Battery health degree detection method and device and medium

Abstract

The invention provides a battery health degree detection method and apparatus, and a medium. The method comprises the steps of collecting data related to a battery; defining a reference parameter group set according to the data, wherein the set at least comprises the following reference parameters: a cell voltage difference, a power consumption mean value, a cell temperature difference, an accumulated mileage, a battery calendar life, an accumulated cycle index, a battery material coefficient, a battery combination string number and a state of charge; classifying and normalizing the reference parameters, and calculating an environment correction coefficient, a calibration coefficient and a dynamic weight factor according to the classified and sorted data; calculating an attenuation contribution value and a total attenuation value of each reference parameter according to the normalized data, the environment correction coefficient, the calibration coefficient and the dynamic weight factor; and calculating a battery health degree value according to the total attenuation value. The battery pack does not need to be disassembled, the detection operation is simple, convenient and rapid, a multi-parameter coupling mechanism is adopted in the calculation process, and the detection accuracy is high.

Description

Technical Field

[0001] This application relates to the technical field of battery detection, and specifically relates to a method, device, and medium for detecting battery health. Background Art

[0002] With the continuous development of new energy technologies, new energy batteries have been widely used in fields such as electric vehicles and energy storage devices. As the core component of new energy, the performance and lifespan of the battery are affected by various factors, such as the number of charge and discharge cycles, temperature, current, etc. To ensure the safe and reliable operation of new energy batteries, the detection of their health status has become particularly important.

[0003] Traditional methods for detecting battery state of health (SOH) have many deficiencies, and there is no unified standard in the market. For example, it is necessary to disassemble the battery pack, the detection time is up to dozens of hours, and the detection accuracy is poor. In addition, existing detection devices are usually large in volume, complex in operation, and not convenient for on-site detection and maintenance. Therefore, there is an urgent need in the market for a method that can quickly and conveniently detect the battery state of health (SOH) to meet the needs of new energy battery detection and battery pack residual value estimation, etc. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, and medium for detecting battery health, which can solve the problems of complex detection operation, slow detection, and poor accuracy in the prior art for detecting battery health.

[0005] To solve the above technical problems, an embodiment of this application provides a method for detecting battery health, including: collecting data related to the battery; defining a set of reference parameter groups according to the data, and the set at least includes the following reference parameters: cell voltage difference, average power consumption, cell temperature difference, cumulative mileage, battery calendar life, cumulative number of cycles, battery material coefficient, number of battery combination strings, and state of charge; classifying and organizing the reference parameters, and calculating an environmental correction coefficient, a calibration coefficient, and a dynamic weight factor according to the classified and organized data; normalizing the reference parameters; calculating the attenuation contribution value of each reference parameter according to the normalized data, the environmental correction coefficient, the calibration coefficient, and the dynamic weight factor; calculating the total attenuation value according to the attenuation contribution values of each reference parameter; and calculating the battery health value according to the total attenuation value.

[0006] In one of the embodiments, before defining the set of reference parameter groups according to the data, it further includes: filtering, fine-tuning, and correcting the collected data, and suppressing data fluctuations outside a preset fluctuation range to correct data drift.

[0007] In one of the embodiments, the set of defined reference parameter groups includes: presetting the nominal maximum and minimum values of each reference parameter; the formula for normalizing the reference parameter is as follows,

[0008] where, is the value of the th parameter, is the value after normalizing the th parameter, and are the nominal maximum and minimum values of the th parameter respectively. In one of the embodiments, the calculation formula for the environmental correction coefficient is:

[0009] where k is the temperature response coefficient (0.1 ≤ k ≤ 0.5), is the environmental temperature, is the standard reference temperature.

[0010] In one of the embodiments, the calculation formula for the dynamic weight factor is:

[0011] where is the empirical coefficient, is the cumulative number of cycles, is the battery calendar life, Q is the battery material coefficient, is the cell voltage difference.

[0012] In one of the embodiments, the formula for calculating the attenuation contribution value of each reference parameter is:

[0013] where, is the attenuation contribution value of each reference parameter, is the calibration coefficient, is the dynamic weight factor, is the environmental correction coefficient.

