A method, device and storage medium for detecting the health of a battery

Abstract

The application relates to a battery health degree detection method, equipment and a storage medium. The health degree detection method comprises the following steps: firstly, detecting the battery based on an SOC-OCV curve and a charging condition to obtain a first health degree of the battery; then, detecting the battery based on a use time of the battery to obtain a second health degree of the battery; finally, obtaining a precise health degree of the battery according to the first health degree or obtaining the precise health degree of the battery according to the first health degree and the second health degree. Therefore, the battery health degree accuracy is improved, the battery is prevented from being used beyond the limit, the battery safety is improved, and the battery system life cycle is ensured or prolonged.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus and storage medium for detecting the health of a battery. Background Technology

[0002] Currently, vehicles, especially commercial vehicles and engineering vehicles, generally do not pay enough attention to battery health. Battery health is usually judged by the battery's lifespan or the range on a single charge.

[0003] While existing testing methods are easy to implement, due to the limitations of battery test data and the simplistic calculation methods, the health status calculated by these methods may deviate significantly from the actual health status of the battery system. This could lead to the battery being used beyond its limits, thereby accelerating battery degradation and even causing safety issues. Summary of the Invention

[0004] Therefore, it is necessary to provide a battery health detection method, device, and storage medium that can accurately detect battery health in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for detecting the health of a battery. The method includes:

[0006] The battery is tested based on the SOC-OCV curve and charging conditions to obtain the battery's first health status;

[0007] The battery is tested based on its usage time to obtain a second health status of the battery;

[0008] The precise health status of the battery can be obtained based on the first health status, or based on the first health status and the second health status.

[0009] In one embodiment, the step of detecting the battery based on the SOC-OCV curve and charging conditions to obtain the battery's first health status further includes:

[0010] The SOC-OCV curve is triggered to calibrate the battery voltage;

[0011] When the battery charging condition meets the preset conditions, the current voltage is obtained based on the voltage calibrated by the SOC-OCV and the charging capacity;

[0012] The first health level is obtained based on the current voltage and the reference voltage.

[0013] In one embodiment, when the battery charging condition is detected to meet preset conditions, the current voltage is obtained based on the voltage and charging capacity calibrated by the SOC-OCV, further comprising:

[0014] When the battery charging condition is detected to meet the full charge condition, the current voltage is obtained based on the voltage calibrated by the SOC-OCV and the charging capacity;

[0015] The step of obtaining the first health status based on the current voltage and the reference voltage further includes:

[0016] The first health status is obtained based on the current voltage and the rated voltage capacity.

[0017] In one embodiment, obtaining the first health status based on the current voltage and the rated voltage capacity further includes:

[0018] The first health status is the ratio of the current voltage to the rated voltage capacity.

[0019] In one embodiment, the step of detecting the battery based on battery usage time to obtain a second health status of the battery includes:

[0020] Obtain the battery usage time;

[0021] The second health level is obtained from the time and health level mapping table.

[0022] In one embodiment, obtaining the precise health status of the battery based on the first health status or the first health status and the second health status further includes:

[0023] Determine whether the battery usage time has reached a preset time threshold;

[0024] When it is determined that the time has reached a preset time threshold, the accurate health status of the battery is obtained based on the first health status and the second health status.

[0025] When it is determined that the time has not reached the preset time threshold, the accurate health status of the battery is obtained based on the first health status.

[0026] In one embodiment, obtaining the precise health status of the battery based on the first health status further includes: using the first health status as the precise health status of the battery;

[0027] The step of obtaining the precise health status of the battery based on the first health status and the second health status further includes:

[0028] The precise health status of the battery is obtained based on the weighted ratio of the first health status and the second health status.

[0029] In one embodiment, obtaining the precise health status of the battery based on the weighted ratio of the first health status and the second health status further includes:

[0030] The first health level, the second health level, and the precise health level of the battery satisfy the following relationship:

[0031] SOH=SOH-01*(SOH-01 / (SOH-01+SOH-02))+SOH-02*(SOH-02 / (SOH-01+SOH-02));

[0032] Wherein, SOH represents the precise health status of the battery, SOH-01 represents the first health status, and SOH-02 represents the second health status.

