Battery physical and chemical model parameter identification method and system based on different temperature impedances

Abstract

The invention belongs to but not limited to the technical field of new energy, particularly relates to a battery physical and chemical model parameter identification method and system based on different temperature impedances, aims to improve the accuracy of model simulation, considers contact resistance when establishing a one-dimensional P2D physical and chemical model of a battery, and simultaneously solves the problem that the model parameters are more and difficult to identify. In order to solve the problems that in the prior art, parameters with high sensitivity and medium sensitivity and meeting actual conditions are selected by adopting a parameter sensitivity analysis method, finally, the parameters selected under different working conditions are identified by adopting a grey wolf optimization algorithm, simulation EIS data are compared with actual measurement data, and the simulation accuracy of the model is influenced by excessive parameters. The result shows that the grey wolf optimization algorithm realizes accurate identification of the parameters of the one-dimensional P2D physical and chemical model of the battery. The method disclosed by the invention achieves remarkable results in the aspects of battery modeling and parameter identification.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of new energy technology, and in particular relates to a battery physical and chemical model parameter identification method and system based on different temperature impedances. Background Art

[0002] Lithium-ion batteries are currently the most widely used automotive energy storage batteries in the world, and their reaction mechanism directly affects the safety and performance of energy storage systems. It has become an important means to simulate the actual internal state of the battery through simulation models. At present, battery models can be divided into three categories: data-driven models, equivalent circuit models, and electrochemical models. Data-driven models ignore the physical and chemical reactions inside the battery and require a large amount of experimental data for training. Real data is difficult to obtain, resulting in high computational costs. Equivalent circuit models can use algorithms for parameter identification, but these parameters lack physical interpretability. Physics-based electrochemical models contain a large number of parameters with physical interpretability, which can reflect the changes in the internal mechanism of the battery. They are expected to help the development and evaluation of digital twin models of new lithium-ion batteries in the future, and become one of the main choices for battery modeling. For electrochemical models, many parameters cannot be directly measured or obtained through manufacturer channels. They can only be obtained through experiments or disassembly of batteries. This method is not only time-consuming and costly, but also not suitable for actual working conditions. Therefore, it brings great difficulties to the parameterization of electrochemical models. In order to solve the problem of many and complex parameters, researchers use a method that relies on sensitivity analysis to study parameter identification. This method not only reduces the time of parameter identification, but also improves the accuracy of model simulation results. In recent years, it has become the main popular method for identifying battery parameters using intelligent algorithms. This method is also called non-invasive or non-destructive method, but the methods currently used have certain shortcomings in accuracy.

[0003] In view of the above analysis, the technical problem that urgently needs to be solved in the prior art is that the currently used method of studying parameter identification by relying on sensitivity analysis has certain shortcomings in accuracy. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method and system for identifying parameters of a battery physical and chemical model based on different temperature impedances. Firstly, a one-dimensional P2D physical and chemical model of a battery is established using simulation software. In order to improve the accuracy of the model EIS data, the contact resistance is also taken into consideration, and the EIS data is output based on the model. By considering the battery EIS data at different temperatures and different SOC states, a sensitivity analysis is performed on the parameters of the battery model to determine the sensitivity of the parameters under different data combinations. Secondly, although metaheuristic algorithms have been used for parameter identification of electrochemical models, they are still limited to simple algorithms such as genetic algorithms and particle swarms, and these methods are not accurate enough when identifying parameters based on impedance data. In order to find other effective parameter identification methods, the present invention adopts the Grey Wolf Optimization Algorithm to identify the parameters in the one-dimensional P2D physical and chemical model of the battery.

[0005] The present invention is implemented as follows: a battery physical and chemical model parameter identification method based on different temperature impedances, comprising:

[0006] S1. Use simulation software to construct a one-dimensional P2D physical and chemical model of the battery, where the construction of the one-dimensional P2D physical and chemical model of the battery includes solid phase mass conservation, solid phase charge conservation, liquid phase mass conservation, liquid phase charge conservation, and electrochemical kinetic equations, while considering contact resistance, setting the parameters required by the model, and determining the value range of the parameters;

[0007] S2. Design 5 different working conditions, set the solution frequency of electrochemical impedance spectrum, battery initial SOC, temperature, etc., solve the one-dimensional P2D battery physicochemical model through simulation software, and obtain the measured EIS data of NCM811 21700 lithium-ion battery and the simulated EIS data of lithium-ion battery;

[0008] S3, parameter sensitivity analysis, using the local sensitivity analysis method to perform sensitivity analysis on the parameters in S1, firstly, select 6 values ​​in the parameter value range by discrete uniform distribution, use step S2 to obtain EIS data under specific conditions, and perform parameter sensitivity analysis on the obtained electrochemical impedance model;

