Method for measuring entropy coefficient of energy storage battery

Abstract

The invention relates to the technical field of electrochemical energy storage system thermal management, and provides an energy storage battery entropy coefficient determination method, which comprises: constructing a finite element model in COMSOL simulation software; carrying out a round of complete battery constant-current charging and discharging experiment on the target battery, and recording related data of the experiment; based on the finite element model, fitting a battery cell thermal conductivity coefficient, a large-surface convection heat transfer coefficient, a bottom-surface convection heat transfer coefficient, battery cell heat, positive tab heat and negative tab heat in sequence, and updating parameters of the finite element model until the number of iterations reaches a preset upper limit; and calculating an entropy coefficient in combination with experimental data. By means of the method, the efficiency and precision of entropy coefficient measurement of the high-capacity energy storage battery can be improved with low cost.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology for electrochemical energy storage systems, and specifically to a method for measuring the entropy coefficient of an energy storage battery. Background Technology

[0002] The entropy coefficient of a battery, as a crucial parameter affecting reversible heat generation, not only directly determines the reversible heat generation during battery operation but can also be used to analyze battery aging behavior. However, how to efficiently obtain the entropy coefficient while ensuring accuracy remains a pressing problem to be solved.

[0003] Currently, the mainstream methods for obtaining the entropy coefficient mainly include the potentiometric method, the calorimetric method, and the numerical inversion method. The potentiometric method calculates the entropy coefficient by measuring the change in open-circuit voltage at different temperatures. The principle is clear, as exemplified by Chinese invention patent application CN110361662B, "A Method for Determining the Temperature Entropy Coefficient of a Lithium-ion Battery." This method is based on a three-electrode lithium-ion battery. A temperature sensing line is set up on the tested three-electrode lithium-ion battery, and the temperature entropy coefficient is obtained by analyzing the specific changes in the positive electrode voltage, negative electrode voltage, and total cell voltage at different temperatures. However, the above methods are cumbersome, time-consuming, and require high precision experimental equipment. The calorimetric method typically relies on an adiabatic accelerated calorimeter (ARC) to measure the total heat generation of the battery under specific operating conditions, and then uses the heat generation equation to inversely deduce the entropy coefficient. Although reliable, this method requires specialized testing equipment and can cause irreversible damage to the battery due to high-temperature conditions. The numerical inversion method indirectly obtains the entropy coefficient by constructing a battery thermal model and using the deviation between the temperature response and experimental data to correct the parameters. This method is economical and efficient, but it is highly dependent on the accuracy of the thermal model. Summary of the Invention

[0004] The technical problem to be solved by this invention is how to improve the efficiency and accuracy of entropy coefficient measurement of large-capacity energy storage batteries at low cost.

[0005] The present invention solves the above-mentioned technical problems through the following technical means: This invention provides a method for determining the entropy coefficient of an energy storage battery, comprising the following steps: S1. Construct a finite element model in COMSOL simulation software based on the size and material of the target battery; S2. Conduct a complete constant current charge-discharge experiment on the target battery in a constant temperature chamber, and record the battery load voltage at a sampling frequency of 1Hz. Current Temperature data from 16 temperature measurement points Initialize the parameters of the finite element model, including the cell heat. Positive electrode heat Negative electrode ear heat Thermal conductivity of the battery cell Large convective heat transfer coefficient and bottom convective heat transfer coefficient 2; among which, The values ​​are divided into segments based on the increase or decrease of 5% SOC, totaling 20 values; S3. Solve the heat influence coefficient matrix of the positive electrode tab under steady state based on the finite element model. Negative electrode ear heat influence coefficient matrix And the cell heat effect coefficient matrix ; S4. Based on the experimental data obtained in step S2, the thermal conductivity coefficient of the battery cell is fitted using a multi-objective particle swarm optimization algorithm. Large convective heat transfer coefficient and bottom convective heat transfer coefficient Update the finite element model parameters; S5, based on the optimized , and Based on the experimental data obtained in step S2, the heat of the battery cell is fitted using a time-series particle swarm optimization algorithm. Positive electrode heat and negative electrode ear heat And update the finite element model parameters; S6. Repeat steps S3-S5 until the number of iterations reaches the preset limit. S7, Output cell heat Positive electrode heat and negative electrode ear heat The entropy coefficient is calculated by combining experimental data.

