Battery health degree determination method and apparatus, and electronic device

Abstract

The invention discloses a method and device for determining the health degree of a battery and electronic equipment. The method for determining the health degree of the battery comprises the steps of obtaining actual self-power-consumption data of the battery; based on the actual self-power consumption data and initial self-power consumption data of the battery, the self-power consumption change rate of the battery is determined, and the initial self-power consumption data is the self-power consumption data of the battery under the condition that the cycle index is smaller than a preset threshold value; the self-power-consumption change rate is input into a target model, the battery health degree corresponding to the self-power-consumption change rate and output by the target model is obtained, and the target model is used for outputting the corresponding battery health degree according to the input battery self-power-consumption change rate.

Description

Technical Field

[0001] This application relates to the field of batteries, and more particularly to a method, apparatus, and electronic device for determining battery health. Background Technology

[0002] In related technologies, battery life (i.e., battery health) is usually estimated by the cycle measurement method. However, estimating battery life by this cycle measurement method requires multiple deep charge-discharge cycles (e.g., from 100% to 0%), which consumes battery cycle life (estimated to lose 0.1% to 0.3% per test). Therefore, estimating battery life by the cycle measurement method will affect the battery's lifespan. Summary of the Invention

[0003] This application discloses a method, apparatus, and electronic device for determining battery health, which can reduce the impact on battery life.

[0004] To solve the above problems, this application adopts the following technical solution: In a first aspect, embodiments of this application disclose a method for determining battery health, comprising: acquiring actual self-consumption data of the battery; determining the self-consumption change rate of the battery based on the actual self-consumption data and the initial self-consumption data of the battery, wherein the initial self-consumption data is the self-consumption data of the battery when the number of cycles is less than a preset threshold; and obtaining the battery health corresponding to the self-consumption change rate by inputting the self-consumption change rate into a target model, wherein the target model is used to output the corresponding battery health based on the input battery self-consumption change rate.

[0005] Secondly, this application discloses a device for determining battery health, comprising: an acquisition module for acquiring actual self-consumption data of a battery; a determination module for determining the self-consumption change rate of a battery based on the actual self-consumption data and the initial self-consumption data of the battery, wherein the initial self-consumption data is the self-consumption data of the battery when the number of cycles is less than a preset threshold; and a obtaining module for obtaining the battery health corresponding to the self-consumption change rate by inputting the self-consumption change rate into a target model, wherein the target model is used to output the corresponding battery health based on the input battery self-consumption change rate.

[0006] Thirdly, embodiments of this application provide an electronic device including a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and the programs or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer-executable program or instructions, which, when executed by a computer, implement the steps of the method described in the first aspect.

[0008] Fifthly, embodiments of this application provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the steps of the method described in the first aspect.

[0009] The technical solution adopted in this application can achieve the following beneficial effects: This application provides a method for determining battery health. By acquiring the actual self-discharge data of the battery, and based on the actual self-discharge data and the initial self-discharge data of the battery, the self-discharge change rate of the battery is determined. The initial self-discharge data is the self-discharge data of the battery when the number of cycles is less than a preset threshold. Then, by inputting the self-discharge change rate into a target model, the battery health corresponding to the self-discharge change rate is obtained from the output of the target model. Since the method of this application is used to determine the battery health, it is not necessary to perform multiple deep charge-discharge cycles on the battery, which can reduce the impact on the battery life. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a method for determining battery health disclosed in an embodiment of this application; Figure 2 This is a flowchart of a model building process disclosed in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a battery disclosed in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a battery health determination device disclosed in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0012] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the electrically connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0013] The method, apparatus, and electronic device for determining battery health disclosed in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0014] This application discloses a method for determining battery health. Figure 1 This is a flowchart illustrating a method for determining battery health disclosed in an embodiment of this application. Figure 1 As shown, the method includes the following steps: S120: Obtain the actual self-discharge data of the battery.

[0015] As batteries age, their self-discharge data gradually changes, exhibiting a characteristic of gradually increasing self-discharge rate.

[0016] In this application, the actual self-discharge data of the battery cells can be obtained to determine the battery health. It should be noted that the actual self-discharge data can be obtained for each battery cycle to determine the battery health corresponding to that cycle.

[0017] S140. Based on the actual self-consumption data and the initial self-consumption data of the battery, determine the self-consumption change rate of the battery, wherein the initial self-consumption data is the self-consumption data of the battery when the number of cycles is less than a preset threshold.

[0018] For example, the preset threshold can be 10, that is, the initial self-discharge data of the battery is the self-discharge data of the battery when the number of cycles is less than 10. Generally, a battery with less than 10 cycles can be considered a fresh battery.

