Battery physicochemical model parameter identification method and system based on impedance at different temperatures

Abstract

The present application belongs to but is not limited to the field of new energy technology, and particularly relates to a battery physicochemical model parameter identification method and system based on different temperature impedances. In order to improve the accuracy of model simulation, the contact resistance is considered when establishing the one-dimensional P2D physicochemical model of the battery. In order to cope with the problems of too many model parameters being difficult to identify and too many parameters affecting the simulation accuracy of the model, the parameter sensitivity analysis method is used to select the parameters with high, medium sensitivity and meeting the actual situation. Finally, the grey wolf optimization algorithm is used to identify the parameters selected under different working conditions. The comparison of the simulation EIS data and the measured data shows that the grey wolf optimization algorithm realizes the accurate identification of the parameters of the one-dimensional P2D physicochemical model of the battery. The method of the present application has achieved remarkable results in battery modeling and parameter identification.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of new energy technology, and in particular relates to a battery physicochemical model parameter identification method and system based on different temperature impedances. BACKGROUND

[0002] Lithium ion battery is the most widely used vehicle energy storage battery in the world, and its reaction mechanism directly affects the safety and performance of the energy storage system. Simulating the actual state inside the battery through a simulation model has become an important means. Current 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. However, it is difficult to obtain real data, resulting in high computational cost. Equivalent circuit models can use algorithms for parameter identification, but these parameters lack physical interpretability. Electrochemical models based on physics contain a large number of parameters with physical interpretability and can reflect the changes in the internal mechanism of the battery. They are expected to help develop and evaluate digital twin models for future new lithium ion batteries and become one of the main choices for current battery modeling. For electrochemical models, many parameters cannot be directly measured or obtained through manufacturer channels. They can only be obtained through experiments or by disassembling the battery. This method is not only time-consuming and costly, but also not suitable for actual working conditions. Therefore, it has brought great difficulty to the parameterization of electrochemical models. In order to solve the problem of multiple 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, using intelligent algorithms to identify battery parameters has become a popular method. This method is also known as a non-invasive or non-destructive method. However, there are certain deficiencies in the accuracy of these methods currently used.

[0003] In view of the above analysis, the existing technical problems in the prior art that need to be solved urgently are that the method currently used for parameter identification relies on sensitivity analysis, which has certain deficiencies in accuracy. SUMMARY

[0004] In response to the problems existing in the prior art, the present invention provides a battery physical and chemical model parameter identification method and system based on different temperature impedances. First, 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's 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 battery model parameters 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 adopts the gray 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. 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. At the same time, contact resistance is considered, and the parameters required for the model are set and the parameter value range is determined;

[0007] S2. Design five different operating conditions, set the electrochemical impedance spectroscopy solution frequency, battery initial SOC, temperature, etc., solve the one-dimensional P2D battery physicochemical model through simulation software, and obtain the measured EIS data of the NCM811 21700 lithium-ion battery and the simulated EIS data of the 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 ​​with discrete uniform distribution within the parameter value range, use step S2 to obtain EIS data under specific conditions, and perform parameter sensitivity analysis on the obtained electrochemical impedance spectroscopy model;

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

[0010] S5. Construct an objective function based on the measured EIS data and simulated EIS data of the 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 number of frequency points, R(Z) and I(Z) represent the real and imaginary parts of the measured EIS, and represent the real and imaginary parts of the simulated EIS.

[0013] S6, select the grey wolf optimization algorithm to identify the physical and chemical model parameters of the battery, identify the parameters of high, medium and important sensitivity in steps S3 and S4, obtain the parameters of the one-dimensional P2D physical and chemical model of the battery under different working conditions, and verify the parameters.