[0006] Furthermore, the finite element model described in step S1 is specifically as follows:

[0007] in, For simulation Temperature distribution at time, where The coordinates of the temperature point; Representative finite element numerical solution process; It is a set of key thermal parameters. It is the heat generated by the battery. This refers to the battery's operating conditions. These are the heat generation parameters of the battery. It is a set of boundary conditions. It is the geometric configuration of the battery system. This is the temperature distribution at the initial moment of the simulation; This refers to the runtime.

[0008] Furthermore, the complete battery constant current charge-discharge experiment described in step S2 specifically includes: First, place the target battery in a constant temperature chamber, charge it to the cutoff voltage, and let it stand for 4 hours; then, discharge it at a constant current of 1C to the cutoff voltage and let it stand for 3.5 hours; finally, recharge it at a constant current of 1C to the cutoff voltage and let it stand for 3.5 hours.

[0009] Furthermore, the 16 temperature points mentioned in step S2 are specifically as follows: Define the large surface of the battery with the positive tab on the left and the negative tab on the right as surface A; define the side surface with the negative tab as surface B; define the large surface of the battery with the positive tab on the right and the negative tab on the left as surface C; define the side surface with the positive tab as surface D; define the top surface as surface E; define the bottom surface as surface F. Among them, temperature measuring point 1 is located at the upper left corner of surface A, near the positive electrode tab; temperature measuring point 2 is located at the upper right corner of surface A, near the negative electrode tab; temperature measuring point 3 is located at the center of surface A; temperature measuring point 4 is located at the lower left corner of surface A; temperature measuring point 5 is located at the lower right corner of surface A; temperature measuring point 6 is located at the top of surface B; temperature measuring point 7 is located at the bottom of surface B; temperature measuring point 8 is located at the upper left corner of surface C, near the negative electrode tab; temperature measuring point 9 is located at the upper right corner of surface C, near the positive electrode tab; temperature measuring point 10 is located at the center of surface C; temperature measuring point 11 is located at the lower left corner of surface C; temperature measuring point 12 is located at the lower right corner of surface C; temperature measuring point 13 is located at the top of surface D; temperature measuring point 14 is located at the bottom of surface D; temperature measuring point 15 is located at the center of surface E; and temperature measuring point 16 is located at the center of surface F.

[0010] Further, step S3 specifically includes: By sequentially changing the finite element model 、 、 Based on the superposition theorem of thermal circuits, the steady-state temperature matrix of the corresponding 16 temperature measurement points was determined. List several independent systems of equations and solve them. ， ， The aforementioned thermal superposition theorem is as follows:

[0011] in, This is the steady-state temperature matrix of 16 temperature measurement points. This is the matrix of thermal influence coefficients of the positive electrode tab. This is the matrix of thermal influence coefficients for the negative electrode ear. This is the matrix of thermal influence coefficients for battery cells.

[0012] Further, step S4 includes the following steps: S41. Run the finite element model and extract the temperature data at the corresponding locations on the finite element model. And combined with experimental temperature data Calculate separately , , Objective function corresponding to three parameters 、 、 ; S42, according to The results are used as the direction for optimization iteration of the parameters, while retaining the 10 smallest ones. corresponding , , Parameter combinations; 10 parameters will be retained after the set iteration limit is reached. , , Parameter combination calculation of its global error As shown in the following formula:

[0013] Among them, global error The sum of the squared differences between the finite element model and the experimental data for all 16 temperature measurement points. and The simulation outputs are respectively Temperature point temperature and experimentally measured Temperature point temperature at The numerical value at any given time; S43. The global error calculated in step S42 The smallest group ， ， The parameters are used as the final output and the finite element model is updated.