[0019] In this application, the current self-consumption data of the battery and the initial self-consumption data when the battery is a fresh battery are used to determine the rate of change of self-consumption of the battery compared to when it is a fresh battery. For example, the rate of change of self-consumption of the battery = initial self-consumption data / actual self-consumption data.

[0020] S160. By inputting the self-consumption rate of change into the target model, the battery health value corresponding to the self-consumption rate of change is obtained by the target model outputting the battery health value corresponding to the self-consumption rate of change.

[0021] By inputting the self-discharge rate of change into the target model, the battery health level corresponding to that rate of change is obtained from the target model's output, thus improving the efficiency of determining battery health. Furthermore, the method described in this application eliminates the need for multiple deep charge-discharge cycles, avoiding cell capacity loss, reducing the impact on battery lifespan, and providing high accuracy in determining battery health.

[0022] This application provides a method for determining battery health. By acquiring the actual self-discharge data of the battery, and based on the actual self-discharge data and the initial self-discharge data of the battery, the self-discharge change rate of the battery is determined. The initial self-discharge data is the self-discharge data of the battery when the number of cycles is less than a preset threshold. Then, by inputting the self-discharge change rate into a target model, the battery health corresponding to the self-discharge change rate is obtained from the output of the target model. Since the method of this application is used to determine the battery health, it is not necessary to perform multiple deep charge-discharge cycles on the battery, which can reduce the impact on the battery life.

[0023] In one implementation, obtaining the actual self-consumption data of the battery may include: adjusting the battery's SOC to a target SOC; allowing the battery to rest for a first resting time and obtaining the capacity change value of the battery during the first resting time; and determining the actual self-consumption data of the battery based on the capacity change value and the first resting time.

[0024] For example, the target SOC can be 50±1%. By adjusting the battery's SOC to 50±1% and then letting it stand to determine the battery's actual self-discharge data, the nonlinear effects of the high / low SOC regions can be avoided.

[0025] For example, the first resting time can be 72 hours. In this application, the voltage (mV) change curve of the battery during the first resting time can be obtained, and then the voltage change curve can be converted into a capacity (mAh) change curve to obtain the capacity change value of the battery during the first resting time. The self-consumption of the battery can be calculated through the capacity decay. Since the voltage and capacity relationship of battery cells made with different chemical materials and winding methods are also different, the voltage-capacity relationship curves of each type of battery cell can be established by measuring the voltage and capacity relationship of different types of battery cells, so as to obtain the capacity change through the voltage change. It should be noted that a high-precision voltage detection module integrated into the battery management system (BMS) can be used to record the voltage decay curve of each battery cell during resting in a constant temperature environment (±0.5℃). The sampling accuracy of this voltage sampling module is ±1mV.

[0026] For example, the actual self-discharge data of the battery can be determined by the following formula. ,in, This represents the actual self-discharge data of the battery. This represents the change in capacity during the first settling time. This is the first settling time.

[0027] It should be noted that battery self-discharge data can also be determined through other methods, such as actual measurement and parameter estimation.

[0028] In one implementation, before placing the battery in a resting state for a first resting time and obtaining the capacity change value of the battery during the first resting time, the method may further include placing the battery in a resting state for a second resting time.

[0029] In other words, when determining the battery's self-discharge data, the battery's resting time can include a second resting time and a first resting time. The battery can be rested for the second resting time first, and then rested for the first resting time. The capacity change value of the battery during the first resting time can be obtained to eliminate the influence of relaxation time.

[0030] Relaxation time refers to the time it takes for a battery to return to its equilibrium state after an external disturbance is applied. Battery relaxation causes voltage changes. In the short period after charging ends, the combined effect of battery relaxation and self-discharge leads to a voltage drop, making it difficult to calculate self-discharge.

[0031] For example, the total resting time for the battery can be 72 hours, with the first resting time starting from the 24th hour of resting and the preceding part being the second resting time.

[0032] It should be noted that the battery can be stopped from charging and discharging by using a fuel gauge, thus allowing the battery to rest.

[0033] In one implementation, determining the actual self-consumption data of the battery based on the capacity change value and the first resting time includes: determining the actual self-consumption data of the battery based on the capacity change value, the temperature coefficient, and the first resting time, wherein the temperature coefficient corresponds to the temperature data of the battery.

[0034] For example, the actual self-discharge data of the battery can be determined by the following formula. ,in, This represents the actual self-discharge data of the battery. Here, k represents the capacity change during the first settling time, and k is the temperature coefficient. This is the first settling time.