[0009] S4. Classification of model parameter sensitivity: the parameter sensitivity is divided into three categories: high, medium and low according to the average sensitivity index; in order to reflect the sensitivity, comprehensive sensitivity is used for analysis;

[0010] S5. Construct an objective function based on the measured EIS data and simulated EIS data of NCM811 21700 lithium-ion battery; the objective function is as follows:

[0011]

[0012] Where x represents the parameters of the battery model to be identified, N fkis the frequency point number, R(Z) and I(Z) represent the real and imaginary parts of the measured EIS, and Expressed as the real and imaginary parts of the simulated EIS;

[0013] S6. Select the Gray Wolf optimization algorithm to identify the parameters of the battery physical and chemical model, identify the highly and medium sensitive parameters and important parameters of steps S3 and S4, obtain the parameters of the battery one-dimensional P2D physical and chemical model under different working conditions, and verify the parameters.

[0014] Furthermore, the parameters required for the one-dimensional P2D physicochemical model of the battery in step S1 can be divided into three categories, as follows:

[0015] The first category: geometric parameters, including

[0016] Category 2: Concentration parameters, including

[0017] Category III: Transport and kinetic parameters, including

[0018] Further, the five working conditions in step S2 are as follows:

[0019] The first operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 95%;

[0020] The second operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 50%;

[0021] The third operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 20%;

[0022] The fourth operating condition is that the battery ambient temperature is 25 degrees and the initial SOC = 95%;

[0023] The fifth operating condition is that the battery ambient temperature is 25 degrees and the initial SOC = 50%.

[0024] Further, step S3 specifically includes the following steps:

[0025] Keeping the battery model the same, by simulating EIS data at different temperatures and different SOC levels, the frequency range is divided into three frequency regions, namely: high, medium, and low, and the parameter sensitivity changes in each range are analyzed. The parameter sensitivity of the electrochemical physicochemical model under different conditions is calculated as follows:

[0026]

[0027] Among them, SI R and SI I Represents the real and imaginary SI of different temperatures and SOC states, T represents temperature, Ns =6 is the number of values ​​each parameter can take within the range, and They represent the parameters V i The average values ​​of the real part R(Z) and the imaginary part I(Z) are obtained by performing 6 simulations within the range of values.

[0028] EIS has different physical meanings in different frequency ranges, and the sensitivity of parameters may change at different temperatures. Therefore, the average sensitivity of parameters in three frequency ranges and temperatures is analyzed. The average sensitivity index of each frequency range is as follows:

[0029]

[0030] ASI R and ASI I represent the average sensitivity index of the real and imaginary parts of the EIS data at different temperatures, SOCs and frequency ranges, respectively, and It represents the number of impedance data points within different frequency ranges.

[0031] Furthermore, the comprehensive sensitivity analysis formula in S4 is as follows:

[0032]

[0033] The subscripts V and T represent the parameter and temperature, respectively, and the superscripts R and I represent the real and imaginary parts of EIS.

[0034] Further, step S6 is as follows:

[0035] Set the wolf pack size and maximum number of iterations of the gray wolf optimization algorithm, and generate the initial battery one-dimensional P2D physical and chemical model parameters by initializing the position of the gray wolf population;

[0036] Simulate the EIS data of a specific frequency, calculate the fitness of each search agent, and increase the number of program iterations by 1;

[0037] If the maximum number of iterations is not reached, the wolf pack position is updated to continue parameter identification. If the maximum number of iterations is reached, the final parameters are output, and the final parameter group is set to the model for EIS simulation. The RMSE and MAE of the simulated EIS data are calculated to verify the accuracy of the parameters.

[0038] Another object of the present invention is to provide a battery physical and chemical model parameter identification system based on different temperature impedances for realizing the battery physical and chemical model parameter identification method based on different temperature impedances, comprising:

[0039] The battery one-dimensional P2D physical and chemical model construction module is used to construct a battery one-dimensional P2D physical and chemical model using simulation software. The construction of the battery one-dimensional P2D physical and chemical model includes solid phase mass conservation, solid phase charge conservation, liquid phase mass conservation, liquid phase charge conservation, and electrochemical kinetic equations. At the same time, the contact resistance is considered, the parameters required for the model are set, and the range of parameter values ​​is determined;

[0040] The simulation EIS data acquisition module is used to design 5 different working conditions, set the solution frequency of the electrochemical impedance spectrum, the initial SOC of the battery, the temperature, etc., solve the one-dimensional P2D battery physicochemical model through the simulation software, and obtain the measured EIS data of the NCM8121700 lithium-ion battery and the simulated EIS data of the lithium-ion battery;

[0041] The parameter sensitivity analysis module is used to perform sensitivity analysis on the parameters in S1 using a local sensitivity analysis method. First, 6 values ​​are selected in a discrete uniform distribution within the parameter value range. Step S2 is used to obtain EIS data under a specific condition, and the obtained electrochemical impedance model is subjected to parameter sensitivity analysis.

