Battery health detection method, device and medium

Abstract

The present application provides a method, device and medium for detecting battery health, including: collecting battery-related data; defining a reference parameter group set based on the data, the set including at least the following reference parameters: battery cell voltage difference, power consumption average, battery 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 normalizing the reference parameters, and calculating the environmental correction coefficient, calibration coefficient and dynamic weight factor based on the classified data; calculating the attenuation contribution value and total attenuation value of each reference parameter based on the normalized data, environmental correction coefficient, calibration coefficient and dynamic weight factor; calculating the battery health value based on the total attenuation value. The embodiment of the present application does not require disassembly of the battery pack, and the detection operation is simple and quick. In addition, the calculation process adopts a multi-parameter coupling mechanism, and the detection accuracy is high.

Description

Technical Field

[0001] The present application relates to the field of battery detection technology, and specifically to a battery health detection method, device, and medium. Background Art

[0002] With the continuous development of new energy technologies, new energy batteries have been widely used in electric vehicles, energy storage equipment and other fields. As a core component of new energy, the performance and life of batteries are affected by many factors, such as the number of charge and discharge times, temperature, current, etc. In order to ensure the safe and reliable operation of new energy batteries, the detection of their health status becomes particularly important.

[0003] Traditional battery state of health (SOH) testing methods have numerous shortcomings and lack unified market standards. These include the need to disassemble the battery pack, testing times of dozens of hours, and poor accuracy. Furthermore, existing testing equipment is often bulky and complex to operate, making it inconvenient for on-site testing and maintenance. Therefore, there is an urgent need for a fast and convenient SOH testing method to meet the needs of new energy battery testing and battery pack residual value estimation. Summary of the Invention

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

[0005] To solve the above technical problems, an embodiment of the present application provides a method for detecting battery health, including: collecting battery-related data; defining a set of benchmark parameter groups based on the data, the set including at least the following benchmark parameters: cell voltage difference, power consumption average, 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 arranging the benchmark parameters, and calculating the environmental correction coefficient, calibration coefficient, and dynamic weight factor based on the classified data; normalizing the benchmark parameters; calculating the attenuation contribution value of each benchmark parameter based on the normalized data, the environmental correction coefficient, the calibration coefficient, and the dynamic weight factor; calculating the total attenuation value based on the attenuation contribution value of each benchmark parameter; and calculating the battery health value based on the total attenuation value.

[0006] In one embodiment, before defining the reference parameter set according to the data, the method 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 embodiment, the definition of the reference parameter set includes: presetting the nominal maximum and minimum values ​​of each reference parameter; the formula for normalizing the reference parameters is as follows:

[0008]

[0009] in, For the The value of the item parameter, For the The normalized value of the parameter, and Respectively The nominal maximum and minimum values ​​of the item parameter.

[0010] In one embodiment, the calculation formula of the environmental correction coefficient is:

[0011]

[0012] Where k is the temperature response coefficient (0.1≤k≤0.5), is the ambient temperature, is the standard reference temperature.

[0013] In one embodiment, the dynamic weight factor is calculated as follows:

[0014]

[0015] in 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.

[0016] In one embodiment, the formula for calculating the attenuation contribution value of each reference parameter is:

[0017]

[0018] in, is the attenuation contribution value of each benchmark parameter, is the calibration coefficient, is the dynamic weight factor, is the environmental correction factor.

[0019] In one embodiment, when the reference parameter for calculating the attenuation contribution value is the accumulated mileage, the mileage intensity factor is coupled. Expressed as:

[0020] in, is the mileage attenuation coefficient, D is the accumulated mileage, is a constant;

[0021] The formula for the attenuation contribution value of the cumulative mileage is:

[0022]

[0023] in, is the calibration coefficient of the accumulated mileage D, is the dynamic weight factor of the accumulated mileage D, is the environmental correction coefficient of the cumulative mileage D, is the normalized cumulative mileage D.

[0024] In one embodiment, the calculation formula for calculating the total attenuation value based on the attenuation contribution value of each reference parameter is:

[0025]

[0026] in, 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 strings S, As the benchmark parameter 、 And the attenuation contribution value of SOC.

[0027] In one embodiment, the calculation formula of the battery health value SOH is:

[0028]

[0029] in is the attenuation threshold, satisfy:

[0030]

[0031] in, is the battery threshold adjustment factor.

[0032] In one embodiment, the battery health detection method further includes: outputting the battery health value result in a preset format and generating a detection report.

