meets the preset conditions as the optimization target, the temperature response measured data and the temperature response simulation data as input parameters, use the swarm intelligence algorithm for iterative calculation, and use the result of each iterative calculation to update the temperature response simulation data in the input parameters of the next iterative calculation until the global optimal solution is found or the preset number of iterations is reached.

[0072] The above-mentioned battery physical property parameter identification device can be used to execute the above-mentioned battery physical property parameter identification method, and can achieve the effect of the battery physical property parameter identification method, which will not be elaborated again.

[0073] In some embodiments, the calculation module includes multiple swarm intelligence algorithm modules. By setting multiple swarm intelligence algorithm modules in the calculation module, any swarm intelligence algorithm can be selected for iterative calculation during iterative calculation, which is easy to operate.

[0074] More specifically, the plurality of swarm intelligence algorithms may include an ant colony algorithm module, a particle swarm algorithm module, an artificial fish swarm algorithm module, a cat swarm algorithm module and a bee colony algorithm module.

[0075] Furthermore, the battery physical property parameter identification device also includes an input module and an output module. The input module is used for the battery parameters of the battery to be tested and the test parameters of the battery temperature response test. The input module can also be used to input the optimization parameters of the iterative calculation, the initial value of the input parameter, the parameter range of the input parameter and the optimization objective function. The output module is used to display the various physical property parameters obtained by the simulation calculation respectively, and to comprehensively display the temperature response simulation data obtained by the simulation calculation and the input temperature response measured data.

[0076] The present invention also proposes a storage medium on which a program is stored. When the program is executed by a processor, the steps of the battery physical property parameter identification method according to any one of the above items are implemented.

[0077] The above embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or changes made by those skilled in the art on the basis of the present invention are all within the protection scope of the present invention. In the description of this specification, the description of reference terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics of the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does 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. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

Claims

1. A method for identifying battery physical property parameters, characterized in that: The following steps are involved: Obtain the measured data of the battery's temperature response; Use the battery temperature response simulation model to perform battery temperature response simulation analysis to obtain battery temperature response simulation data; The physical properties of the battery are used as optimization parameters, and the degree of agreement between the temperature response measured data and the temperature response simulation data that meets the preset conditions is used as the optimization target. The temperature response measured data and the temperature response simulation data are used as input parameters, and a swarm intelligence algorithm is used for iterative calculation. The result of each iterative calculation is used to update the temperature response simulation data in the input parameters of the next iterative calculation until the global optimal solution is found or the preset number of iterations is reached.

2. The battery physical property parameter identification method according to claim 1, characterized in that: The swarm intelligence algorithm includes a particle swarm optimization algorithm.

3. The battery physical property parameter identification method according to claim 1, characterized in that: The objective function of the optimization objective is: Where n is the number of temperature measurement points in the battery temperature response test, w i is the weight coefficient, Y i is the heating temperature for battery temperature response simulation analysis, T i is the heating temperature of the battery temperature response test, t0 is the start time of the iterative calculation, and t1 is the end time of the iterative calculation; the preset condition is that the objective function converges to the minimum value.

4. The battery physical property parameter identification method according to claim 1, characterized in that: The battery temperature response simulation model includes six one-dimensional models respectively corresponding to six side surfaces of the battery, and heat conduction equations are respectively established between the six one-dimensional models.

5. The battery physical property parameter identification method according to claim 4, characterized in that: The heat conduction equation is: Where ρ is the density, C p is the specific heat capacity, T is the temperature, t is the time, q v is the body heat source, λ is the thermal conductivity, and x, y, and z are the coordinate directions.

6. The battery physical property parameter identification method according to claim 1, characterized in that: The physical parameters of the battery include density, specific heat capacity, thermal conductivity in thickness direction, thermal conductivity in length direction and thermal conductivity in height direction.

7. The battery physical property parameter identification method according to claim 1, characterized in that: The following steps are also included: A battery temperature response test is performed to obtain the temperature response actual measured data; the battery temperature response test comprises the following steps: a heating element is arranged on one side of the battery to be tested, and temperature sensors are arranged on the other five sides of the battery to be tested respectively; the battery to be tested is heated by the heating element, and the temperature sensor obtains the temperature response actual measured data of the battery to be tested.

8. A battery physical property parameter identification device, characterized in that: include: An acquisition module, used to obtain the measured temperature response data of the battery; A simulation module is used to: use a battery temperature response simulation model to perform battery temperature response simulation analysis and obtain battery temperature response simulation data; The calculation module is used to: use the physical property parameters of the battery as optimization parameters, the degree of agreement between the temperature response measured data and the temperature response simulation data that meets the preset conditions as the optimization target, use the temperature response measured data and the temperature response simulation data as input parameters, use the swarm intelligence algorithm to perform iterative calculations, and use the result of each iterative calculation to update the temperature response simulation data in the input parameters of the next iterative calculation until the global optimal solution is found or the preset number of iterations is reached. 9 . The battery physical property parameter identification device according to claim 8 , wherein the calculation module comprises a plurality of swarm intelligence algorithm modules.

10. A storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the steps of the battery physical property parameter identification method according to any one of claims 1 to 7 are implemented.

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