[0042] Model parameter sensitivity classification module is used to classify parameter sensitivity into three categories: high, medium and low according to the average sensitivity index; in order to reflect the size of sensitivity, comprehensive sensitivity is used for analysis;

[0043] The objective function construction module constructs the objective function based on the measured EIS data and simulated EIS data of NCM811 21700 lithium-ion battery;

[0044] The parameter acquisition module of the physical and chemical model is used to select the Gray Wolf optimization algorithm to identify the parameters of the battery physical and chemical model, identify the highly and medium sensitive parameters and important parameters, obtain the parameters of the battery one-dimensional P2D physical and chemical model under different working conditions, and verify the parameters.

[0045] Another object of the present invention is to provide a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the battery physical and chemical model parameter identification method based on different temperature impedances.

[0046] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the battery physical and chemical model parameter identification method based on different temperature impedances.

[0047] Another object of the present invention is to provide an information data processing terminal, which includes the battery physical and chemical model parameter identification system based on different temperature impedances.

[0048] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0049] First, the present invention first uses simulation software to establish a one-dimensional P2D physical and chemical model of a battery. In order to improve the accuracy of the model EIS data, the contact resistance is also taken into consideration, and the EIS data is output based on the model; a sensitivity analysis of the parameters of the battery model is performed by considering the battery EIS data at different temperatures and different SOC states to determine the sensitivity of the parameters under different data combinations; secondly, although metaheuristic algorithms have been used for parameter identification of electrochemical models, they are still limited to simple algorithms such as genetic algorithms and particle swarms, and these methods are not accurate enough when identifying parameters based on impedance data. In order to find other effective parameter identification methods, the present invention uses the Grey Wolf optimization algorithm to identify the parameters in the one-dimensional P2D physical and chemical model of the battery.

[0050] Second, the present invention solves the following key technical problems in the prior art through a battery physical and chemical model parameter identification method based on different temperature impedances:

[0051] Technical problems in the prior art

[0052] 1. Low identification accuracy of battery model parameters: In the prior art, there is a lack of accurate methods for identifying the physical and chemical model parameters of lithium-ion batteries. In particular, the impedance changes under different temperatures and operating conditions cannot be effectively identified, resulting in inaccurate prediction of battery performance.

[0053] 2. The battery management system has difficulty coping with complex operating conditions: Due to the lack of comprehensive analysis of battery impedance characteristics under multiple operating conditions, existing battery management systems have difficulty accurately judging the health status of batteries at high or low temperatures, which may lead to problems such as overcharging and discharging, thermal runaway, etc.

[0054] 3. Lack of parameter sensitivity analysis method: The existing technology is insufficient in parameter sensitivity analysis and cannot effectively distinguish the parameters that have a greater impact on battery performance, resulting in a lack of scientific basis in the process of model simplification or optimization.

[0055] Significant technical progress achieved by the invention in industrial applications

[0056] 1. Improved the identification accuracy of battery model parameters: By designing electrochemical impedance spectroscopy tests under different operating conditions and temperatures, and combining the Gray Wolf optimization algorithm, the key physical and chemical model parameters of lithium-ion batteries can be more accurately identified. This method greatly improves the accuracy of parameter identification, especially under complex temperature conditions, and realizes the classification and precise optimization of high, medium and low sensitive parameters.

[0057] 2. Enhanced reliability and adaptability of the battery management system: The present invention enables the battery management system to obtain battery model parameters in real time in actual applications, dynamically adjust management strategies according to different temperatures and working conditions, avoid overcharging or overdischarging of the battery, and significantly improve the reliability and safety of the battery system. For example, in electric vehicles and energy storage systems, this method significantly extends the service life of the battery and improves the efficiency of system operation.

[0058] 3. Realized the prediction and optimization of battery performance under complex working conditions: The present invention solves the technical problem of the existing technology that it is difficult to deal with the impact of different working conditions on battery performance through in-depth analysis of parameter sensitivity. Through this method, the electrochemical performance of the battery under different temperature conditions can be accurately predicted, so that the battery performance is significantly improved in the application of electric vehicles and energy storage systems, especially in high and low temperature extreme environments, ensuring the efficient and stable operation of the system.