[0033] An embodiment of the present application also provides a battery health detection device, which is applied to the above-mentioned battery health detection method, including: a data acquisition module, used to collect battery-related data; a set definition module, used to define a benchmark parameter group set based on the data, and the set includes at least the following benchmark parameters: battery cell voltage difference, power consumption average, battery cell temperature difference, cumulative mileage, battery calendar life, cumulative number of cycles, battery material coefficient, number of battery combination strings and state of charge; a classification and sorting module, used to classify and sort the benchmark parameters; a normalization module, used to normalize the benchmark parameters; a calculation module, used to calculate the environmental correction coefficient, calibration coefficient and dynamic weight factor based on the classified and sorted data; calculate the attenuation contribution value of each benchmark parameter based on the normalized data, the environmental correction coefficient, the calibration coefficient and the dynamic weight factor; calculate the total attenuation value based on the attenuation contribution value of each benchmark parameter; and calculate the battery health value based on the total attenuation value.

[0034] In one embodiment, the calculation module is further used to generate a test report based on the calculation results; the detection device also includes an output module; the output module is used to output the battery health value result and the test report in a preset format.

[0035] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned battery health detection method when executed by a processor.

[0036] This embodiment of the application collects battery-related information and uses relevant algorithms and custom variables to calculate the final battery health value without disassembling the battery pack, making the operation simple and quick. In addition, the calculation process uses a multi-parameter coupling mechanism, which has high detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings. These exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements. Unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0038] Figure 1 is a flow chart of a method for detecting battery health according to one embodiment of the present application;

[0039] Figure 2 is a flow chart of a method for detecting battery health according to another embodiment of the present application;

[0040] Figure 3 1 is a schematic structural diagram of a battery health detection device according to an embodiment of the present application;

[0041] Figure 4 is a structural diagram of a battery health detection device according to another embodiment of the present application;

[0042] Figure 5 This is a product schematic diagram of a battery health detection device according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents 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 embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and 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 work are within the scope of protection of this application.

[0044] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, it should be understood by those skilled in the art that an 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 aspect described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0045] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0046] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples, however, one skilled in the art will appreciate that the examples can be practiced without these specific details.

[0047] At present, in view of the problems in existing technologies such as complex battery health detection operation, long detection time and poor accuracy, there is an urgent need for a method that can quickly, conveniently and accurately detect battery health to meet the needs of new energy battery detection and battery pack residual value estimation.

[0048] Based on this, the embodiment of the present application proposes a method for detecting battery health, the process of which is as follows: Figure 1 As shown in , the details are as follows:

[0049] In step 101, battery-related data is collected. Specifically, there are multiple ways to collect data in the embodiment of the present application, such as establishing communication with the vehicle's fast charging port or a dedicated interface and the battery management system (BMS) to automatically obtain battery-related parameters (such as cell voltage difference ( ), temperature difference ( ), state of charge (SOC); charge and discharge energy, current, cumulative number of cycles ( ), accumulated mileage D, etc.); obtain manufacturer and battery model information by parsing the vehicle identification code or supplement key data such as battery specifications and production date by scanning or manually entering nameplate information; directly read real-time data (such as state of charge SOC, voltage, temperature, etc.) from the vehicle instrument or central control system; measure in real time through external sensors; or use other supplementary collection methods.

[0050] The data collected in the embodiments of the present application include but are not limited to the following parameters: battery manufacturer, battery material coefficient, installation method, series-parallel mode, number of battery combination strings, cell type, ideal life, ideal number of cycles, ideal voltage and pressure difference, battery calendar life, balancing mode, fast / slow charging ratio, cumulative number of cycles, cumulative mileage, average power consumption (kWh / 100km), common areas, cell calibrated capacity / voltage / energy, total calibrated capacity / voltage / energy, single cell voltage / temperature extremes, state of charge SOC and other parameters.

[0051] In step 102, a reference parameter set is defined based on the data. The reference parameter set defined in the embodiment of the present application includes at least the following reference parameters: cell voltage difference , average power consumption , battery cell temperature difference , accumulated mileage D, battery calendar life , cumulative number of cycles , battery material coefficient Q, battery combination string number S and state of charge SOC index. The parameter group set defined in this embodiment can be recorded as ,in They correspond to the above-mentioned benchmark parameters respectively, and the nominal maximum and minimum values ​​of each benchmark parameter can be preset when defining the benchmark parameter group set, for example:

[0052] State of charge SOC: =0, =100(%);

[0053] Accumulated mileage D: =0, =Constant(km);

[0054] Number of battery strings S: =1, =200(string);

[0055] Battery material coefficient Q: =0.7, =1.0;

[0056] 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 this application.