[0014] Further, the objective function described in step S4.1 , , The formulas are as follows:

[0015]

[0016] in, and The top and bottom temperatures output from the simulation are respectively... The average temperature at any given time and The temperatures measured in the experiment are the top and bottom temperatures, respectively. Average temperature at any given time; and These are the time constants of the temperature during the resting period output by simulation and the time constants of the temperature during the resting period measured by experiment, respectively. The time constant is the average value of the temperature time constants during the resting period obtained by fitting data from all temperature points. and The simulation output and the experimentally measured temperature measurement point 16 are respectively located at... The temperature of a moment.

[0017] Further, step S5 includes the following steps: S51. Run the finite element model and extract the temperature data at the corresponding locations on the finite element model. And combined with experimental temperature data Calculate separately , objective function and ; S52, according to and The result is used as optimization and The direction of parameter optimization iteration, while based on and The results obtained in each iteration are used to calculate the heat of the negative electrode ear. As shown in the following formula:

[0018] in, This is the open-circuit voltage of the battery. for The average value across 20 intervals.

[0019] S53. Output the final optimized parameters after the parameter iteration count reaches the set upper limit of the iteration count. , and And update the finite element model.

[0020] Furthermore, the objective function described in step S51 and The formulas are as follows:

[0021]

[0022] in, The starting time of the k-th segment is... The end time of the k-th segment. Thermal effect matrix of battery cell The The numerical value of each element, i.e., the temperature point. The thermal influence coefficient of the battery cell and The simulation outputs are respectively Temperature point temperature and experimentally measured Temperature point temperature at The numerical value at any given time; The thermal influence matrix of the positive electrode ear The The numerical value of each element, i.e., the temperature point. The thermal influence coefficient of the positive electrode ear.

[0023] Furthermore, the calculation of the entropy coefficient in step S7 is specifically as follows:

[0024] in, This represents the average temperature during operation.

[0025] The advantages of this invention are: (1) The present invention uses temperature distribution data of constant temperature chamber charging and discharging conditions. The test duration is less than 24 hours, which is much shorter than the several weeks required by the traditional potential method, greatly improving the efficiency of determining the entropy coefficient of large capacity energy storage batteries.

[0026] (2) This invention only requires a constant temperature chamber and related temperature acquisition equipment for testing, unlike calorimetry which requires specialized calorimetric equipment such as an adiabatic calorimeter (ARC). At the same time, it does not cause irreversible damage to the target battery, reducing the cost of measuring the entropy coefficient of large-capacity energy storage batteries.

[0027] (3) The present invention uses a three-dimensional finite element model as the optimization object to more accurately fit the actual thermal behavior of the battery, thereby ensuring the accuracy of the entropy coefficient fitting. Attached Figure Description

[0028] Figure 1 This is a schematic flowchart of a method for determining the entropy coefficient of an energy storage battery according to an embodiment of the present invention. Figure 2 This is a schematic diagram showing the locations of 16 temperature points in the target battery according to an embodiment of the present invention; Figure 3This is a schematic diagram comparing the fitting results of the embodiments of the present invention with the experimental results of ARC. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example 1 This embodiment provides a method for determining the entropy coefficient of an energy storage battery. Based on temperature distribution data under constant temperature chamber charging and discharging conditions, a particle swarm optimization algorithm framework for joint estimation of multiple thermal parameters is constructed. The evaluation indicators for optimizing each parameter are clarified, enabling simultaneous fitting of multiple thermal parameters of the battery. The specific implementation process is as follows: Figure 1 As shown, it includes the following steps: S1. Based on the size and material of the target battery, a finite element model is constructed in COMSOL simulation software. This model accurately solves the three-dimensional heat transfer control equations of the battery system using numerical methods, and can predict the transient and steady-state temperature field distribution inside the battery and the battery as a whole under different operating conditions and thermal management strategies. The finite element model is specifically as follows:

[0031] in, For simulation Temperature distribution at time, where The coordinates of the temperature point; Representative finite element numerical solution process; It is a set of key thermal parameters, including but not limited to: thermal conductivity, specific heat capacity, and density of each component; It is the heat generated by the battery. This refers to the battery's operating conditions, including its terminal voltage, charge level, and charge / discharge rate. These are the battery's heat generation parameters, including entropy coefficient, internal resistance, and average body temperature. It is a set of boundary conditions, including but not limited to: ambient convective heat transfer coefficient and ambient temperature; It refers to the geometric configuration of the battery system, including the geometry of each component and the contact configuration. This is the temperature distribution at the initial moment of the simulation; This refers to the runtime.

[0032] In this embodiment, the battery dimensions required to construct the COMSOL finite element thermal model include: the overall height, length, and width of the battery; the height, length, and width of the battery cell; the position and radius of the tabs; and the thickness of the outer casing.

[0033] The material information required to construct the COMSOL finite element thermal model includes: the thermal conductivity, specific heat capacity, and density of the tabs and casing; and the specific heat capacity and density of the battery. The algorithm optimizes the equivalent thermal model of the target battery. In this embodiment, a three-dimensional finite element thermal model constructed in COMSOL is used as the optimization object. The accuracy of parameter fitting largely depends on the accuracy of the finite element model construction. The model includes the heat generation and distribution of the main heat sources, the thermal conductivity of the main components, specific heat capacity settings, and geometric structure construction. This includes: (1) Dimensional parameters and corresponding materials of various battery components: The required dimensions include the overall height, length, and width of the battery, the thickness of the casing, the radius of the tabs, and the height, length, and width of the cells; the required materials include the materials of the tabs and the casing. Using the above dimensional and material parameters, a preliminary finite element thermal model of the target battery can be constructed in COMSOL. The above parameters are generally specified in the manufacturer's instruction manual.

[0034] (2) Specific heat capacity and density of the battery: Given the battery size, the overall heat capacity of the battery can be calculated based on its specific heat capacity and density. Some of the above parameters can be provided directly by the manufacturer; if not, they can be obtained through weighing and specific heat capacity experiments.

[0035] (3) Open circuit voltage of the battery ( The open-circuit voltage of a battery refers to the voltage between its two terminals when the battery has been left undisturbed for an extended period without current flowing through it (i.e., in an open-circuit state). The open-circuit voltage can be used to calculate the irreversible heat generated inside the battery and the heat generated by the tabs, based on the load voltage and current during battery operation.

[0036] S2. Conduct a complete constant current charge-discharge experiment on the target battery in a constant temperature chamber (ambient temperature set to 25°C), and record the battery load voltage at a sampling frequency of 1Hz. Current Temperature data from 16 temperature measurement points Initialize the parameters of the finite element model, including the cell heat. Positive electrode heat Negative electrode ear heat Thermal conductivity of the battery cell Large convective heat transfer coefficient and bottom convective heat transfer coefficient 2; among which, The values ​​are divided into segments based on the increase or decrease of 5% SOC, totaling 20 values; The complete battery constant current charge-discharge experiment described herein is as follows: First, place the target battery in a constant temperature chamber, charge it to the cutoff voltage, and let it stand for 4 hours; then, discharge it at a constant current of 1C to the cutoff voltage and let it stand for 3.5 hours; finally, recharge it at a constant current of 1C to the cutoff voltage and let it stand for 3.5 hours.