[0059] 4. Improved technological advancement in industrial applications: By introducing the Gray Wolf optimization algorithm and comprehensive sensitivity analysis, the invention improves the intelligence and automation of battery impedance model parameter identification. This method not only reduces manual intervention and the number of experiments, but also reduces R&D and operating costs, bringing significant technological progress to battery management systems, energy storage systems and other fields.

[0060] The present invention effectively solves the problems of inaccurate battery model parameter identification and poor operating condition adaptability in the prior art through precise parameter identification methods and optimization strategies, bringing significant technological progress in the fields of electric vehicles and energy storage, and greatly improving the intelligence level of the battery management system and the overall performance of the battery system.

[0061] Third, the present invention solves several key problems in the prior art through the construction and parameter identification method of a one-dimensional P2D physical and chemical model of batteries based on different temperature impedances. First, in the prior art, the parameter identification accuracy of lithium-ion batteries is low, especially under different temperature and SOC conditions, the model is difficult to accurately reflect the complex physical and chemical processes inside the battery. The present invention constructs a one-dimensional P2D model, considers the mass of the solid and liquid phases, charge conservation and electrochemical kinetic equations, and introduces contact resistance, which further improves the accuracy and adaptability of the model, thereby solving the problem of lack of effective description of the real behavior of the battery during model construction.

[0062] Secondly, the present invention uses electrochemical impedance spectroscopy (EIS) data to design experimental conditions under different working conditions, such as the initial SOC of the battery and the ambient temperature, etc. The measured and simulated EIS data obtained by simulation software can more accurately reflect the response characteristics of the battery in actual work. This method significantly solves the problem of the difficulty in accurately identifying parameters under multiple working conditions in the prior art, allowing the model to maintain a high degree of accuracy under different environments and working conditions, and providing more reliable input parameters for the battery management system (BMS).

[0063] The present invention uses a local sensitivity analysis method to comprehensively classify the sensitivity of model parameters, and divides the parameters into three sensitivity levels: high, medium, and low. By analyzing the electrochemical impedance data at different frequency ranges and temperatures, the problem of the inability to effectively distinguish key parameters in the prior art is solved, providing a scientific basis for model simplification and optimization. At the same time, comprehensive sensitivity analysis further improves the robustness and adaptability of the model under different working conditions, making the parameter identification process more accurate and efficient.

[0064] Finally, the present invention uses the Gray Wolf optimization algorithm to identify the model parameters, focuses on optimizing the parameters with high and medium sensitivity, and obtains accurate parameters under different working conditions. This algorithm greatly improves the efficiency and accuracy of parameter identification, and solves the problem of cumbersome and time-consuming parameter optimization process in the prior art. Through this method, the model of lithium-ion batteries can not only accurately reflect the performance under different working conditions, but also verify the reliability of the model parameters, and improve the intelligence and automation level of battery management systems and energy storage systems in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flow chart of a battery physical and chemical model parameter identification method based on different temperature impedances provided by an embodiment of the present invention;

[0066] Figure 2 It is a structural diagram of a battery physical and chemical model parameter identification system based on different temperature impedances provided by an embodiment of the present invention;

[0067] Figure 3 It is a schematic diagram of the average sensitivity index of the real and imaginary parts of 25 parameters at different SOC levels and frequency ranges at 35 degrees provided by an embodiment of the present invention;

[0068] Figure 4 It is a schematic diagram of the average sensitivity index of the real and imaginary parts of 25 parameters at different SOC levels and frequency ranges at 25 degrees provided by an embodiment of the present invention;

[0069] Figure 5 5. The comparison diagram of measured EIS and simulated EIS under different working conditions provided by the embodiment of the present invention;

[0070] Figure 6 5. This is a comparison diagram of the real and imaginary part errors of the measured EIS and simulated EIS under five different working conditions provided by an embodiment of the present invention;

[0071] Figure 7 It is a graph of RMSE and MAE results of EIS simulation under five different working conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] The present invention first uses simulation software to establish a one-dimensional P2D physical and chemical model of a battery. In order to improve the accuracy of the model EIS data, the contact resistance is also taken into consideration, and the EIS data is output based on the model; the sensitivity analysis of the parameters of the battery model is performed by considering the battery EIS data at different temperatures and different SOC states to determine the sensitivity of the parameters under different data combinations; secondly, although metaheuristic algorithms have been used for parameter identification of electrochemical models, they are still limited to simple algorithms such as genetic algorithms and particle swarms, and these methods are not accurate enough when identifying parameters based on impedance data. In order to find other effective parameter identification methods, the present invention uses the Grey Wolf Optimization Algorithm to identify the parameters in the one-dimensional P2D physical and chemical model of the battery.