[0057] 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 data. Optionally, the calculation formula of the environmental correction coefficient is:

[0058] (1)

[0059] Where k is the temperature response coefficient (0.1≤k≤0.5), is the ambient temperature, is the standard reference temperature. Environmental correction factor It is used to quantify the impact of environmental conditions (such as temperature) on the rate of battery parameter attenuation, dynamically adjust the parameter attenuation contribution value under different environments, and make the battery health test results closer to the actual aging situation.

[0060] Optionally, the calculation formula of the dynamic weight factor is:

[0061] (2)

[0062] in 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.

[0063] By adjusting these parameters, it is possible to adapt to different battery types (such as power batteries vs. energy storage batteries) and usage scenarios (such as taxis vs. family cars) to achieve accurate health assessment.

[0064] The definitions of the empirical coefficients are shown in Table 1.

[0065] Table 1: Definition of parameters

[0066]

[0067] The battery material coefficient Q can be found in Table 2.

[0068] Table 2: Battery material coefficient and its physical properties

[0069]

[0070] It should be noted that the specific numerical values ​​in Table 1 and Table 2 are only for illustrative purposes and may be determined according to actual conditions and are not limited in the embodiments of this application.

[0071] In step 104, the reference parameters are normalized. Specifically, the reference parameters can be normalized using the following formula:

[0072] (3)

[0073] in, For the The value of the item parameter, For the The normalized value of the parameter, and Respectively The nominal maximum and minimum values ​​of the item parameter.

[0074] This embodiment provides a method for normalizing the reference parameters. However, other methods may also be used in actual applications and are not limited in this embodiment. By normalizing the reference parameters, data of different dimensions and orders of magnitude can be converted to the same scale, eliminating the dimensional effects between data features, facilitating comprehensive comparative evaluation, and improving the speed and accuracy of data processing.

[0075] In step 105, the attenuation contribution value of each reference parameter is calculated based on the normalized data, the environmental 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 using formula (4):

[0076] (4)

[0077] in, is the calibration coefficient, which can be obtained through experiments or neural network training; is the dynamic weight factor, which can be calculated by formula 2; is the environmental correction factor, which can be calculated using Formula 1.

[0078] Preferably, in order to make the calculated attenuation contribution value more accurate, when the reference parameter for calculating the attenuation contribution value is the accumulated mileage D, the mileage intensity factor can be coupled. Expressed as: ,in, is the mileage attenuation coefficient, the default value can be set to 0.8, is a constant; specific The value can be determined based on the product of the maximum mileage and the battery material coefficient Q.

[0079] The formula for the attenuation contribution value of the cumulative mileage D is:

[0080] (5)

[0081] in, is the calibration coefficient of the accumulated mileage D, is the dynamic weight factor of the accumulated mileage D, is the environmental correction coefficient of the cumulative mileage D, is the normalized cumulative mileage D.

[0082] In step 106, a total attenuation value is calculated based on the attenuation contribution values ​​of the respective reference parameters. Specifically, the total attenuation value can be calculated based on the attenuation contribution values ​​of the respective reference parameters and the battery combination string number coupling adjustment factor. The formula for calculating the total attenuation value is:

[0083] (6)

[0084] in, is the cell voltage difference, is the cell temperature difference, SOC is the state of charge, As the benchmark parameter 、 And the attenuation contribution value of SOC, is the coupling adjustment factor related to the number of battery strings S, , is a basic coupling factor, which can be obtained through support vector machine (SVM) or neural network training, for example, 0.15.

[0085] The coupling mechanism is: when S ≤ 100, , has little effect on the total attenuation value Y. When S>100, As S increases significantly. That is, when S>100, the cell voltage difference can be amplified ( ), battery cell temperature difference ( ), the synergistic degradation effect of the state of charge (SOC) can more accurately reflect the attenuation characteristics of a large number of battery packs, and is suitable for battery health SOH detection scenarios of high-number battery systems in new energy vehicles.