[0014] In one of the embodiments, when the reference parameter for calculating the attenuation contribution value is the cumulative mileage, the coupling of the mileage intensity factor is increased, and the mileage intensity factor is expressed as:

[0015] where, is the mileage attenuation coefficient, D is the cumulative mileage, is a constant; The formula for the attenuation contribution value of the cumulative mileage is as follows:

[0016] Wherein, is the calibration coefficient of the cumulative mileage D, is the dynamic weight factor of the cumulative mileage D, is the environmental correction coefficient of the cumulative mileage D, is the cumulative mileage D after normalization processing.

[0017] In one embodiment, the calculation formula for calculating the total attenuation value based on the attenuation contribution values of the respective reference parameters is:

[0018] Wherein, is the cell voltage difference, is the cell temperature difference, SOC is the state of charge, is the coupling adjustment factor related to the number of battery cell series strings S, is the reference parameter 、 and the attenuation contribution values of SOC.

[0019] In one embodiment, the calculation formula for the battery health value SOH is:

[0020] Where is the attenuation threshold, Satisfies:

[0021] Wherein, is the battery threshold adjustment factor.

[0022] In one embodiment, the method for detecting the battery health further includes: outputting the result of the battery health value in a preset format and generating a detection report.

[0023] The embodiment of the present application also provides a detection device for battery health, which is applied to the above-mentioned detection method for battery health, and includes: a data acquisition module for acquiring battery-related data; a set definition module for defining a set of reference parameter groups according to the data, and the set at least includes the following reference parameters: cell voltage difference, average power consumption, cell temperature difference, cumulative mileage, battery calendar life, cumulative cycle times, battery material coefficient, number of battery combination strings, and state of charge; a classification and sorting module for classifying and sorting the reference parameters; a normalization module for performing normalization processing on the reference parameters; a calculation module for calculating an environment correction coefficient, a calibration coefficient, and a dynamic weight factor according to the data after classification and sorting; calculating the attenuation contribution value of each reference parameter according to the data after normalization processing, the environment correction coefficient, the calibration coefficient, and the dynamic weight factor; calculating the total attenuation value according to the attenuation contribution values of each reference parameter; and calculating the battery health value according to the total attenuation value.

[0024] In one of the embodiments, the calculation module is further configured to generate a detection report according to the calculation result; the detection device further includes an output module; and the output module is configured to output the result of the battery health value and the detection report in a preset format.

[0025] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned detection method for battery health are implemented.

[0026] The embodiment of the present application calculates the final battery health value by collecting battery-related information, applying relevant algorithms and custom variables, without the need to disassemble the battery pack, and the operation is simple and fast. Moreover, the calculation process adopts a multi-parameter coupling mechanism, and the detection accuracy is high. Description of the Drawings

[0027] One or more embodiments are exemplarily illustrated by the pictures in the corresponding drawings. These exemplary illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements, unless otherwise stated, and the drawings in the drawings do not constitute a proportional limitation.

[0028] Figure 1 is a flowchart of a detection method for battery health according to an embodiment of the present application; Figure 2 is a flowchart of a detection method for battery health according to another embodiment of the present application; Figure 3 is a schematic structural diagram of a detection device for battery health according to an embodiment of the present application; Figure 4 is a schematic structural diagram of a detection device for battery health according to another embodiment of the present application; Figure 5 It is a product schematic diagram of a device for detecting battery health according to an embodiment of the present application. Detailed implementation manners

[0029] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application. It should be noted that the following describes various aspects of embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present application, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspects described herein can be used to implement the device and / or practice the method. In addition, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0030] It should also be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. The drawings only show the components related to the present application, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex. In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the examples can be practiced without these specific details.

[0031] Currently, aiming at the problems of complex detection operation, long detection time, and poor accuracy in detecting battery health in the prior art, there is an urgent need for a method for quickly, conveniently, and accurately detecting battery health to meet the needs of new energy battery detection and battery pack residual value estimation, etc.