[0074] The present invention implements a battery physical and chemical model parameter identification method based on different temperature impedances through the following technical scheme, comprising the following steps:

[0075] S1. Use simulation software to construct a one-dimensional P2D physical and chemical model of the battery, where the construction of the one-dimensional P2D physical and chemical model of the battery includes solid phase mass conservation, solid phase charge conservation, liquid phase mass conservation, liquid phase charge conservation, and electrochemical kinetic equations, while considering contact resistance, setting the parameters required by the model, and determining the value range of the parameters;

[0076] The parameters required for the one-dimensional P2D physicochemical model of the battery in step S1 can be divided into three categories, as follows:

[0077] The first category: geometric parameters, including

[0078] Category 2: Concentration parameters, including

[0079] Category III: Transport and kinetic parameters, including

[0080] S2. Design 5 different working conditions, set the solution frequency of electrochemical impedance spectrum, battery initial SOC, temperature, etc., solve the one-dimensional P2D battery physicochemical model through simulation software, and obtain the measured EIS data of NCM811 21700 lithium-ion battery and the simulated EIS data of lithium-ion battery;

[0081] The five working conditions in step S2 are as follows:

[0082] The first operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 95%;

[0083] The second operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 50%;

[0084] The third operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 20%;

[0085] The fourth operating condition is that the battery ambient temperature is 25 degrees and the initial SOC = 95%;

[0086] The fifth operating condition is that the battery ambient temperature is 25 degrees and the initial SOC = 50%;

[0087] S3, parameter sensitivity analysis, using the local sensitivity analysis method to perform sensitivity analysis on the parameters in S1, firstly, select 6 values ​​in the parameter value range by discrete uniform distribution, use step S2 to obtain EIS data under specific conditions, and perform parameter sensitivity analysis on the obtained electrochemical impedance model;

[0088] Step S3 specifically includes the following steps:

[0089] Keeping the battery model the same, by simulating EIS data at different temperatures and different SOC levels, the frequency range is divided into three frequency regions, namely: high, medium, and low, and the parameter sensitivity changes in each range are analyzed. The parameter sensitivity of the electrochemical physicochemical model under different conditions is calculated as follows:

[0090]

[0091] Among them, SI R and SI I Represents the real and imaginary SI of different temperatures and SOC states, T represents the temperature N s =6 is the number of values ​​each parameter can take within the range, and They represent the parameters V i The average values ​​of the real part R(Z) and the imaginary part I(Z) are obtained by performing 6 simulations within the range of values.

[0092] EIS has different physical meanings in different frequency ranges, and the sensitivity of parameters may change at different temperatures. Therefore, the average sensitivity of parameters in three frequency ranges and temperatures is analyzed. The average sensitivity index of each frequency range is as follows:

[0093]

[0094] ASI R and ASI I represents the average sensitivity index of the real and imaginary parts of the EIS data at different temperatures, SOCs and frequency ranges, and N fk It represents the number of impedance data points within different frequency ranges.

[0095] S4. Classification of model parameter sensitivity: According to the average sensitivity index, the parameter sensitivity is divided into three categories: high, medium and low. In order to reflect the sensitivity, comprehensive sensitivity is used for analysis. The comprehensive sensitivity analysis formula is as follows:

[0096]

[0097] The subscripts V and T represent the parameter and temperature, respectively, and the superscripts R and I represent the real and imaginary parts of EIS.

[0098] S5. Construct an objective function based on the measured EIS data and simulated EIS data of NCM811 21700 lithium-ion battery; the objective function is as follows:

[0099]

[0100] Where x represents the parameters of the battery model to be identified, N fk is the frequency point number, R(Z) and I(Z) represent the real and imaginary parts of the measured EIS, and Expressed as the real and imaginary parts of the simulated EIS.

[0101] S6. Select the Gray Wolf optimization algorithm to identify the parameters of the battery physical and chemical model, identify the highly and medium sensitive parameters and important parameters of steps S3 and S4, obtain the parameters of the battery one-dimensional P2D physical and chemical model under different working conditions, and verify the parameters.

[0102] The S6 step is as follows:

[0103] Set the wolf pack size and maximum number of iterations of the gray wolf optimization algorithm, and generate the initial battery one-dimensional P2D physical and chemical model parameters by initializing the position of the gray wolf population;

[0104] Simulate the EIS data of a specific frequency, calculate the fitness of each search agent, and increase the number of program iterations by 1;

[0105] If the maximum number of iterations is not reached, the wolf pack position is updated to continue parameter identification. If the maximum number of iterations is reached, the final parameters are output, and the final parameter group is set to the model for EIS simulation. The RMSE and MAE of the simulated EIS data are calculated to verify the accuracy of the parameters.