[0037] The 16 temperature points mentioned above, such as Figure 2 As shown, specifically: Define the large surface of the battery with the positive tab on the left and the negative tab on the right as surface A; define the side surface with the negative tab as surface B; define the large surface of the battery with the positive tab on the right and the negative tab on the left as surface C; define the side surface with the positive tab as surface D; define the top surface as surface E; define the bottom surface as surface F. Among them, temperature measuring point 1 is located at the upper left corner of surface A, near the positive electrode tab; temperature measuring point 2 is located at the upper right corner of surface A, near the negative electrode tab; temperature measuring point 3 is located at the center of surface A; temperature measuring point 4 is located at the lower left corner of surface A; temperature measuring point 5 is located at the lower right corner of surface A; temperature measuring point 6 is located at the top of surface B; temperature measuring point 7 is located at the bottom of surface B; temperature measuring point 8 is located at the upper left corner of surface C, near the negative electrode tab; temperature measuring point 9 is located at the upper right corner of surface C, near the positive electrode tab; temperature measuring point 10 is located at the center of surface C; temperature measuring point 11 is located at the lower left corner of surface C; temperature measuring point 12 is located at the lower right corner of surface C; temperature measuring point 13 is located at the top of surface D; temperature measuring point 14 is located at the bottom of surface D; temperature measuring point 15 is located at the center of surface E; and temperature measuring point 16 is located at the center of surface F.

[0038] S3. Solve the heat influence coefficient matrix of the positive electrode tab under steady state based on the finite element model. Negative electrode ear heat influence coefficient matrix And the cell heat effect coefficient matrix The specific implementation method is as follows: By sequentially changing the finite element model 、 、 Based on the superposition theorem of thermal circuits, the steady-state temperature matrix of the corresponding 16 temperature measurement points was determined. List several independent systems of equations and solve them. ， ， The aforementioned thermal superposition theorem is as follows:

[0039] in, This is the steady-state temperature matrix of 16 temperature measurement points. This is the matrix of thermal influence coefficients of the positive electrode tab. This is the matrix of thermal influence coefficients for the negative electrode ear. This is the matrix of thermal influence coefficients for battery cells.

[0040] After completing the above steps, this embodiment employs a step-by-step optimization strategy for thermal conductivity and heat parameters. Only through fitting these parameters can the thermal model better reflect the actual target battery, thereby obtaining a more realistic parameter fitting result for the heat parameters. The spatial scale optimization process completes the optimization of the cell's thermal conductivity (…). ), the convective heat transfer coefficient between the large surface area and the external environment ( ) and the convective heat transfer coefficient between the bottom surface and the external environment ( Joint optimization of parameters; time-scale optimization process to complete the optimization of cell heat ( ) and positive electrode ear heat ( ) and negative electrode ear heat ( The optimization iterations are performed to ensure accurate characterization of the heat generation process over time. Simultaneously, after completing one round of optimization, the finite element model parameters are updated using the latest parameter fitting results, thereby ensuring the accuracy of subsequent optimization processes. The specific algorithm is as follows: S4. Based on the experimental data obtained in step S2, the thermal conductivity coefficient of the battery cell is fitted using a multi-objective particle swarm optimization algorithm. Large convective heat transfer coefficient and bottom convective heat transfer coefficient Update the finite element model parameters; the specific implementation includes the following steps: S41. Run the finite element model and extract the temperature data at the corresponding locations on the finite element model. And combined with experimental temperature data Calculate separately ， ， Objective function corresponding to three parameters 、 、 The objective function 、 、 The formulas are as follows:

[0041]

[0042] in, and The top and bottom temperatures output from the simulation are respectively... The average temperature at any given time and The temperatures measured in the experiment are the top and bottom temperatures, respectively. The average temperature at any given time; the top temperature point is the temperature point at the top of surface A and surface C, that is, the average temperature of temperature measuring points 1, 2, 8, and 9; the bottom temperature is the temperature point at the top of surface A and surface C, that is, the average temperature of temperature measuring points 4, 5, 11, and 12. The smaller the value, the lower the thermal conductivity of the battery cell. The better the fitting result.

[0043] and These are the time constants of the temperature during the resting period output by simulation and the time constants of the temperature during the resting period measured by experiment, respectively. The time constant is the average value of the temperature time constants during the resting period obtained by fitting data from all temperature points. The smaller the value, the higher the convective heat transfer coefficient. The better the fitting result, the better the convective heat transfer coefficient. The impact on battery temperature is most pronounced during the battery's resting period. This design selects a time constant for the battery's temperature during the resting period. As The objective function is used to construct the standard. The temperature during the battery's resting period can be obtained by curve fitting using the following formula to obtain its time constant.