[0033] Secondly, this application also provides a battery health detection device. It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0034] Thirdly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method described above.

[0035] The above discloses a battery health detection method, device, and storage medium. The health detection method includes the following steps: first, detecting the battery based on the SOC-OCV curve and charging conditions to obtain a first health level; then, detecting the battery based on its usage time to obtain a second health level; finally, obtaining the precise health level of the battery based on the first health level, or based on both the first and second health levels. Therefore, firstly, the SOC-OCV curve and charging conditions yield a highly accurate first health level, and then the precise health level is obtained based on the first health level. Because the first health level is obtained by combining the SOC-OCV curve and charging conditions, a highly accurate precise health level can be obtained. Furthermore, a second health level based on battery usage time can be further obtained, and then the precise health level can be obtained by combining the first and second health levels, improving the accuracy of battery health detection, preventing the battery from exceeding its usage limits, thereby improving battery safety and ensuring or even extending the battery system's lifespan. Attached Figure Description

[0036] Figure 1 This is an application environment diagram of a battery health detection method in one embodiment;

[0037] Figure 2 This is a flowchart illustrating a battery health detection method in one embodiment;

[0038] Figure 3 This is a flowchart illustrating a battery health detection method in one embodiment;

[0039] Figure 4 This is a flowchart illustrating a battery health detection method in one embodiment;

[0040] Figure 5 This is a structural block diagram of a battery health detection device in one embodiment;

[0041] Figure 6 This is an internal structural diagram of a battery health detection device in one embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] This application provides a battery health detection method that can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed in the cloud or on other network servers. Terminal 102 detects the battery based on the SOC-OCV curve and charging conditions to obtain a first health level, and further detects the battery based on its usage time to obtain a second health level. Finally, it obtains the precise health level of the battery based on the first health level, or based on both the first and second health levels. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0044] In one embodiment, such as Figure 2 As shown, a method for detecting the health of a battery is provided, which can be applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps:

[0045] Step 202: Detect the battery based on the SOC-OCV curve and charging conditions to obtain the battery's first health status.

[0046] Among them, the SOC-OCV curve is a very important curve in the SOC (State of Charge, the current remaining capacity of the battery or the state of charge of the battery) calibration process. Usually, after the electric vehicle has been running for a period of time, before restarting the vehicle after it has been stationary, this curve is called to correct the SOC value and obtain an updated SOC value through a preset algorithm and other correction coefficients.

[0047] When a battery system is discharged to a certain depth and left to stand for a period of time, the SOC value can be calibrated using the SOC-OCV curve. Currently, the mainstream lithium batteries on the market include two types: ternary lithium batteries and lithium iron phosphate batteries. Ternary lithium batteries have better linearity in their charge-discharge voltage curves, and a relatively accurate SOC value can be obtained within the usage range through resting correction. Lithium iron phosphate batteries have poorer linearity in their charge-discharge voltage curves, exhibiting a plateau range where the voltage values ​​differ little between 25% and 90%, making it impossible to correct using the SOC-OCV curve. Therefore, the SOC-OCV range for lithium iron phosphate should be selected below 25% (3.2V). Based on the above selection of the SOC-OCV range, during vehicle use, after the battery has been left to stand for 2 hours, the true SOC value can be calculated using the SOC-OCV curve.

[0048] In other words, before this step, the battery's resting time will be calculated, and the SOC-OCV calibration will only be started after the resting time reaches a preset time threshold, such as 2 hours.

[0049] Furthermore, after starting SOC-OCV for calibration, the battery is marked for charging, and the charging status is recorded in real time.

[0050] Step 204: Detect the battery based on its usage time to obtain the battery's second health status.

[0051] Specifically, the battery's usage time is first obtained, and then the second health status is obtained from the usage time and health status mapping table based on the usage time. It is understood that the usage time and health status mapping table is pre-set, and the mapping relationship is different for each battery specification. Therefore, in practical applications, a usage time and health status mapping table needs to be stored for batteries of the same specification.

[0052] The usage time can be calculated from the battery's cycle data, i.e., the cumulative discharge ampere-hours.