[0106] like Figure 2 As shown, the battery physical and chemical model parameter identification system based on different temperature impedances includes:

[0107] The battery one-dimensional P2D physical and chemical model construction module is used to construct a battery one-dimensional P2D physical and chemical model using simulation software. The construction of the battery one-dimensional P2D physical and chemical model includes solid phase mass conservation, solid phase charge conservation, liquid phase mass conservation, liquid phase charge conservation, and electrochemical kinetic equations. At the same time, the contact resistance is considered, the parameters required for the model are set, and the range of parameter values ​​is determined;

[0108] The simulation EIS data acquisition module is used to design 5 different working conditions, set the solution frequency of the electrochemical impedance spectrum, the initial SOC of the battery, the temperature, etc., solve the one-dimensional P2D battery physicochemical model through the simulation software, and obtain the measured EIS data of the NCM8121700 lithium-ion battery and the simulated EIS data of the lithium-ion battery;

[0109] The parameter sensitivity analysis module is used to perform sensitivity analysis on the parameters in S1 using a local sensitivity analysis method. First, 6 values ​​are selected in a discrete uniform distribution within the parameter value range. Step S2 is used to obtain EIS data under a specific condition, and the obtained electrochemical impedance model is subjected to parameter sensitivity analysis.

[0110] Model parameter sensitivity classification module is used to classify parameter sensitivity into three categories: high, medium and low according to the average sensitivity index; in order to reflect the size of sensitivity, comprehensive sensitivity is used for analysis;

[0111] The objective function construction module constructs the objective function based on the measured EIS data and simulated EIS data of NCM81121700 lithium-ion battery;

[0112] The parameter acquisition module of the physical and chemical model is used to select the Gray Wolf optimization algorithm to identify the parameters of the battery physical and chemical model, identify the highly and medium sensitive parameters and important parameters, obtain the parameters of the battery one-dimensional P2D physical and chemical model under different working conditions, and verify the parameters.

[0113] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a battery physical and chemical model parameter identification method based on different temperature impedances.

[0114] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a battery physical and chemical model parameter identification method based on different temperature impedances.

[0115] An application embodiment of the present invention provides an information data processing terminal, which includes a battery physical and chemical model parameter identification system based on different temperature impedances.

[0116] The working principle of the present invention is based on constructing and optimizing the one-dimensional P2D physicochemical model of the battery, and the identification and optimization of the battery model parameters are realized through the electrochemical impedance spectroscopy (EIS) data under various working conditions. First, in the stage of building the one-dimensional P2D physicochemical model of the battery, the internal physical and chemical processes of the battery are modeled by simulation software. This model includes the mass conservation and charge conservation equations in the solid and liquid phases, as well as the electrochemical kinetic equations, taking into account the influence of contact resistance. Model construction requires setting a series of parameters, and the range of parameter values ​​is based on the actual physical properties of the battery. The goal of this stage is to establish a basic model that reflects the complex internal processes of the battery for subsequent data matching and optimization.

[0117] Next, the system uses electrochemical impedance spectroscopy (EIS) technology to simulate the performance of the battery under different working conditions during the simulated EIS data acquisition phase. Specifically, the system generates simulated EIS data for a one-dimensional P2D model by designing five different working conditions, including the initial state of the battery (SOC), operating temperature, and solution frequency. These data reflect the behavior of the battery at different temperatures and impedances, and provide a reference for subsequent parameter identification. Comparison of simulated EIS data with measured data can help analyze the dynamic response of the battery under different working conditions.

[0118] Subsequently, in the parameter sensitivity analysis phase, the system uses a local sensitivity analysis method to perform sensitivity analysis on the battery model parameters. By selecting several discrete values ​​within the parameter range, the system can evaluate the degree of influence of each parameter on the EIS data under different operating conditions. Through this analysis, the parameters are divided into three categories of high, medium, and low sensitivity parameters, and the system also uses a comprehensive sensitivity index to further analyze the overall sensitivity of the parameters. The purpose of this step is to identify the parameters that have the greatest impact on battery performance so that these parameters can be adjusted first in subsequent optimization.

[0119] Finally, the system uses the Gray Wolf optimization algorithm to identify and optimize highly sensitive and medium-sensitive parameters during the model parameter identification and optimization stage. The algorithm combines the measured EIS data and simulated EIS data of the battery. By constructing an objective function, the system optimizes the battery parameters under different operating conditions to make the simulation results as close as possible to the actual measurement results. This parameter identification and optimization method can accurately determine the key parameters of the battery, such as conductivity, diffusion coefficient, etc., thereby improving the accuracy and reliability of the battery model under various operating conditions.

