Electrochemical performance identification method, device and equipment based on battery charging and discharging process

Abstract

The invention relates to an electrochemical performance identification method, device and equipment based on a battery charging and discharging process, and the method comprises the steps: inputting a first battery signal through a data anchor point preset at a battery charging and discharging lug, and generating first battery feature data through the first battery signal; inputting a second battery signal through a Bragg grating sensor preset in the battery, and generating second battery characteristic data by using the second battery signal; inputting a third battery signal to the battery through an in-situ microscopy technology, and generating third battery characteristic data by using the third battery signal; performing data fitting on the first battery characteristic data, the second battery characteristic data and the third battery characteristic data through a model, performing battery simulation SOC calculation under a period on the battery characteristic data after data fitting, and calculating electrochemical performance data of the battery based on an SOC value range of SOC calculation; and rapid and accurate identification of various electrochemical properties of the battery is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium batteries, and in particular to a method, device and equipment for identifying electrochemical properties based on the battery charging and discharging process. Background Art

[0002] With the rapid development of lithium-ion battery technology, its application in electric vehicles, energy storage systems, consumer electronics and other fields is becoming more and more extensive. However, the electrochemical properties of lithium-ion batteries will change significantly during their charging and discharging process, and these changes directly affect the safety, service life and overall performance of the battery. Therefore, accurately identifying the electrochemical properties of the battery during the charging and discharging process is crucial for the design, manufacture, maintenance and performance optimization of the battery.

[0003] At present, some technologies have been used to identify the electrochemical properties of lithium-ion batteries, such as directly measuring the electrochemical parameters of the battery through electrochemical testing, or building a battery model for simulation prediction. However, these methods still have some limitations in practical applications. For example, although the traditional electrochemical testing method is accurate, it is time-consuming and costly, making it difficult to achieve real-time monitoring of battery performance. Existing battery models, such as the electrochemical-thermal-mechanical coupling model, can predict the electrochemical properties of the battery to a certain extent, but their complexity and computational cost are high, and they mainly predict changes in battery thickness and stress distribution within the electrode, and are still insufficient for the identification of other key electrochemical properties. Summary of the invention

[0004] The main purpose of the present invention is to provide a method, device and equipment for identifying electrochemical properties based on the battery charging and discharging process, by deeply analyzing the electrochemical behavior of the battery during the charging and discharging process, combined with advanced data processing technology and algorithms, to achieve rapid and accurate identification of multiple electrochemical properties of the battery.

[0005] To achieve the above object, the present invention provides a method for identifying electrochemical properties based on the battery charging and discharging process, comprising the following steps:

[0006] A first battery signal is inputted through a data anchor point preset at a battery charge and discharge level, and first battery characteristic data is generated using the first battery signal, wherein the first battery signal includes voltage, current and battery temperature distribution; at the same time,

[0007] The second battery signal is inputted by a Bragg grating sensor preset in the battery, and the second battery characteristic data is generated by using the second battery signal, wherein the second battery signal includes the internal stress of the battery; at the same time,

[0008] Inputting a third battery signal to the battery through in-situ microscopy technology, and generating third battery characteristic data using the third battery signal, wherein the third battery signal includes SEI film dynamic change information;

[0009] Calling the MBCF-DSEM model under the Matlab neural network simulation toolbox under the device to perform data fitting on the first battery characteristic data, the second battery characteristic data, and the third battery characteristic data;

[0010] A battery simulation SOC calculation is performed on the battery characteristic data after data fitting in one cycle, and the electrochemical performance data of the battery is inferred based on the SOC value range of the SOC calculation.

[0011] Furthermore, the first battery signal is inputted through a data anchor point preset at a battery charge and discharge stage, and the steps include:

[0012] A thermistor is preset at the charging and discharging stage of the battery as a data anchor point. The thermistor is connected in series with the current path of the battery, and its resistance value changes with the change of the battery temperature. The current information of the battery is collected in real time through the thermistor, because a voltage drop will be generated when the current passes through the thermistor, and the voltage drop is proportional to the current size. At the same time, the temperature information of the battery is calculated by measuring the voltage difference across the thermistor and combining the temperature-resistance characteristic curve of the thermistor. In addition, the voltage at the charging and discharging stage of the battery is directly measured to obtain the voltage information of the battery.

[0013] Furthermore, the second battery signal is inputted by a Bragg grating sensor preset in the battery, and the steps include:

[0014] At least one Bragg grating sensor is preset inside the battery. The sensor is composed of an optical fiber and a periodically arranged grating structure to sense the mechanical strain generated inside the battery due to the charging and discharging process.