[0032] Based on this, on the one hand, an embodiment of the present application proposes a method for detecting battery health, and its process is asFigure 1 As shown below: In step 101, data related to the battery is collected. Specifically, in the embodiments of the present application, there are various ways to collect data. For example, communication can be established with the battery management system (BMS) through the vehicle fast charging port or a dedicated interface to automatically obtain battery-related parameters (such as the voltage difference between battery cells ( ), temperature difference ( ), state of charge (SOC); charge and discharge energy, current, cumulative cycle count ( ), cumulative mileage D, etc.); by parsing the vehicle identification number, the manufacturer and battery model information are obtained, or by scanning or manually inputting the nameplate information, key data such as battery specifications and factory date are supplemented; real-time data (such as SOC, voltage, temperature, etc.) is directly read from the vehicle instrument or the central control system; measured in real time through an external sensor; or other supplementary collection methods. The data content collected in the embodiments of the present application includes but is not limited to the following parameters: battery manufacturer, battery material coefficient, installation method, series-parallel mode, number of battery combination strings, battery cell type, ideal life, ideal cycle count, ideal voltage and voltage difference, battery calendar life, balancing mode, fast / slow charge ratio, cumulative cycle count, cumulative mileage, average power consumption (kWh / 100km), common regions, calibrated capacity / voltage / energy of battery cells, total calibrated capacity / voltage / energy, extreme values of single-cell voltage / temperature, state of charge SOC, and other parameters.

[0033] In step 102, a set of reference parameter groups is defined according to the data. The set of reference parameter groups defined in the embodiments of the present application includes at least the following reference parameters: voltage difference between battery cells , average power consumption , temperature difference between battery cells , cumulative mileage D, battery calendar life , cumulative cycle count , battery material coefficient Q, number of battery combination strings S, and state of charge SOC index. The set of parameter groups defined in this embodiment can be denoted as , where correspond to the above-mentioned reference parameters respectively, and when defining the set of reference parameter groups, the nominal maximum and minimum values of each reference parameter can be preset. For example: State of charge SOC: =0, =100 (%) Cumulative mileage D: =0, =constant (km); Number of battery combination strings S: =1, =200 (strings); Battery material coefficient Q: = 0.7, = 1.0; It should be noted that the serial numbers and upper and lower limit data of the above nominal maximum and minimum values are only for illustrative purposes and can be determined according to actual needs, and are not limited in the embodiments of the present application.

[0034] In step 103, the reference parameters are classified and sorted, and the environmental correction coefficient, calibration coefficient, and dynamic weight factor are calculated based on the classified and sorted data. Optionally, the calculation formula for the environmental correction coefficient is: (1) where k is the temperature response coefficient (0.1 ≤ k ≤ 0.5), is the environmental temperature, is the standard reference temperature. The environmental correction coefficient is used to quantify the impact of environmental conditions (such as temperature) on the attenuation rate of battery parameters, dynamically adjust the parameter attenuation contribution value under different environments, and make the detection result of battery health closer to the actual aging situation.

[0035] Optionally, the calculation formula for the dynamic weight factor is: (2) where is the empirical coefficient, is the cumulative number of charge-discharge cycles, is the calendar life of the battery, Q is the battery material coefficient, is the voltage difference between battery cells.

[0036] By adjusting these parameters, different battery types (such as power batteries vs. energy storage batteries) and usage scenarios (such as taxis vs. household cars) can be adapted to achieve accurate health assessment.

[0037] The definitions of each empirical coefficient are shown in Table 1.

[0038] Table 1: Definitions of each parameter

[0039] The battery material coefficient Q can refer to Table 2.

[0040] Table 2: Battery material coefficient and its physical properties

[0041] It should be noted that the specific values in Table 1 and Table 2 are only for illustrative purposes and can be determined according to the actual situation, and are not limited in the embodiments of the present application.

[0042] In step 104, the reference parameters are normalized. Specifically, the reference parameters can be normalized by the following formula (3) where is the value of the th parameter, is the value after normalizing the th parameter, and are the nominal maximum and minimum values of the th parameter respectively.

[0043] In this embodiment, a method for normalizing reference parameters is provided. However, in practical applications, other methods can also be used, which are not limited in the embodiments of this application. By normalizing the reference parameters, data with different dimensions and orders of magnitude can be converted to the same scale, eliminating the dimensional influence between data features, facilitating comprehensive comparison and evaluation, and improving the speed and accuracy of data processing.