[0120] The following are two examples of battery physical and chemical model parameter identification methods based on different temperature impedances in industry:

[0121] Example 1: Optimization of Electric Vehicle Battery Management System (BMS)

[0122] In the field of electric vehicles, accurate battery state estimation is crucial to improving vehicle range and battery life. By adopting a battery physical and chemical model parameter identification method based on different temperature impedances, the battery management system (BMS) can more accurately evaluate the health state and available capacity of lithium-ion batteries. Using this method, the BMS can automatically identify and adjust model parameters under different temperature conditions to optimize battery performance and efficiency:

[0123] 1. Improved battery life of electric vehicles: By real-time monitoring and correction of battery impedance model parameters, BMS can optimize the battery discharge curve and improve battery life.

[0124] 2. Extend battery life: Identifying physical and chemical parameters at different temperatures helps the BMS take protective measures in high or low temperature environments of the battery, reducing the impact of overheating or overcooling on battery life.

[0125] Example 2: Health monitoring of lithium-ion batteries in energy storage systems

[0126] In renewable energy storage systems, the health status of lithium-ion batteries directly affects energy scheduling and system stability. The battery physical and chemical model parameter identification method based on different temperature impedances can be applied to battery health management (BHM) in energy storage systems, enabling the system to optimize the operating parameters of the battery pack according to different temperature and load conditions:

[0127] 1. Grid balance optimization: By identifying the battery impedance characteristics at different temperatures, the energy storage system can adjust the charging and discharging speed of the battery pack to ensure the balance and stable operation of the grid.

[0128] 2. Battery life prediction and preventive maintenance: Using the precise parameters obtained by this method, the system can detect battery performance degradation at an early stage and take maintenance measures in advance, thereby avoiding sudden failure of the battery pack and reducing maintenance costs.

[0129] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0130] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A battery physical and chemical model parameter identification method based on different temperature impedances, characterized in that: include: S1. Use simulation software to construct a one-dimensional P2D physical and chemical model of the battery, where the construction of the one-dimensional P2D physical and chemical model of the battery includes solid phase mass conservation, solid phase charge conservation, liquid phase mass conservation, liquid phase charge conservation, and electrochemical kinetic equations, while considering contact resistance, setting the parameters required by the model, and determining the value range of the parameters; S2. Design 5 different working conditions, set the solution frequency of electrochemical impedance spectrum, battery initial SOC, temperature, solve the one-dimensional P2D battery physicochemical model through simulation software, and obtain the measured EIS data of NCM81121700 lithium-ion battery and the simulated EIS data of lithium-ion battery; S3, parameter sensitivity analysis, using the local sensitivity analysis method to perform sensitivity analysis on the parameters in S1, firstly, select 6 values ​​in the parameter value range by discrete uniform distribution, use step S2 to obtain EIS data under specific conditions, and perform parameter sensitivity analysis on the obtained electrochemical impedance model; S4. Classification of model parameter sensitivity: the parameter sensitivity is divided into three categories: high, medium and low according to the average sensitivity index; in order to reflect the sensitivity, comprehensive sensitivity is used for analysis; S5. Construct an objective function based on the measured EIS data and simulated EIS data of NCM81121700 lithium-ion battery; the objective function is as follows: Where x represents the parameters of the battery model to be identified, N fk is the frequency point number, R(Z) and I(Z) represent the real and imaginary parts of the measured EIS, and Expressed as the real and imaginary parts of the simulated EIS; S6. Select the Gray Wolf optimization algorithm to identify the parameters of the battery physical and chemical model, identify the highly and medium sensitive parameters and important parameters of steps S3 and S4, obtain the parameters of the battery one-dimensional P2D physical and chemical model under different working conditions, and verify the parameters.

2. The battery physical and chemical model parameter identification method based on different temperature impedances according to claim 1, characterized in that: The parameters required for the one-dimensional P2D physicochemical model of the battery in step S1 can be divided into three categories, as follows: The first category: geometric parameters, including L + L - L s A + ε - r + r - Category 2: Concentration parameters, including D s + D s - b + b - b s σ + σ - κ e Category III: Transport and kinetic parameters, including K + K - cdl + cdl - cl0.

3. The battery physical and chemical model parameter identification method based on different temperature impedances according to claim 1, characterized in that: The five working conditions in step S2 are as follows: The first operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 95%; The second operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 50%; The third operating condition is that the battery ambient temperature is 35 degrees and the initial SOC = 20%; The fourth operating condition is that the battery ambient temperature is 25 degrees and the initial SOC = 95%; The fifth operating condition is that the battery ambient temperature is 25 degrees and the initial SOC = 50%.