[0044]

[0045] In the above formula, for time instantaneous temperature at temperature point This is the start time of the resting period. That is, the time constant of the curve.

[0046] and The simulation output and the experimentally measured temperature measurement point 16 are respectively located at... The temperature at that moment. Due to the bottom convective heat transfer coefficient. The change in temperature has the greatest impact on the bottom surface temperature; therefore, temperature measurement point 16 on the bottom surface was selected as the temperature reference point. and The simulation output and the experimentally measured temperature measurement point 16 are respectively located at... The temperature of a moment. The smaller the value, the higher the bottom convective heat transfer coefficient. The better the fitting result.

[0047] S42, according to The results are used as the direction for optimization iteration of the parameters, while retaining the 10 smallest ones. corresponding Parameter combinations; 10 parameters will be retained after the set iteration limit is reached. Parameter combination calculation of its global error As shown in the following formula:

[0048] Among them, global error The sum of the squared differences between the finite element model and the experimental data for all 16 temperature measurement points. and The simulation outputs are respectively Temperature point temperature and experimentally measured Temperature point temperature at The numerical value at any given time; S43. The global error calculated in step S42 The smallest group The parameters are used as the final output and the finite element model is updated. The smaller, the more... ， ， The better the overall fit result.

[0049] S5, based on the optimized , and Based on the experimental data obtained in step S2, the heat of the battery cell is fitted using a time-series particle swarm optimization algorithm. Positive electrode heat and negative electrode ear heat And update the finite element model parameters; the specific implementation includes the following steps: S51. Run the finite element model and extract the temperature data at the corresponding locations on the finite element model. And combined with experimental temperature data Calculate separately , objective function and The objective function and The formulas are as follows:

[0050]

[0051] in, The starting time of the k-th segment is... The end time of the k-th segment. Thermal effect matrix of battery cell The The numerical value of each element, i.e., the temperature point. The thermal influence coefficient of the battery cell and The simulation outputs are respectively Temperature point temperature and experimentally measured Temperature point temperature at The numerical value at any given time; The thermal influence matrix of the positive electrode ear The The numerical value of each element, i.e., the temperature point. The thermal influence coefficient of the positive electrode ear.

[0052] S52, according to and The result is used as optimization and The direction of parameter optimization iteration, negative electrode heat. It can be combined with the load voltage during the charging and discharging process of the battery cell. Open circuit voltage Operating current and the heat of the battery cell obtained by fitting The calculation shows that it does not need to be used as a separate optimization variable; therefore, based on... and The results obtained in each iteration are used to calculate the heat of the negative electrode ear. As shown in the following formula:

[0053] S53. Output the final optimized parameters after the parameter iteration count reaches the set upper limit of the iteration count. , and And update the finite element model; The smaller, the more... The better the fitting result; at the same time, The smaller, the more... The better the fitting result.

[0054] Due to parameters The parameters are divided into 20 segments based on a 5% increase or decrease in SOC. Therefore, the above steps need to be repeated for each of the 20 capacity segments to complete the parameter calculation. Fitting.

[0055] in, This is the open-circuit voltage of the battery. for The average value across 20 intervals.

[0056] S6. Repeat steps S3-S5 until the number of iterations reaches the preset limit. S7, Output cell heat Positive electrode heat and negative electrode ear heat The entropy coefficient is calculated based on experimental data. The specific formula is as follows:

[0057] in, This represents the average temperature during operation.

[0058] In this embodiment, the final entropy coefficient is obtained The entropy coefficient results were compared with those obtained using an adiabatic reaction calorimeter (ARC). The ARC experiment measures the heat generated by the battery by maintaining the ambient temperature and battery temperature at a constant level, thus obtaining an accurate entropy coefficient. While this method is highly accurate, it requires specialized equipment and can cause irreversible damage to the tested battery.