[0086] In step 107, the battery health value is calculated based on the total attenuation value. Specifically, the battery health value can be calculated based on the total attenuation value and the battery threshold adjustment factor. The formula for calculating the battery health value is as follows:

[0087] (7)

[0088] in is the attenuation threshold, satisfying:

[0089]

[0090] in, is the theoretical maximum value of the normalized parameter, is the battery threshold adjustment factor, , is the compensation coefficient, for example By introducing the battery threshold adjustment factor It can dynamically correct the differences in attenuation characteristics of different battery material coefficients, ensure the compatibility and accuracy of different battery materials, and solve the problem of "threshold rigidity" in the traditional battery health SOH model.

[0091] This embodiment of the application collects battery-related information and uses relevant algorithms and custom variables to calculate the final battery health value. The detection process does not require disassembling the battery pack, making the operation simple and quick. In addition, the calculation process uses a multi-parameter coupling mechanism, which has high detection accuracy.

[0092] In an optional embodiment, after collecting battery-related data and before defining a set of baseline parameter groups based on the data, the collected data can also be filtered, fine-tuned, and corrected, and data fluctuations beyond a preset fluctuation range can be suppressed (for example, suppressing data fluctuations with a fluctuation range ≥ 30%) to correct data drift.

[0093] In another optional embodiment, the battery health detection method further includes: outputting the battery health value result in a preset format and generating a detection report, the process of which is as follows: Figure 2 As shown in , the details are as follows:

[0094] Steps 201 to 207 in this embodiment are Figure 1 Steps 101 to 107 of the illustrated embodiment are similar and will not be described in detail here.

[0095] In step 208, the battery health value results are output in a preset format and a test report is generated. In this embodiment, the battery health (SOH) results can be output in a unified output format (e.g., dd.dd%) to avoid ambiguity in manual interpretation and facilitate automated system processing. The test report in this embodiment can be generated based on the calculation results. The generated test report can include test results (e.g., current health: 82.3%), health status rating, grading labels (e.g., good (80%-90%), warning (<70%)), decay analysis, maintenance recommendations, risk warnings, and other content to help customers quickly understand the battery status and formulate charging, discharging, or maintenance strategies.

[0096] Based on the same inventive concept, this application also provides a battery health detection device. It should be noted that the device illustrated below is an example of a device corresponding to the above method embodiment, and in other device embodiments, the settings of unit module functions and module quantity can be set accordingly based on the above method embodiment.

[0097] like Figure 3 As shown, the battery health detection device includes: a data acquisition module 1, which is used to collect battery-related data; a set definition module 2, which is used to define a benchmark parameter group set based on the data, and the set includes at least the following benchmark parameters: battery cell voltage difference, power consumption average, battery cell temperature difference, cumulative mileage, battery calendar life, cumulative number of cycles, battery material coefficient, number of battery combination strings and state of charge; a classification and sorting module 3, which is used to classify and sort the benchmark parameters; a normalization module 4, which normalizes the benchmark parameters; a calculation module 5, which is used to calculate the environmental correction coefficient, calibration coefficient and dynamic weight factor based on the classified and sorted data; calculate the attenuation contribution value of each benchmark parameter based on the normalized data, the environmental correction coefficient, the calibration coefficient and the dynamic weight factor; calculate the total attenuation value based on the attenuation contribution value of each benchmark parameter; and calculate the battery health value based on the total attenuation value.

[0098] This embodiment of the application uses a data acquisition module 1 to collect battery-related information, applies relevant algorithms and custom variables, and calculates the final battery health value through a calculation module 5. This does not require disassembling the battery pack, making the operation simple and quick. In addition, the calculation process uses a multi-parameter coupling mechanism, which provides high detection accuracy.

[0099] In another embodiment of the present application, Figure 4 As shown, the battery health detection device also includes an output module 6, and the calculation module 5 is also used to generate a detection report based on the calculation results; the output module 6 is used to output the battery health value results and the detection report in a preset format to help customers quickly understand the battery status and formulate charging, discharging or maintenance strategies.

[0100] In one application scenario, refer to Figure 4 and Figure 5 The battery health detection device of the embodiment of the present application is as follows Figure 5 As shown in the figure, a detection gun head 51 is installed at the front of the device, and a circuit board 52 is installed 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 supplies power to the entire device.