[0044] In step 105, the attenuation contribution value of each reference parameter is calculated according to the normalized data, the environment correction coefficient, the calibration coefficient, and the dynamic weight factor. Specifically, in an optional embodiment, the attenuation contribution value of each reference parameter can be calculated by formula (4): (4) where is the calibration coefficient, which can be obtained through experiments or neural network training; is the dynamic weight factor, which can be obtained by formula 2; is the environment correction coefficient, which can be obtained by formula 1.

[0045] Preferably, to make the calculated attenuation contribution value more accurate, when the reference parameter for calculating the attenuation contribution value is the cumulative mileage D, the coupling of the mileage intensity factor can be increased. The mileage intensity factor is expressed as: , where is the mileage attenuation coefficient, and the preset value can be set to 0.8, is a constant; specifically the value can be determined according to the product of the maximum mileage and the battery material coefficient Q.

[0046] The formula for the attenuation contribution value of the cumulative mileage D is: (5) where is the calibration coefficient of the cumulative mileage D, is the dynamic weight factor of the cumulative mileage D, is the environmental correction factor for the cumulative mileage D, is the cumulative mileage D after normalization.

[0047] In step 106, the total attenuation value is calculated according to the attenuation contribution values of the benchmark parameters. Specifically, the total attenuation value can be calculated according to the attenuation contribution values of the benchmark parameters and the coupling adjustment factor of the number of battery combination strings. The formula for calculating the total attenuation value is: (6) Wherein, is the cell voltage difference, is the cell temperature difference, SOC is the state of charge, is the benchmark parameter 、 and the attenuation contribution values of SOC, is the coupling adjustment factor related to the number of battery combination strings S, , is the basic coupling factor, which can be obtained through support vector machine (SVM) or neural network training, such as 0.15.

[0048] Its coupling mechanism is: when S ≤ 100, has a small impact on the total attenuation value Y. When S > 100, increases significantly with S. That is, when S > 100, the synergistic degradation effect of the cell voltage difference ( ), the cell temperature difference ( ), and the state of charge SOC can be amplified, which can more accurately reflect the attenuation characteristics of high-string battery packs and is applicable to the battery health SOH detection scenario of high-string battery systems in new energy vehicles.

[0049] In step 107, the battery health value is calculated according to the total attenuation value. Specifically, the battery health value can be calculated according to the total attenuation value and the battery threshold adjustment factor. The formula for calculating the battery health value is as follows: (7) Where is the attenuation threshold, satisfying:

[0050] Wherein, is the theoretical maximum value of the normalized parameter, is the battery threshold adjustment factor, , is the compensation coefficient, such as . By introducing the battery threshold adjustment factor It can dynamically correct the attenuation characteristic differences of different battery material coefficients, ensure the compatibility and accuracy for different battery materials, and solve the problem of "threshold rigidity" in the traditional battery health SOH model.

[0051] In the embodiment of the present application, by collecting battery-related information, applying relevant algorithms and custom variables, the final battery health value is calculated. The detection process does not require disassembling the battery pack, and the operation is simple and fast. Moreover, the calculation process adopts a multi-parameter coupling mechanism, and the detection accuracy is high.

[0052] In an optional embodiment, after collecting battery-related data and before defining the set of reference parameter groups according to the data, the collected data can also be filtered, fine-tuned and corrected, and data fluctuations outside a preset fluctuation range can be suppressed (for example, data fluctuations with a fluctuation range ≥ 30% are suppressed) to correct data drift.

[0053] In another optional embodiment, the method for detecting battery health further includes: outputting the result of the battery health value in a preset format and generating a detection report, and its process is as Figure 2 shown below: Steps 201 - 207 in this embodiment are similar to Figure 1 Steps 101 - 107 of the embodiment shown, and will not be described in detail here.

[0054] In step 208, the result of the battery health value is output in a preset format and a detection report is generated. In this embodiment, the battery health SOH result can be output in a unified output format (such as dd.dd%). To avoid ambiguity in manual interpretation and facilitate system automation processing. The detection report in this embodiment can be generated according to the calculation result. The generated detection report can include detection results (such as current health: 82.3%), health status ratings, classification labels (such as good (80% - 90%), warning (< 70%)), attenuation analysis, maintenance suggestions, risk warnings, etc., to help customers quickly understand the battery status and formulate charge and discharge or maintenance strategies.