[0059] like Figure 3 As shown, the entropy coefficient obtained in this embodiment has good accuracy. Therefore, the method provided in this embodiment ensures the accuracy of battery entropy coefficient measurement, eliminates the need for high-precision instruments during the measurement process, reduces costs, and does not cause irreversible damage to the target battery. Furthermore, compared to the traditional potentiometric method which requires several weeks, it significantly improves measurement efficiency.

[0060] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the entropy coefficient of an energy storage battery, characterized in that, Includes the following steps: S1. Construct a finite element model in COMSOL simulation software based on the size and material of the target battery; S2. Conduct a complete constant current charge-discharge experiment on the target battery in a constant temperature chamber, and record the battery load voltage at a sampling frequency of 1Hz. Current Temperature data from 16 temperature measurement points Initialize the parameters of the finite element model, including the cell heat. Positive electrode heat Negative electrode ear heat Cell thermal conductivity Large convective heat transfer coefficient and bottom convective heat transfer coefficient 2; among which, The values ​​are divided into segments based on the increase or decrease of 5% in SOC, totaling 20 values; S3. Solve the heat influence coefficient matrix of the positive electrode tab under steady state based on the finite element model. Negative electrode ear heat influence coefficient matrix And the cell heat effect coefficient matrix ; S4. Based on the experimental data obtained in step S2, the thermal conductivity coefficient of the battery cell is fitted using a multi-objective particle swarm optimization algorithm. Large convective heat transfer coefficient and bottom convective heat transfer coefficient Update the finite element model parameters; S5, based on the optimized , and Based on the experimental data obtained in step S2, the heat of the battery cell is fitted using a time-series particle swarm optimization algorithm. Positive electrode heat and negative electrode ear heat And update the finite element model parameters; S6. Repeat steps S3-S5 until the number of iterations reaches the preset limit. S7, Output cell heat Positive electrode heat and negative electrode ear heat The entropy coefficient is calculated by combining experimental data.

2. The method for determining the entropy coefficient of an energy storage battery according to claim 1, characterized in that, The finite element model described in step S1 is specifically as follows: in, For simulation Temperature distribution at time, where The coordinates of the temperature point; Representative finite element numerical solution process; It is a set of key thermal parameters. It is the heat generated by the battery. This refers to the battery's operating conditions. These are the heat generation parameters of the battery. It is a set of boundary conditions. It is the geometric configuration of the battery system. This is the temperature distribution at the initial moment of the simulation; This refers to the runtime.

3. The method for determining the entropy coefficient of an energy storage battery according to claim 1, characterized in that, A complete round of constant current charge-discharge experiment of the battery described in step S2 is specifically as follows: First, place the target battery in a constant temperature chamber, charge it to the cutoff voltage, and let it stand for 4 hours; then, discharge it at a constant current of 1C to the cutoff voltage and let it stand for 3.5 hours; finally, recharge it at a constant current of 1C to the cutoff voltage and let it stand for 3.5 hours.

4. The method for determining the entropy coefficient of an energy storage battery according to claim 1, characterized in that, The 16 temperature points mentioned in step S2 are specifically as follows: Define the large surface of the battery with the positive tab on the left and the negative tab on the right as surface A; define the side surface with the negative tab as surface B; define the large surface of the battery with the positive tab on the right and the negative tab on the left as surface C; define the side surface with the positive tab as surface D; define the top surface as surface E; define the bottom surface as surface F. Among them, temperature measuring point 1 is located at the upper left corner of surface A, near the positive electrode tab; temperature measuring point 2 is located at the upper right corner of surface A, near the negative electrode tab; temperature measuring point 3 is located at the center of surface A; temperature measuring point 4 is located at the lower left corner of surface A; temperature measuring point 5 is located at the lower right corner of surface A; temperature measuring point 6 is located at the top of surface B; temperature measuring point 7 is located at the bottom of surface B; temperature measuring point 8 is located at the upper left corner of surface C, near the negative electrode tab; temperature measuring point 9 is located at the upper right corner of surface C, near the positive electrode tab; temperature measuring point 10 is located at the center of surface C; temperature measuring point 11 is located at the lower left corner of surface C; temperature measuring point 12 is located at the lower right corner of surface C; temperature measuring point 13 is located at the top of surface D; temperature measuring point 14 is located at the bottom of surface D; temperature measuring point 15 is located at the center of surface E; and temperature measuring point 16 is located at the center of surface F.