[0053] Step 206: Obtain the precise health status of the battery based on the first health status, or obtain the precise health status of the battery based on the first health status and the second health status.

[0054] Therefore, a highly accurate first health level is obtained first from the SOC-OCV curve and charging conditions. Then, the precise health level of the battery is obtained based on the first health level, as the first health level is obtained by combining the SOC-OCV curve and charging conditions, thus achieving a highly accurate health level. Furthermore, a second health level based on battery usage time can be obtained. Then, the first and second health levels are combined to obtain the precise battery health level. This method combines battery cycle parameters, SOC-OCV correction, and charging conditions to calculate battery health, improving the accuracy of battery health assessment, preventing battery overuse, improving battery safety, and ensuring or even extending the battery system's lifespan.

[0055] In one embodiment, such as Figure 3 As shown, step S202 above also includes:

[0056] Step 302: Trigger the SOC-OCV curve to calibrate the battery voltage.

[0057] Step 304: When the battery charging condition meets the preset conditions, the current voltage is obtained based on the voltage and charging capacity calibrated by the SOC-OCV.

[0058] As mentioned above, the linearity of the charge-discharge voltage curve of lithium iron phosphate is poor, with a plateau range. The voltage values ​​in the 25%-90% range are small and cannot be corrected using the SOC-OCV curve. Therefore, the charging conditions need to meet preset conditions. In one embodiment, the charging conditions can be either the charging condition that meets 90% or the charging condition that meets full charge.

[0059] Therefore, this step specifically involves determining the current voltage based on the voltage calibrated by SOC-OCV and the charging capacity when the battery charging condition meets the full charge condition. Specifically, first, the battery's SOC is calibrated using SOC-OCV; then, during charging, the cumulative charging ampere-hours are recorded, and the calibrated SOC value is added to obtain the current voltage value.

[0060] Step 306: Obtain the first health status based on the current voltage and the reference voltage.

[0061] In one embodiment, this step may obtain the first health status based on the current voltage and the rated voltage capacity. Specifically, the first health status may be the ratio of the current voltage to the rated voltage capacity.

[0062] In this embodiment, the SOC of the battery is first calibrated by SOC-OCV to obtain an accurate SOC value. Then, the battery is charged to a preset charging condition, and the amount of charge is obtained based on the charging ampere-hours. The current voltage is then obtained based on the SOC calibration value and the amount of charge. Finally, the first health status is obtained by comparing the current voltage with the rated voltage capacity, which can improve the accuracy of the first health status.

[0063] In one embodiment, such as Figure 4 As shown, step S206 above also includes:

[0064] Step 402: Determine whether the battery usage time has reached a preset time threshold.

[0065] This step specifically determines whether the battery's cycle data has reached a preset number, such as 2000, which means that the usage time has reached a preset time threshold.

[0066] When it is determined that the time has reached a preset time threshold, step S404 is executed; when it is determined that the time has not reached the preset time threshold, step S406 is executed.

[0067] Step 404: Obtain the precise health status of the battery based on the first health status and the second health status.

[0068] Specifically, this step involves obtaining the battery's precise health status based on the weighted ratio of the first health status and the second health status. That is, after the battery has undergone SOC-OCV calibration and full-charge calculations to obtain the first health status, and after the battery's usage time reaches a preset time threshold, the weighted ratio of the first and second health status is obtained. A weighted value for the first health status is then derived based on this weighted ratio and the first health status. A weighted value for the second health status is also derived based on this weighted ratio and the second health status. Finally, the weighted values ​​for the first and second health status are added together to obtain the battery's precise health status.

[0069] Specifically, the first health level, the second health level, and the precise health level of the battery satisfy the following relationship:

[0070] SOH = SOH-01*(SOH-01 / (SOH-01+SOH-02))+SOH-02*(SOH-02 / (SOH-01+SOH-02)), where SOH is the precise health status of the battery, SOH-01 is the first health status, and SOH-02 is the second health status.

[0071] Step 406: Obtain the precise health status of the battery based on the first health status.

[0072] In other words, after the battery has undergone SOC-OCV calibration and full-charge condition calculation to obtain the first health level, but the battery usage time has not reached the preset time threshold, the first health level is directly used as the accurate health level of the battery.