[0055] Based on the same inventive concept, the present application also provides a device for detecting battery health. It should be noted that the device exemplified below is an example of the device corresponding to the above method embodiment. In other device embodiments, the function settings of unit modules and the number of modules can be set accordingly according to the foregoing method embodiments. As Figure 3As shown in the figure, the battery health detection device includes: a data acquisition module 1 for acquiring battery-related data; a set definition module 2 for defining a set of reference parameter groups according to the data, the set at least including the following reference parameters: cell voltage difference, average power consumption, cell temperature difference, cumulative mileage, battery calendar life, cumulative cycle times, battery material coefficient, number of battery combination strings, and state of charge; a classification and sorting module 3 for classifying and sorting the reference parameters; a normalization module 4 for normalizing the reference parameters; a calculation module 5 for calculating an environmental correction coefficient, a calibration coefficient, and a dynamic weight factor according to the classified and sorted data; calculating the attenuation contribution value of each reference parameter according to the normalized data, the environmental correction coefficient, the calibration coefficient, and the dynamic weight factor; calculating the total attenuation value according to the attenuation contribution values of the respective reference parameters; and calculating the battery health value according to the total attenuation value.

[0056] In the embodiment of the present application, the data acquisition module 1 acquires battery-related information, applies relevant algorithms and custom variables, and the calculation module 5 calculates the final battery health value without disassembling the battery pack, and the operation is simple and fast. Moreover, the calculation process adopts a multi-parameter coupling mechanism, and the detection accuracy is high.

[0057] In another embodiment of the present application, as Figure 4 shown, the battery health detection device further includes an output module 6, and the calculation module 5 is further configured to generate a detection report according to the calculation result; the output module 6 is configured to output the result of the battery health value and the detection report in a preset format to help the customer quickly understand the battery state and formulate a charge / discharge or maintenance strategy.

[0058] In an application scenario, referring to Figure 4 and Figure 5 , the battery health detection device of the embodiment of the present application is as Figure 5 shown. A detection gun head 51 is installed at the front of the device, and there is a circuit board 52 inside the device. The circuit board 52 can be an integrated circuit chip (such as an MCU). The circuit board 52 integrates a data acquisition module 1, a set definition module 2, a classification and sorting module 3, a normalization module 4, a calculation module 5, and a communication unit; a lithium battery 53 powers the entire device.

[0059] When using this detection device to detect the state of health (SOH) of the battery, first turn on the switch 54 of the detection device. The detection gun head 51 establishes a communication connection with the vehicle battery management system (BMS) through the new energy vehicle fast charging port, and then collects battery-related data information and sends it to the circuit board 52. The circuit board 52 processes the data, calculates the relevant data through a linear regression model or a neural network model, and outputs the calculation result to the display module through a communication unit (such as the output module 6) for display, so that the user can intuitively see the result of the battery health value and the detection report. Optionally, the communication unit can also be a wireless transmission module, such as a Bluetooth module that supports the Bluetooth Low Energy (BLE) protocol. The calculation result can be output to an external terminal device through the wireless transmission unit, and the result of the battery health value and the detection report can be viewed through the external terminal device. In the embodiment of the present application, the battery-related data information collected can also include manually input supplementary information related to the battery, and the battery health value is calculated based on the supplementary information, and an improved solution for battery health management is given. To help customers quickly understand the battery state and formulate charge and discharge or maintenance strategies.

[0060] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method for detecting the battery health degree described in any one of the embodiments of the present application.

[0061] It should be noted that the computer storage medium may include, but is not limited to: portable disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical storage devices, magnetic storage devices, or any suitable combination of the above. In a possible implementation manner, the present invention can also provide the data processing in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute several steps of the method described in any one of the foregoing embodiments. As mentioned above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for detecting the health of a battery, characterized in that, Including: Collecting data related to the battery; Defining a set of reference parameter groups according to the data, the set at least including the following reference parameters: cell voltage difference, average power consumption, cell temperature difference, cumulative mileage, battery calendar life, cumulative cycle times, battery material coefficient, number of battery combination strings, and state of charge; Classifying and sorting out the reference parameters, and calculating an environmental correction coefficient, a calibration coefficient, and a dynamic weight factor according to the classified and sorted data; Normalizing the reference parameters; Calculating the attenuation contribution value of each reference parameter according to the normalized data, the environmental correction coefficient, the calibration coefficient, and the dynamic weight factor; Calculating the total attenuation value according to the attenuation contribution values of the reference parameters; Calculating the battery health value according to the total attenuation value.