[0014] Further, the parameters required by the one-dimensional P2D physical and chemical model of the battery in step S1 can be divided into three categories, as follows:

[0015] The first category: geometric parameters, including

[0016] The second category: concentration parameters, including

[0017] The third category: transport and kinetic parameters, including

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

[0019] The first working condition is that the battery environment temperature is 35 degrees, and the initial SOC is 95%;

[0020] The second working condition is that the battery environment temperature is 35 degrees, and the initial SOC is 50%;

[0021] The third working condition is that the battery environment temperature is 35 degrees, and the initial SOC is 20%;

[0022] The fourth working condition is that the battery environment temperature is 25 degrees, and the initial SOC is 95%;

[0023] The fifth working condition is that the battery environment temperature is 25 degrees, and the initial SOC is 50%.

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

[0025] Keep the battery model the same, simulate EIS data at different temperatures and different SOC levels, and at the same time divide the frequency range into three frequency regions, namely: high, medium and low, and analyze the parameter sensitivity changes in each range. The parameter sensitivity of the electrochemical physical and chemical model under different conditions is calculated as follows:

[0026]

[0027] wherein SI R and SI I represent the SI of the real and imaginary parts under different temperatures and SOC states, T represents the temperature, and 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] The physical meaning of EIS varies across different frequency ranges, and the sensitivity of parameters may change at different temperatures. Therefore, we analyzed the average sensitivity of the parameters across three frequency ranges and temperatures. The average sensitivity index for each frequency range is as follows:

[0029]

[0030] Among them 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 in 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 the EIS.

[0034] Furthermore, step S6 is specifically as follows:

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

[0036] Simulate 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 and parameter identification continues. 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 parameter accuracy.

[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 implementing 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 one-dimensional P2D battery 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. It also takes into account contact resistance, sets the parameters required for the model, and determines the parameter value range.

[0040] The simulation EIS data acquisition module is used to design five different operating conditions, set the electrochemical impedance spectrum solution frequency, battery initial SOC, temperature, etc., and solve the one-dimensional P2D battery physicochemical model through simulation software to 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 from the parameter range using a discrete uniform distribution. Then, using step S2, EIS data is obtained under specific circumstances. The obtained electrochemical impedance model is subjected to parameter sensitivity analysis.

[0042] The 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 the 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 the 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, which includes 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.

[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's EIS data, the contact resistance is also taken into account, 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 uses the gray 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 existing technologies

[0052] 1. Low identification accuracy of battery model parameters: In the existing technology, 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 battery performance predictions.

[0053] 2. Battery management systems have 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 battery health status 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 methods: Existing technologies are insufficient in parameter sensitivity analysis and cannot effectively distinguish parameters that have a greater impact on battery performance, resulting in a lack of scientific basis in the model simplification or optimization process.

[0055] Significant technological progress achieved by this invention in industrial applications

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

[0057] 2. Enhanced reliability and adaptability of the battery management system: This invention enables the battery management system to obtain battery model parameters in real time during practical applications, dynamically adjust management strategies based on different temperatures and operating conditions, and prevent battery overcharging or over-discharging, significantly improving the reliability and safety of the battery system. For example, in electric vehicles and energy storage systems, this method significantly extends battery life and improves system efficiency.

[0058] 3. Achieves prediction and optimization of battery performance under complex operating conditions: This invention addresses the existing technical challenge of addressing the impact of varying operating conditions on battery performance through in-depth analysis of parameter sensitivity. This method accurately predicts the electrochemical performance of batteries under varying temperature conditions, significantly improving battery performance in applications such as electric vehicles and energy storage systems. This ensures efficient and stable operation, particularly in extreme high and low temperature environments.

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

[0060] Through precise parameter identification methods and optimization strategies, the present invention effectively solves the problems of inaccurate battery model parameter identification and poor adaptability to working conditions in the prior art, 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 further improves the accuracy and adaptability of the model by constructing a one-dimensional P2D model, taking into account the mass, charge conservation and electrochemical kinetic equations of the solid and liquid phases, and introducing contact resistance, thereby solving the problem of lack of effective description of the real behavior of the battery during the model construction process.

[0062] Secondly, the present invention uses electrochemical impedance spectroscopy (EIS) data to design experimental conditions under different operating conditions, such as the battery's initial SOC and ambient temperature. The measured and simulated EIS data obtained through simulation software can more accurately reflect the battery's response characteristics in actual operation. This method significantly solves the problem of the existing technology that is difficult to accurately identify parameters under multiple operating conditions. It enables the model to maintain high accuracy in different environments and operating conditions, providing more reliable input parameters for the battery management system (BMS).