[0073] In this embodiment, different health calculation methods are selected by judging the battery usage time to obtain the value that is closest to the true health of the battery under different conditions (i.e., usage time), thereby improving the accuracy of health detection.

[0074] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0075] Based on the same inventive concept, this application also provides a battery health detection device for implementing the battery health detection method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations of one or more battery health detection device embodiments provided below can be found in the limitations of the battery health detection method described above, and will not be repeated here.

[0076] In one embodiment, such as Figure 5 As shown, a battery health detection device 500 is provided, including: a first health acquisition module 501, a second health acquisition module 502, and a precise health acquisition module 503, wherein:

[0077] The first health status acquisition module 501 is used to detect the battery based on the SOC-OCV curve and charging conditions, and acquire the first health status of the battery.

[0078] The second health status acquisition module 502 is used to detect the battery based on the battery's usage time and acquire the battery's second health status.

[0079] The precise health status acquisition module 503 is used to acquire the precise health status of the battery based on the first health status, or to acquire the precise health status of the battery based on the first health status and the second health status.

[0080] In one embodiment, the first health status acquisition module 501 further triggers the SOC-OCV curve to calibrate the battery voltage, and then detects that the battery charging condition meets the preset conditions. Based on the voltage and charging capacity calibrated by the SOC-OCV, the current voltage is obtained, and finally the first health status is obtained based on the current voltage and the reference voltage.

[0081] In one embodiment, the first health status acquisition module 501 further detects that when the battery charging condition meets the full charge condition, it obtains the current voltage based on the voltage and charging capacity calibrated by the SOC-OCV, and further obtains the first health status based on the current voltage and the rated voltage capacity.

[0082] In one embodiment, the first health status is the ratio of the current voltage to the rated voltage capacity.

[0083] In one embodiment, the second health acquisition module 502 acquires the battery usage time and then acquires the second health status from the usage time and health status mapping table.

[0084] In one embodiment, the precise health status acquisition module 503 further determines whether the battery usage time has reached a preset time threshold, and when it is determined that the time has reached the preset time threshold, it acquires the precise health status of the battery based on the first health status and the second health status; when it is determined that the time has not reached the preset time threshold, it acquires the precise health status of the battery based on the first health status.

[0085] In one embodiment, the precise health status acquisition module 503 further uses the first health status as the precise health status of the battery;

[0086] Alternatively, the precise health status of the battery can be obtained based on the weighted ratio of the first health status and the second health status.

[0087] In one embodiment, the first health level, the second health level, and the precise health level of the battery satisfy the following relationship:

[0088] SOH=SOH-01*(SOH-01 / (SOH-01+SOH-02))+SOH-02*(SOH-02 / (SOH-01+SOH-02));

[0089] Wherein, SOH represents the precise health status of the battery, SOH-01 represents the first health status, and SOH-02 represents the second health status.

[0090] Each module in the aforementioned battery health detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0091] In one embodiment, a battery health detection device is provided. This device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores battery health detection data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the battery health detection method described above.

[0092] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0093] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0094] The battery is tested based on the SOC-OCV curve and charging conditions to obtain the battery's first health status;

[0095] The battery is tested based on its usage time to obtain a second health status of the battery;

[0096] The precise health status of the battery can be obtained based on the first health status, or based on the first health status and the second health status.

[0097] In one embodiment, when the computer program is executed by a processor, it further implements the following steps: detecting the battery based on the SOC-OCV curve and charging conditions to obtain a first health status of the battery, and further includes:

[0098] The SOC-OCV curve is triggered to calibrate the battery voltage;

[0099] When the battery charging condition meets the preset conditions, the current voltage is obtained based on the voltage calibrated by the SOC-OCV and the charging capacity;

[0100] The first health level is obtained based on the current voltage and the reference voltage.

[0101] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: when the battery charging condition is detected to meet preset conditions, the current voltage is obtained based on the voltage and charging capacity calibrated by the SOC-OCV, and the program further includes:

[0102] When the battery charging condition is detected to meet the full charge condition, the current voltage is obtained based on the voltage calibrated by the SOC-OCV and the charging capacity;

[0103] The step of obtaining the first health status based on the current voltage and the reference voltage further includes:

[0104] The first health status is obtained based on the current voltage and the rated voltage capacity.