2. The method for detecting the battery health according to claim 1, wherein, Before defining the set of reference parameter groups according to the data, it further includes: Filtering, fine-tuning, and correcting the collected data, and suppressing data fluctuations outside a preset fluctuation range to correct data drift.

3. The method for detecting the battery health according to claim 1, wherein, Defining the set of reference parameter groups includes: presetting the nominal maximum and minimum values of each reference parameter; The formula for normalizing the reference parameters is as follows, Among them, is the value of the th parameter, is the value after normalizing the th parameter, and are the nominal maximum and minimum values of the th parameter, respectively.

4. The method for detecting the battery health according to claim 3, wherein The formula for calculating the attenuation contribution value of each reference parameter is: Among them, is the attenuation contribution value of each reference parameter, is the calibration coefficient, is the dynamic weight factor, is the environmental correction coefficient.

5. The method for detecting battery health according to claim 4, wherein The calculation formula for the environmental correction coefficient is: where k is the temperature response coefficient (0.1 ≤ k ≤ 0.5), is the ambient temperature, is the standard reference temperature.

6. The method for detecting the battery health according to claim 4, wherein The calculation formula for the dynamic weight factor is: wherein is an empirical coefficient, is the cumulative number of cycles, is the calendar life of the battery, Q is the battery material coefficient, is the voltage difference between battery cells.

7. The method for detecting the battery health according to claim 4, characterized in that, When the reference parameter for calculating the attenuation contribution value is the cumulative mileage, the coupling of the mileage intensity factor is increased, The mileage intensity factor is expressed as: Among them, is the mileage attenuation coefficient, D is the cumulative mileage, is a constant; The formula for the attenuation contribution value of the cumulative mileage is: Among them, is the calibration coefficient of the cumulative mileage D, is the dynamic weight factor of the cumulative mileage D, is the environmental correction coefficient of the cumulative mileage D, is the cumulative mileage D after normalization processing.

8. The method for detecting battery health according to claim 4, characterized in that, The calculation formula for calculating the total attenuation value according to the attenuation contribution values of the reference parameters is: Among them, is the voltage difference of the battery cells, is the temperature difference of the battery cells, SOC is the state of charge, is the coupling adjustment factor related to the number of battery cell strings S, is the reference parameter 、 and the attenuation contribution value of SOC.

9. The method for detecting the battery health according to claim 8, characterized in that, The battery health value is calculated as follows: wherein is the attenuation threshold, satisfying: Among them, is the battery threshold adjustment factor.

10. The method for detecting the battery health according to claim 1, wherein It further includes: Outputting the result of the battery health value in a preset format and generating a detection report.

11. A detection device for battery health, which is applied to the detection method for battery health described in any one of claims 1-10, characterized in that, Including: A data acquisition module for collecting data related to the battery; A set definition module for defining a set of reference parameter groups according to the data, the set at least including the following reference parameters: cell voltage difference, average power consumption, cell temperature difference, cumulative mileage, battery calendar life, cumulative cycle times, battery material coefficient, number of battery combination strings, and state of charge; A classification and sorting module for classifying and sorting out the reference parameters; A normalization module for normalizing the reference parameters; A calculation module for calculating an environmental correction coefficient, a calibration coefficient, and a dynamic weight factor according to the classified and sorted data; Calculating the attenuation contribution value of each reference parameter according to the normalized data, the environmental correction coefficient, the calibration coefficient, and the dynamic weight factor; calculating the total attenuation value according to the attenuation contribution values of the reference parameters; And calculating the battery health value according to the total attenuation value.

12. The detection device for battery health according to claim 11, characterized in that, The calculation module is further used for generating a detection report according to the calculation result; The detection device further includes an output module; The output module is used for outputting the result of the battery health value and the detection report in a preset format.

13. A computer-readable storage medium, on which a computer program is stored, characterized in that When the computer program is executed by a processor, it implements the steps of the battery health detection method according to any one of claims 1 to 10.

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