[0015] When the battery is charged or discharged, the mechanical strain inside the battery causes the grating period of the Bragg grating sensor to change, thereby changing the wavelength of the light reflected or transmitted by the sensor;

[0016] The optical detection system monitors the wavelength change of the light reflected or transmitted by the Bragg grating sensor in real time, and converts the wavelength change into an electrical signal;

[0017] The converted electrical signal is analyzed and the stress distribution inside the battery is calculated based on the known relationship between the wavelength change of the Bragg grating sensor and the mechanical strain.

[0018] Furthermore, the third battery signal is input to the battery by in-situ microscopy technology, and the steps include:

[0019] During the battery charging and discharging process, the in-situ microscopy equipment is used to perform real-time, high-resolution microscopic observation of the battery surface or a specific area. The in-situ microscopy equipment captures the dynamic change image of the SEI film on the battery surface or in a specific area, including but not limited to the changes in the morphology, thickness, composition and distribution of the SEI film. The captured image data is processed and analyzed, and the extracted dynamic change characteristic parameters of the SEI film are used as the third battery signal.

[0020] Furthermore, the training method of the MBCF-DSEM model includes:

[0021] Acquire a training sample, wherein the training sample includes multiple sampled first battery characteristic data, second battery characteristic data, and third battery characteristic data;

[0022] Building a MBCF-DSEM model, the model structure includes an input layer, a feature fusion layer, a dynamic SEI film simulation layer, an output layer and a feedback mechanism layer, and importing the training samples into the output layer;

[0023] The MBCF-DSEM model is trained using supervised training, and the training goal is to minimize the difference between the model prediction value and the battery performance data.

[0024] The simulated battery data under the MBCF-DSEM model is tested, wherein the simulated battery data is the battery SOC prediction accuracy of data fitting.

[0025] Furthermore, the step of performing data fitting on the first battery characteristic data, the second battery characteristic data and the third battery characteristic data by the MBCF-DSEM model includes:

[0026] The first linear frame, the second linear frame and the third linear frame are respectively set to correspond to the linear changes of the battery characteristics in the first battery characteristic data, the second battery characteristic data and the third battery characteristic data respectively;

[0027] Generate battery data fitting wave line based on linearly changing fluctuation frequency and amplitude;

[0028] The digitized battery characteristic data is obtained by fitting the wave line of the battery data.

[0029] Further, the step of performing a battery simulation SOC calculation for one cycle on the battery characteristic data after data fitting, and inferring the electrochemical performance data of the battery based on the SOC value range of the SOC calculation includes:

[0030] Using the battery characteristic data to simulate the battery SOC from 0% to 100%;

[0031] The battery charge and discharge capacity that can be achieved when the electrochemical performance of the battery is 100% at the simulation time is identified to obtain the electrochemical performance data.

[0032] The present invention also proposes an electrochemical performance identification device based on the battery charging and discharging process, comprising:

[0033] The first signal unit is used to input a first battery signal through a data anchor point preset at a battery charge and discharge stage, and generate first battery characteristic data using the first battery signal, wherein the first battery signal includes voltage, current and battery temperature distribution; and at the same time,

[0034] The second signal unit is used to input a second battery signal through a Bragg grating sensor preset in the battery, and generate second battery characteristic data using the second battery signal, wherein the second battery signal includes the internal stress of the battery; and at the same time,

[0035] A third signal unit, used for inputting a third battery signal to the battery through an in-situ microscopy technique, and generating third battery characteristic data using the third battery signal, wherein the third battery signal includes SEI film dynamic change information;

[0036] A model fitting unit, used for calling the MBCF-DSEM model under the Matlab neural network simulation toolbox under the device to perform data fitting on the first battery characteristic data, the second battery characteristic data and the third battery characteristic data;

[0037] The performance evaluation unit is used to perform a battery simulation SOC calculation for one cycle on the battery characteristic data after data fitting, and to deduce the electrochemical performance data of the battery based on the SOC value range of the SOC calculation.

[0038] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned electrochemical performance identification method based on the battery charging and discharging process when executing the computer program.

[0039] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned electrochemical performance identification method based on the battery charging and discharging process are implemented.

[0040] The electrochemical performance identification method, device and equipment based on the battery charging and discharging process provided by the present invention have the following beneficial effects:

[0041] The first battery characteristic data, the second battery characteristic data and the third battery characteristic data are fitted by the MBCF-DSEM model, and a battery simulation SOC calculation is performed on the battery characteristic data after data fitting in one cycle, and then the electrochemical performance data of the battery is calculated based on the SOC numerical range of the SOC calculation; thereby realizing rapid and accurate identification of various electrochemical properties of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic flow chart of an electrochemical performance identification method based on a battery charging and discharging process in one embodiment of the present invention;

[0043] Figure 2 It is a structural block diagram of an electrochemical performance identification device based on a battery charging and discharging process in one embodiment of the present invention;

[0044] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0045] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] 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 accompanying drawings and 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.