[0105] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0106] The step of obtaining the first health status based on the current voltage and rated voltage capacity further includes:

[0107] The first health status is the ratio of the current voltage to the rated voltage capacity.

[0108] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0109] The process of detecting the battery based on its usage time to obtain a second health status of the battery includes:

[0110] Obtain the battery usage time;

[0111] The second health level is obtained from the time and health level mapping table.

[0112] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0113] The step of obtaining the precise health status of the battery based on the first health status, or obtaining the precise health status of the battery based on the first health status and the second health status, further includes:

[0114] Determine whether the battery usage time has reached a preset time threshold;

[0115] When it is determined that the time has reached a preset time threshold, the accurate health status of the battery is obtained based on the first health status and the second health status.

[0116] When it is determined that the time has not reached the preset time threshold, the accurate health status of the battery is obtained based on the first health status.

[0117] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0118] The step of obtaining the precise health status of the battery based on the first health status includes: using the first health status as the precise health status of the battery;

[0119] The step of obtaining the precise health status of the battery based on the first health status and the second health status further includes:

[0120] The precise health status of the battery is obtained based on the weighted ratio of the first health status and the second health status.

[0121] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0122] The step of obtaining the precise health status of the battery based on the weighted ratio of the first health status and the second health status further includes:

[0123] The first health level, the second health level, and the precise health level of the battery satisfy the following relationship:

[0124] SOH=SOH-01*(SOH-01 / (SOH-01+SOH-02))+SOH-02*(SOH-02 / (SOH-01+SOH-02));

[0125] Wherein, SOH represents the precise health status of the battery, SOH-01 represents the first health status, and SOH-02 represents the second health status.

[0126] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0127] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for detecting the health of a battery, characterized in that, The method includes: The battery is tested based on the SOC-OCV curve and charging conditions to obtain the battery's first health status, including: The SOC-OCV curve is triggered to calibrate the battery voltage; When the battery charging condition is detected to meet the preset conditions, the current voltage is obtained based on the voltage calibrated by the SOC-OCV and the charging capacity, including: when the battery charging condition is detected to meet the full charge condition, the current voltage is obtained based on the voltage calibrated by the SOC-OCV and the charging capacity. The method of obtaining the first health status based on the current voltage and the reference voltage further includes: obtaining the first health status based on the current voltage and the rated voltage capacity, wherein the first health status is the ratio of the current voltage to the rated voltage capacity; The battery is tested based on its usage time to obtain a second health status of the battery; The precise health status of the battery can be obtained based on the first health status, or based on the first health status and the second health status.

2. The method according to claim 1, characterized in that, The process of detecting the battery based on its usage time to obtain a second health status of the battery includes: Obtain the battery usage time; The second health level is obtained from the usage time and the health level mapping table.

3. The method according to claim 2, characterized in that, The step of obtaining the precise health status of the battery based on the first health status or the first health status and the second health status further includes: Determine whether the battery usage time has reached a preset time threshold; When it is determined that the time has reached a preset time threshold, the accurate health status of the battery is obtained based on the first health status and the second health status. When it is determined that the time has not reached the preset time threshold, the accurate health status of the battery is obtained based on the first health status.

4. The method according to claim 3, characterized in that, The step of obtaining the precise health status of the battery based on the first health status further includes: using the first health status as the precise health status of the battery; The step of obtaining the precise health status of the battery based on the first health status and the second health status further includes: The precise health status of the battery is obtained based on the weighted ratio of the first health status and the second health status.

5. The method according to claim 4, characterized in that, The step of obtaining the precise health status of the battery based on the weighted ratio of the first health status and the second health status further includes: The first health level, the second health level, and the precise health level of the battery satisfy the following relationship: SOH=SOH-01*(SOH-01 / (SOH-01+SOH-02))+SOH-02*(SOH-02 / (SOH-01+SOH-02)); Wherein, SOH represents the precise health status of the battery, SOH-01 represents the first health status, and SOH-02 represents the second health status.

6. A battery health detection device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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