5. The method for determining the entropy coefficient of an energy storage battery according to claim 1, characterized in that, Step S3 specifically involves: By sequentially changing the finite element model 、 、 Based on the superposition theorem of thermal circuits, the steady-state temperature matrix of the corresponding 16 temperature measurement points was determined. List several independent systems of equations and solve them. ， ， The aforementioned thermal superposition theorem is as follows: in, This is the steady-state temperature matrix of 16 temperature measurement points. This is the matrix of thermal influence coefficients of the positive electrode tab. This is the matrix of heat influence coefficients for the negative electrode ear. This is the matrix of thermal influence coefficients of the battery cell.

6. The method for determining the entropy coefficient of an energy storage battery according to claim 1, characterized in that, Step S4 includes the following steps: S41. Run the finite element model and extract the temperature data at the corresponding locations on the finite element model. And combined with experimental temperature data Calculate separately , , Objective function corresponding to three parameters 、 、 ; S42, according to The results are used as the direction for optimization iteration of the parameters, while retaining the 10 smallest ones. corresponding , , Parameter combinations; 10 parameters will be retained after the set iteration limit is reached. , , Parameter combination calculation of its global error As shown in the following formula: Among them, global error The sum of the squared differences between the finite element model and the experimental data for all 16 temperature measurement points. and The simulation outputs are respectively Temperature point temperature and experimentally measured Temperature point temperature at The numerical value at any given time; S43. The global error calculated in step S42 The smallest group ， ， The parameters are used as the final output and the finite element model is updated.

7. The method for determining the entropy coefficient of an energy storage battery according to claim 1, characterized in that, The objective function described in step S41 , , The formulas are as follows: in, and The top and bottom temperatures output from the simulation are respectively... The average temperature at any given time and The temperatures measured in the experiment are the top and bottom temperatures, respectively. Average temperature at any given time; and These are the time constants of the temperature during the resting period output by simulation and the time constants of the temperature during the resting period measured by experiment, respectively. The time constant is the average value of the temperature time constants during the resting period obtained by fitting data from all temperature points. and The simulation output and the experimentally measured temperature measurement point 16 are respectively located at... The temperature of a moment.

8. The method for determining the entropy coefficient of an energy storage battery according to claim 1, characterized in that, Step S5 includes the following steps: S51. Run the finite element model and extract the temperature data at the corresponding locations on the finite element model. And combined with experimental temperature data Calculate separately , objective function and ; S52, according to and The result is used as optimization and The direction of parameter optimization iteration. Simultaneously based on and The results obtained in each iteration are used to calculate the heat of the negative electrode ear. As shown in the following formula: in, This is the open-circuit voltage of the battery. for The average value across 20 intervals. S53. Output the final optimized parameters after the parameter iteration count reaches the set upper limit of the iteration count. , and And update the finite element model.

9. The method for determining the entropy coefficient of an energy storage battery according to claim 8, characterized in that, The objective function described in step S51 and The formulas are as follows: in, The starting time of the k-th segment is... The end time of the k-th segment. Thermal effect matrix of battery cell The The numerical value of each element, i.e., the temperature point. The thermal influence coefficient of the battery cell and The simulation outputs are respectively Temperature point temperature and experimentally measured Temperature point temperature at The numerical value at any given time; The thermal influence matrix of the positive electrode ear The The numerical value of each element, i.e., the temperature point. The thermal influence coefficient of the positive electrode ear.

10. The method for determining the entropy coefficient of an energy storage battery according to claim 1, characterized in that, The calculation of the entropy coefficient in step S7 is specifically as follows: in, This represents the average temperature during operation.

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