[0063] This paper uses a local sensitivity analysis method to comprehensively classify the sensitivity of model parameters, categorizing them into three sensitivity levels: high, medium, and low. By analyzing electrochemical impedance data across different frequency ranges and temperatures, this approach overcomes the existing problem of effectively distinguishing key parameters, providing a scientific basis for model simplification and optimization. Furthermore, comprehensive sensitivity analysis further enhances the robustness and adaptability of the model under different operating conditions, making the parameter identification process more accurate and efficient.

[0064] Finally, the present invention uses the Gray Wolf Optimization Algorithm to identify model parameters, focusing on optimizing highly and moderately sensitive parameters to obtain precise parameters under different operating conditions. This algorithm significantly improves the efficiency and accuracy of parameter identification, resolving the cumbersome and time-consuming parameter optimization process in existing technologies. Through this method, the lithium-ion battery model not only accurately reflects performance under different operating conditions but also verifies the reliability of the model parameters, enhancing the intelligence and automation level of battery management systems and energy storage systems in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This 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 1 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 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 25 parameters at different SOC levels and frequency ranges at 25 degrees Celsius, and the average sensitivity index of real and imaginary parts is shown in FIG.

[0069] Figure 5 This is a comparison chart of measured EIS and simulated EIS under five different working conditions provided by an embodiment of the present invention;

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

[0071] Figure 7 3. 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 solutions 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 intended 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's EIS data, contact resistance is also taken into account, and EIS data is output based on the model. A sensitivity analysis of the battery model parameters 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. Moreover, 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 Gray 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 solutions, including the following steps:

[0075] S1. Use simulation software to construct a one-dimensional P2D physical and chemical model of the battery. 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. At the same time, contact resistance is considered, and the parameters required for the model are set and the parameter value range is determined;

[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 five different operating conditions, set the electrochemical impedance spectroscopy solution frequency, battery initial SOC, temperature, etc., solve the one-dimensional P2D battery physicochemical model through simulation software, and obtain the measured EIS data of the NCM811 21700 lithium-ion battery and the simulated EIS data of the 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 is 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 ​​with discrete uniform distribution within the parameter value range, use step S2 to obtain EIS data under specific conditions, and perform parameter sensitivity analysis on the obtained electrochemical impedance spectroscopy 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: high, medium, and low. The parameter sensitivity changes in each range are analyzed. The parameter sensitivity of the electrochemical model under different conditions is calculated as follows:

[0090]

[0091] Among them, SI R and SI I Represents the real and imaginary SI at 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] The physical meaning of EIS varies across different frequency ranges, and the sensitivity of parameters may change at different temperatures. Therefore, we analyzed the average sensitivity of the parameters across three frequency ranges and temperatures. The average sensitivity index for each frequency range is as follows:

[0093]

[0094] Among them 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 N fk It represents the number of impedance data points in different frequency ranges.

[0095] S4. Classification of model parameter sensitivity: 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 analysis is used. 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 the EIS.

[0098] S5. Construct an objective function based on the measured EIS data and simulated EIS data of the 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 steps are as follows:

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

[0104] Simulate 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 and parameter identification continues. 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 parameter accuracy.

[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 one-dimensional P2D battery 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. It also takes into account contact resistance, sets the parameters required for the model, and determines the parameter value range.

[0108] The simulation EIS data acquisition module is used to design five different operating conditions, set the electrochemical impedance spectrum solution frequency, battery initial SOC, temperature, etc., and solve the one-dimensional P2D battery physicochemical model through simulation software to 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 from the parameter range using a discrete uniform distribution. Then, using step S2, EIS data is obtained under specific circumstances. The obtained electrochemical impedance model is subjected to parameter sensitivity analysis.