[0047] Reference Figure 1 , which is a flow chart of a method for identifying electrochemical properties based on a battery charging and discharging process proposed by the present invention, comprising the following steps:

[0048] S11, inputting a first battery signal through a data anchor point preset at a battery charge and discharge stage, and generating first battery characteristic data using the first battery signal, wherein the first battery signal includes voltage, current and battery temperature distribution; and simultaneously,

[0049] S12, inputting a second battery signal through a Bragg grating sensor preset in the battery, and generating second battery characteristic data using the second battery signal, wherein the second battery signal includes the internal stress of the battery; and at the same time,

[0050] S13, inputting a third battery signal to the battery through an in-situ microscopy technique, and generating third battery characteristic data using the third battery signal, wherein the third battery signal includes SEI film dynamic change information;

[0051] S2, calling the MBCF-DSEM model under the Matlab neural network simulation toolbox under the device to perform data fitting on the first battery characteristic data, the second battery characteristic data and the third battery characteristic data;

[0052] S3, performing a battery simulation SOC calculation for one cycle on the battery characteristic data after data fitting, and calculating the electrochemical performance data of the battery based on the SOC value range of the SOC calculation.

[0053] Specifically,

[0054] The first battery signal is inputted through a data anchor point preset at a battery charge and discharge level, and the steps include:

[0055] A thermistor is preset at the charging and discharging stage of the battery as a data anchor point. The thermistor is connected in series with the current path of the battery, and its resistance value changes with the change of the battery temperature. The current information of the battery is collected in real time through the thermistor, because a voltage drop will be generated when the current passes through the thermistor, and the voltage drop is proportional to the current size. At the same time, the temperature information of the battery is calculated by measuring the voltage difference across the thermistor and combining the temperature-resistance characteristic curve of the thermistor. In addition, the voltage at the charging and discharging stage of the battery is directly measured to obtain the voltage information of the battery.

[0056] The second battery signal is inputted by a Bragg grating sensor preset in the battery, and the steps include:

[0057] At least one Bragg grating sensor is preset inside the battery. The sensor is composed of an optical fiber and a periodically arranged grating structure to sense the mechanical strain generated inside the battery due to the charging and discharging process.

[0058] When the battery is charged or discharged, the mechanical strain inside the battery causes the grating period of the Bragg grating sensor to change, thereby changing the wavelength of the light reflected or transmitted by the sensor;

[0059] The optical detection system monitors the wavelength change of the light reflected or transmitted by the Bragg grating sensor in real time, and converts the wavelength change into an electrical signal;

[0060] The converted electrical signal is analyzed and the stress distribution inside the battery is calculated based on the known relationship between the wavelength change of the Bragg grating sensor and the mechanical strain.

[0061] Inputting a third cell signal to the cell by in situ microscopy technology comprises:

[0062] During the battery charging and discharging process, the in-situ microscopy equipment is used to perform real-time, high-resolution microscopic observation of the battery surface or a specific area. The in-situ microscopy equipment captures the dynamic change image of the SEI film on the battery surface or in a specific area, including but not limited to the changes in the morphology, thickness, composition and distribution of the SEI film. The captured image data is processed and analyzed, and the extracted dynamic change characteristic parameters of the SEI film are used as the third battery signal.

[0063] Among them, the training method of the MBCF-DSEM model includes:

[0064] Acquire a training sample, wherein the training sample includes multiple sampled first battery characteristic data, second battery characteristic data, and third battery characteristic data;

[0065] Building a MBCF-DSEM model, the model structure includes an input layer, a feature fusion layer, a dynamic SEI film simulation layer, an output layer and a feedback mechanism layer, and importing the training samples into the output layer;

[0066] The MBCF-DSEM model is trained using supervised training, and the training goal is to minimize the difference between the model prediction value and the battery performance data.

[0067] The simulated battery data under the MBCF-DSEM model is tested, wherein the simulated battery data is the battery SOC prediction accuracy of data fitting.