[0101] When using this testing device to test the battery health (SOH), the device's switch 54 is first turned on. The test gun head 51 establishes a communication connection with the vehicle's battery management system (BMS) via the new energy vehicle's fast charging port. Battery-related data is then collected and sent to the circuit board 52. The circuit board 52 processes the data, calculating relevant data using a linear regression model or a neural network model. The calculation results are then output to the display module via a communication unit (e.g., output module 6) for display, allowing the user to intuitively view the battery health value and test report. Optionally, the communication unit can also be a wireless transmission module, such as a Bluetooth module supporting the Bluetooth Low Energy (BLE) protocol. The calculation results can be output to an external terminal device via the wireless transmission unit, allowing the battery health value and test report to be viewed via the external terminal device. In this embodiment of the present application, the collected battery-related data can also include manually entered supplementary battery information. Based on this supplementary information, the battery health value is calculated and a battery health management improvement plan is provided. This helps customers quickly understand the battery status and formulate charging, discharging, or maintenance strategies.

[0102] An 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, the steps of the battery health detection method described in any embodiment of the present application are implemented.

[0103] It should be noted that the computer storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In a possible embodiment, the present invention may also provide a method of implementing data processing in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to perform several steps of the method described in any of the aforementioned embodiments.

[0104] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for detecting battery health, characterized in that: include: Collect battery-related data; A reference parameter set is defined based on the data, and the set includes at least the following reference parameters: cell voltage difference , average power consumption, battery cell temperature difference, accumulated mileage, battery calendar life , cumulative number of cycles , battery material coefficient Q, number of battery combination strings and state of charge; Classifying and arranging the reference parameters, and calculating environmental correction coefficients, calibration coefficients, and dynamic weighting factors based on the classified and arranged data; performing normalization processing on 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; Calculate the total attenuation value based on the attenuation contribution value of each reference parameter; Calculating a battery health value based on the total attenuation value; Wherein, the environmental correction factor The calculation formula is: k is the temperature response coefficient (0.1≤k≤0.5), is the ambient temperature, is the standard reference temperature; The dynamic weight factor The calculation formula is: , is the empirical coefficient; Calculate the attenuation contribution of each benchmark parameter The formula is: , is the calibration coefficient, For the The normalized value of the parameter.

2. The battery health detection method according to claim 1, characterized in that: Before defining a reference parameter set according to the data, the method further includes: The collected data is filtered, fine-tuned and corrected, and data fluctuations outside the preset fluctuation range are suppressed to correct data drift.

3. The battery health detection method according to claim 1, characterized in that: The defining of the reference parameter set includes: presetting the nominal maximum value and minimum value of each reference parameter; The formula for normalizing the reference parameters is as follows: in, For the The value of the item parameter, For the The normalized value of the parameter, and Respectively The nominal maximum and minimum values ​​of the item parameter.

4. The battery health detection method according to claim 1, characterized in that: When the base parameter for calculating the attenuation contribution value is the accumulated mileage, the coupling of the mileage intensity factor is added. The mileage intensity factor Expressed as: in, is the mileage attenuation coefficient, D is the accumulated mileage, is a constant; The formula for the attenuation contribution value of the cumulative mileage is: in, is the calibration coefficient of the accumulated mileage D, is the dynamic weight factor of the accumulated mileage D, is the environmental correction coefficient of the cumulative mileage D, is the normalized cumulative mileage D.

5. The battery health detection method according to claim 1, characterized in that: The calculation formula for calculating the total attenuation value based on the attenuation contribution value of each reference parameter is: in, 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 strings S, As the benchmark parameter 、 And the attenuation contribution value of SOC.

6. The battery health detection method according to claim 5, characterized in that: The battery health value The calculation formula is: in is the attenuation threshold, satisfying: in, is the battery threshold adjustment factor.

7. The battery health detection method according to claim 1, characterized in that: Also includes: Output the battery health value results in a preset format and generate a test report.

8. A battery health detection device, applied to the battery health detection method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to collect battery-related data; a set definition module, configured to define a reference parameter group set based on the data, the set including at least the following reference parameters: battery cell voltage difference, power consumption mean, battery cell temperature difference, cumulative mileage, battery calendar life, cumulative number of cycles, battery material coefficient, number of battery combination strings, and state of charge; A classification and arrangement module, used for classifying and arranging the reference parameters; A normalization module, configured to perform normalization processing on the reference parameters; A calculation module is used to calculate the environmental correction coefficient, calibration coefficient and dynamic weight factor based on the classified and organized 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; and calculating the total attenuation value according to the attenuation contribution value of each reference parameter; And the battery health value is calculated according to the total attenuation value.

9. The battery health detection device according to claim 8, characterized in that: The calculation module is also used to generate a test report based on the calculation results; The detection device also includes an output module; The output module is used to output the battery health value result and the test report in a preset format.

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 battery health detection method according to any one of claims 1 to 7 are implemented.

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