[0110] The 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 the 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 the construction and optimization of a one-dimensional P2D physical and chemical model of the battery, and the identification and optimization of the battery model parameters are achieved through electrochemical impedance spectroscopy (EIS) data under various working conditions. First, in the stage of building a one-dimensional P2D physical and chemical model of the battery, the internal physical and chemical processes of the battery are modeled using 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 operating 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 operating conditions, including the battery's initial state of charge (SOC), operating temperature, and solution frequency. These data reflect the battery's behavior 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 operating conditions.

[0118] Subsequently, during 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's range, the system can assess the impact of each parameter on the EIS data under different operating conditions. Through this analysis, the parameters are classified into three categories of high, medium, and low sensitivity. 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 during subsequent optimization.

[0119] Finally, during the model parameter identification and optimization phase, the system uses the Gray Wolf optimization algorithm to identify and optimize highly and moderately sensitive parameters. This algorithm combines the measured and simulated EIS data of the battery. By constructing an objective function, the system optimizes battery parameters under different operating conditions, ensuring that the simulated results are as close as possible to the actual measured results. This parameter identification and optimization method accurately determines key battery parameters, such as conductivity and diffusion coefficient, 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 industrial applications:

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

[0122] In the electric vehicle sector, accurate battery state estimation is crucial for improving vehicle range and battery life. By employing a battery physical and chemical model parameter identification method based on different temperature impedances, a battery management system (BMS) can more accurately assess the health status 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, 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 of lithium-ion batteries directly affects energy scheduling and system stability. A 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 impedance characteristics of batteries at different temperatures, the energy storage system can adjust the charge and discharge 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 failures 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 portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, 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. The devices and 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, 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, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0130] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection 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. 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. At the same time, contact resistance is considered, and the parameters required for the model are set and the parameter value range is determined; S2. Design five different operating conditions, set the electrochemical impedance spectroscopy solution frequency, battery initial SOC, and temperature, solve the one-dimensional P2D battery physicochemical model through simulation software, and obtain the measured EIS data and simulated EIS data of the NCM81121700 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 ​​with discrete uniform distribution within the parameter value range, use step S2 to obtain EIS data under specific conditions, and perform parameter sensitivity analysis on the obtained electrochemical impedance spectroscopy model; S4. Classification of model parameter sensitivity: Parameter sensitivity is divided into three categories: high, medium, and low according to the average sensitivity index. In order to reflect the magnitude of sensitivity, comprehensive sensitivity is used for analysis. S5. Construct an objective function based on the measured EIS data and simulated EIS data of the 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 + ε - r + r - Category 2: Concentration parameters, including D s + D s - b + b - b s σ + σ - κ e Category 3: 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 is 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: high, medium, and low. The parameter sensitivity changes in each range are analyzed. The parameter sensitivity of the electrochemical model under different conditions is calculated as follows: Among them, SI R and SI I Represents the real and imaginary SI at 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 value range; The physical meaning of EIS varies across different frequency ranges, and the sensitivity of parameters may change at different temperatures. Therefore, we analyzed the average sensitivity of the parameters across three frequency ranges and temperatures. The average sensitivity index for each frequency range is as follows: Among them 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 in 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 the 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 steps are as follows: Set the wolf population size and maximum number of iterations for the gray wolf optimization algorithm, and generate the initial battery one-dimensional P2D physical and chemical model parameters by initializing the positions of the gray wolf population; Simulate 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 and parameter identification continues. 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 parameter accuracy.

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 one-dimensional P2D battery 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. It also takes into account contact resistance, sets the parameters required for the model, and determines the parameter value range. The simulation EIS data acquisition module is used to design five different operating conditions, set the electrochemical impedance spectrum solution frequency, battery initial SOC, temperature, etc., and solve the one-dimensional P2D battery physicochemical model through simulation software to 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 from the parameter range using a discrete uniform distribution. Then, using step S2, EIS data is obtained under specific circumstances. The obtained electrochemical impedance model is subjected to parameter sensitivity analysis. The 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 the 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, wherein when the computer program is executed by a processor, the processor executes the steps of the battery physicochemical model parameter identification method based on different temperature impedances according to 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 according to claim 7.

Images