4. The battery physical and chemical model parameter identification method based on different temperature impedances according to claim 1, characterized in that: Step S3 specifically includes the following steps: Keeping the battery model the same, by simulating EIS data at different temperatures and different SOC levels, the frequency range is divided into three frequency regions, namely: high, medium, and low, and the parameter sensitivity changes in each range are analyzed. The parameter sensitivity of the electrochemical physicochemical model under different conditions is calculated as follows: Among them, SI R and SI I Represents the real and imaginary SI of different temperatures and SOC states, T represents temperature, N s =6 is the number of values ​​each parameter can take within the range, and They represent the parameters V i The average values ​​of the real part R(Z) and the imaginary part I(Z) are simulated 6 times within the range of values; EIS has different physical meanings in different frequency ranges, and the sensitivity of parameters may change at different temperatures. Therefore, the average sensitivity of parameters in three frequency ranges and temperatures is analyzed. The average sensitivity index of each frequency range is as follows: ASI R and ASI I represent the average sensitivity index of the real and imaginary parts of the EIS data at different temperatures, SOCs and frequency ranges, respectively, and It represents the number of impedance data points within different frequency ranges.

5. The battery physical and chemical model parameter identification method based on different temperature impedances according to claim 1, characterized in that: The comprehensive sensitivity analysis formula in S4 is as follows: The subscripts V and T represent the parameter and temperature, respectively, and the superscripts R and I represent the real and imaginary parts of EIS.

6. The battery physical and chemical model parameter identification method based on different temperature impedances according to claim 1, characterized in that: The S6 step is as follows: Set the wolf pack size and maximum number of iterations of the gray wolf optimization algorithm, and generate the initial battery one-dimensional P2D physical and chemical model parameters by initializing the position of the gray wolf population; Simulate the EIS data of a specific frequency, calculate the fitness of each search agent, and increase the number of program iterations by 1; If the maximum number of iterations is not reached, the wolf pack position is updated to continue parameter identification. If the maximum number of iterations is reached, the final parameters are output, and the final parameter group is set to the model for EIS simulation. The RMSE and MAE of the simulated EIS data are calculated to verify the accuracy of the parameters.

7. A battery physical and chemical model parameter identification system based on different temperature impedances for implementing the battery physical and chemical model parameter identification method based on different temperature impedances as claimed in any one of claims 1 to 6, characterized in that: include: The battery one-dimensional P2D physical and chemical model construction module is used to construct a battery one-dimensional P2D physical and chemical model using simulation software. The construction of the battery one-dimensional P2D physical and chemical model includes solid phase mass conservation, solid phase charge conservation, liquid phase mass conservation, liquid phase charge conservation, and electrochemical kinetic equations. At the same time, the contact resistance is considered, the parameters required for the model are set, and the range of parameter values ​​is determined; The simulation EIS data acquisition module is used to design 5 different working conditions, set the solution frequency of the electrochemical impedance spectrum, the initial SOC of the battery, the temperature, etc., solve the one-dimensional P2D battery physicochemical model through the simulation software, and obtain the measured EIS data of the NCM8121700 lithium-ion battery and the simulated EIS data of the lithium-ion battery; The parameter sensitivity analysis module is used to perform sensitivity analysis on the parameters in S1 using a local sensitivity analysis method. First, 6 values ​​are selected in a discrete uniform distribution within the parameter value range. Step S2 is used to obtain EIS data under a specific condition, and the obtained electrochemical impedance model is subjected to parameter sensitivity analysis. Model parameter sensitivity classification module is used to classify parameter sensitivity into three categories: high, medium and low according to the average sensitivity index; in order to reflect the size of sensitivity, comprehensive sensitivity is used for analysis; The objective function construction module constructs the objective function based on the measured EIS data and simulated EIS data of NCM81121700 lithium-ion battery; The parameter acquisition module of the physical and chemical model is used to select the Gray Wolf optimization algorithm to identify the parameters of the battery physical and chemical model, identify the highly and medium sensitive parameters and important parameters, obtain the parameters of the battery one-dimensional P2D physical and chemical model under different working conditions, and verify the parameters.

8. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the battery physical and chemical model parameter identification method based on different temperature impedances as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the battery physicochemical model parameter identification method based on different temperature impedances as described in any one of claims 1 to 6.

10. An information data processing terminal, comprising the battery physical and chemical model parameter identification system based on different temperature impedances as claimed in claim 7.

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