[0068] The step of performing data fitting on the first battery characteristic data, the second battery characteristic data and the third battery characteristic data by using the MBCF-DSEM model (Multidimensional Battery Characteristic Fusion-DynamicSEI Membrane Embedded Model, MBCF-DSEM) comprises:

[0069] The first linear frame, the second linear frame and the third linear frame are respectively set to correspond to the linear changes of the battery characteristics in the first battery characteristic data, the second battery characteristic data and the third battery characteristic data respectively;

[0070] Generate battery data fitting wave line based on linearly changing fluctuation frequency and amplitude;

[0071] The digitized battery characteristic data is obtained by fitting the wave line of the battery data.

[0072] The steps of performing a battery simulation SOC calculation for one cycle on the battery characteristic data after data fitting, and inferring the electrochemical performance data of the battery based on the SOC value range of the SOC calculation include:

[0073] Using the battery characteristic data to simulate the battery SOC from 0% to 100%;

[0074] The battery charge and discharge capacity that can be achieved when the electrochemical performance of the battery is 100% at the simulation time is identified to obtain the electrochemical performance data.

[0075] refer to Figure 2 , which is an electrochemical performance identification device based on the battery charging and discharging process proposed by the present invention, comprising:

[0076] The first signal unit is used to input a first battery signal through a data anchor point preset at a battery charge and discharge stage, and generate first battery characteristic data using the first battery signal, wherein the first battery signal includes voltage, current and battery temperature distribution; and at the same time,

[0077] The second signal unit is used to input a second battery signal through a Bragg grating sensor preset in the battery, and generate second battery characteristic data using the second battery signal, wherein the second battery signal includes the internal stress of the battery; and at the same time,

[0078] A third signal unit, used for inputting a third battery signal to the battery through an in-situ microscopy technique, and generating third battery characteristic data using the third battery signal, wherein the third battery signal includes SEI film dynamic change information;

[0079] A model fitting unit, used for calling the MBCF-DSEM model under the Matlab neural network simulation toolbox under the device to perform data fitting on the first battery characteristic data, the second battery characteristic data and the third battery characteristic data;

[0080] The performance evaluation unit is used to perform a battery simulation SOC calculation for one cycle on the battery characteristic data after data fitting, and to deduce the electrochemical performance data of the battery based on the SOC value range of the SOC calculation.

[0081] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0082] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0083] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0084] In summary, the first battery signal is input through the data anchor point preset at the battery charge and discharge stage, and the first battery characteristic data is generated by using the first battery signal; the second battery signal is input through the Bragg grating sensor preset in the battery, and the second battery characteristic data is generated by using the second battery signal; the third battery signal is input to the battery through in-situ microscopy technology, and the third battery characteristic data is generated by using the third battery signal; the first battery characteristic data, the second battery characteristic data and the third battery characteristic data are then fitted by the model, and the battery simulation SOC calculation is performed on the battery characteristic data after the data fitting under one cycle, and the electrochemical performance data of the battery is deduced based on the SOC numerical range of the SOC calculation; the rapid and accurate identification of various electrochemical properties of the battery is achieved.

[0085] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0086] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0087] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for identifying electrochemical properties based on the battery charging and discharging process, characterized in that: The following steps are involved: A first battery signal is inputted through a data anchor point preset at a battery charge and discharge level, and first battery characteristic data is generated using the first battery signal, wherein the first battery signal includes voltage, current and battery temperature distribution; at the same time, The second battery signal is inputted by a Bragg grating sensor preset in the battery, and the second battery characteristic data is generated by using the second battery signal, wherein the second battery signal includes the internal stress of the battery; at the same time, Inputting a third battery signal to the battery through in-situ microscopy technology, and generating third battery characteristic data using the third battery signal, wherein the third battery signal includes SEI film dynamic change information; Calling the MBCF-DSEM model under the Matlab neural network simulation toolbox under the device to perform data fitting on the first battery characteristic data, the second battery characteristic data, and the third battery characteristic data; A battery simulation SOC calculation is performed on the battery characteristic data after data fitting in one cycle, and the electrochemical performance data of the battery is inferred based on the SOC value range of the SOC calculation.

2. The electrochemical performance identification method based on the battery charging and discharging process according to claim 1 is characterized in that: The first battery signal is inputted through a data anchor point preset at a battery charge and discharge level, and the steps include: A thermistor is preset at the charging and discharging stage of the battery as a data anchor point. The thermistor is connected in series with the current path of the battery, and its resistance value changes with the change of the battery temperature. The current information of the battery is collected in real time through the thermistor, because a voltage drop will be generated when the current passes through the thermistor, and the voltage drop is proportional to the current size. At the same time, the temperature information of the battery is calculated by measuring the voltage difference across the thermistor and combining the temperature-resistance characteristic curve of the thermistor. In addition, the voltage at the charging and discharging stage of the battery is directly measured to obtain the voltage information of the battery.

3. The electrochemical performance identification method based on the battery charging and discharging process according to claim 1 is characterized in that: The second battery signal is inputted by a Bragg grating sensor preset in the battery, and the steps include: At least one Bragg grating sensor is preset inside the battery. The sensor is composed of an optical fiber and a periodically arranged grating structure to sense the mechanical strain generated inside the battery due to the charging and discharging process. When the battery is charged or discharged, the mechanical strain inside the battery causes the grating period of the Bragg grating sensor to change, thereby changing the wavelength of the light reflected or transmitted by the sensor; The optical detection system monitors the wavelength change of the light reflected or transmitted by the Bragg grating sensor in real time, and converts the wavelength change into an electrical signal; The converted electrical signal is analyzed and the stress distribution inside the battery is calculated based on the known relationship between the wavelength change of the Bragg grating sensor and the mechanical strain.

4. The electrochemical performance identification method based on the battery charging and discharging process according to claim 1 is characterized in that: Inputting a third cell signal to the cell by in situ microscopy technology comprises: During the battery charging and discharging process, the in-situ microscopy equipment is used to perform real-time, high-resolution microscopic observation of the battery surface or a specific area. The in-situ microscopy equipment captures the dynamic change image of the SEI film on the battery surface or in a specific area, including but not limited to the changes in the morphology, thickness, composition and distribution of the SEI film. The captured image data is processed and analyzed, and the extracted dynamic change characteristic parameters of the SEI film are used as the third battery signal.

5. The electrochemical performance identification method based on the battery charging and discharging process according to claim 1 is characterized in that: The training method of the MBCF-DSEM model includes: Acquire a training sample, wherein the training sample includes multiple sampled first battery characteristic data, second battery characteristic data, and third battery characteristic data; Building a MBCF-DSEM model, the model structure includes an input layer, a feature fusion layer, a dynamic SEI film simulation layer, an output layer and a feedback mechanism layer, and importing the training samples into the output layer; The MBCF-DSEM model is trained using supervised training, and the training goal is to minimize the difference between the model prediction value and the battery performance data. The simulated battery data under the MBCF-DSEM model is tested, wherein the simulated battery data is the battery SOC prediction accuracy of data fitting.

6. The electrochemical performance identification method based on the battery charging and discharging process according to claim 1 is characterized in that: The step of performing data fitting on the first battery characteristic data, the second battery characteristic data and the third battery characteristic data by the MBCF-DSEM model includes: The first linear frame, the second linear frame and the third linear frame are respectively set to correspond to the linear changes of the battery characteristics in the first battery characteristic data, the second battery characteristic data and the third battery characteristic data respectively; Generate battery data fitting wave line based on linearly changing fluctuation frequency and amplitude; The digitized battery characteristic data is obtained by fitting the wave line of the battery data.

7. The electrochemical performance identification method based on the battery charging and discharging process according to claim 1 is characterized in that: The steps of performing a battery simulation SOC calculation for one cycle on the battery characteristic data after data fitting, and inferring the electrochemical performance data of the battery based on the SOC value range of the SOC calculation include: Using the battery characteristic data to simulate the battery SOC from 0% to 100%; The battery charge and discharge capacity that can be achieved when the electrochemical performance of the battery is 100% at the simulation time is identified to obtain the electrochemical performance data.

8. An electrochemical performance identification device based on the battery charging and discharging process, characterized in that: include: The first signal unit is used to input a first battery signal through a data anchor point preset at a battery charge and discharge stage, and generate first battery characteristic data using the first battery signal, wherein the first battery signal includes voltage, current and battery temperature distribution; and at the same time, The second signal unit is used to input a second battery signal through a Bragg grating sensor preset in the battery, and generate second battery characteristic data using the second battery signal, wherein the second battery signal includes the internal stress of the battery; and at the same time, A third signal unit, used for inputting a third battery signal to the battery through an in-situ microscopy technique, and generating third battery characteristic data using the third battery signal, wherein the third battery signal includes SEI film dynamic change information; A model fitting unit, used for calling the MBCF-DSEM model under the Matlab neural network simulation toolbox under the device to perform data fitting on the first battery characteristic data, the second battery characteristic data and the third battery characteristic data; The performance evaluation unit is used to perform a battery simulation SOC calculation for one cycle on the battery characteristic data after data fitting, and to deduce the electrochemical performance data of the battery based on the SOC value range of the SOC calculation.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the electrochemical performance identification method based on the battery charging and discharging process described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electrochemical performance identification method based on the battery charging and discharging process described in any one of claims 1 to 7 are implemented.

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