[0035] Since temperature fluctuations can cause voltage fluctuations and affect the calculation of self-consumption power, introducing a temperature coefficient k into the self-consumption power calculation formula can eliminate the interference caused by temperature changes.

[0036] It should be noted that the temperature coefficient used to determine the actual self-discharge data of the battery corresponds to the battery's temperature data. For example, when the battery temperature is 10°C, the temperature coefficient used for calculation is the temperature coefficient corresponding to 10°C; when the battery temperature is 25°C, the temperature coefficient used for calculation is the temperature coefficient corresponding to 25°C; and when the battery temperature is 40°C, the temperature coefficient used for calculation is the temperature coefficient corresponding to 40°C. For example, the temperature coefficient corresponding to 10°C can be determined based on two voltage change curves obtained by placing the battery at the same SOC under ambient temperatures of 25°C and 10°C for the same amount of time. Similarly, the temperature coefficient corresponding to 40°C can be determined based on two voltage change curves obtained by placing the battery at the same SOC under ambient temperatures of 25°C and 40°C for the same amount of time. A battery at 25°C is generally considered to be in a normal temperature state. The battery's temperature data can be detected and determined by the temperature detection module integrated in the Battery Management System (BMS).

[0037] In one implementation, before obtaining the battery health corresponding to the self-discharge rate of change by inputting the self-discharge rate of change into the target model, the method may further include: acquiring initial battery data and multiple sets of aging data, wherein the initial data includes initial maximum discharge capacity and initial self-discharge data, the initial maximum discharge capacity being the maximum discharge capacity of the battery when the number of cycles is less than a preset threshold, the aging data including the maximum discharge capacity after aging and the self-discharge data after aging, and the multiple sets of aging data corresponding to different aging cycle numbers; determining multiple sets of training data based on the initial data and the multiple sets of aging data, wherein the training data includes the battery self-discharge rate of change and battery health, the battery self-discharge rate of change being determined based on the initial self-discharge data and the self-discharge data after aging, and the battery health being determined based on the maximum discharge capacity after aging and the initial maximum discharge capacity; and iteratively training the target model to be trained using the multiple sets of training data.

[0038] like Figure 2 As shown, a target model is established using multiple cells from the same system. The specific method is as follows: S1. Perform capacity cycling on fresh batteries (i.e., batteries with fewer than a preset threshold number of cycles) to determine the initial maximum discharge capacity Cf of the fresh batteries; S2. Calculate the battery's self-discharge using the above static method, and obtain the initial self-discharge data If of the fresh battery; S3. Perform cyclic aging on the battery, for example, 100 cycles; S4. Perform capacity cycling on the aged battery to determine the maximum discharge capacity Cn of the aged battery, where n is the number of aging cycles. S5. Measure the self-dissipation of the aged battery using the static method, and obtain the self-dissipation data In of the aged battery, where n is the number of aging cycles. S6, Battery Health Status SOH = Cn / Cf; S7, Battery self-dissipation rate of change Ir = If / In; S8. Import multiple sets of SOH and Ir results into the computer for model training, establish a correlation model between battery health SOH and self-consumption rate Ir, i.e. the above target model, and establish the mapping relationship between self-consumption and capacity.

[0039] It should be noted that the input to the target model is the rate of change of battery self-discharge, and the output is the battery health, i.e., battery life.

[0040] In one implementation, after obtaining the battery health value output by the target model corresponding to the self-consumption change rate, the method may further include: determining a health value difference based on the battery health value corresponding to the current cycle and the battery health value corresponding to the previous cycle; and generating a warning message if the health value difference is greater than a first threshold, wherein the warning message is used to indicate abnormal degradation of the battery's lifespan.

[0041] In other words, if the health difference between two consecutive battery cycles exceeds the first threshold, it indicates an abnormal increase in battery self-discharge and an abnormal decrease in battery life, generating a warning message for safety alert. It should be noted that this application does not specifically limit the value of the first threshold; the specific value of the first threshold can be set according to actual needs.

[0042] In another implementation, a pre-defined relationship between battery cycle count and battery health can be established. When the battery is in its Mth cycle, based on the aforementioned scheme of this application, the first battery health corresponding to the Mth cycle (i.e., the actual battery health corresponding to the Mth cycle) is obtained through the target model. Based on the relationship between battery cycle count and battery health, the second battery health corresponding to the Mth cycle (i.e., the battery health that should correspond to the Mth cycle) is determined. If the first battery health is less than the second battery health, and the difference between the first and second battery health is greater than a set threshold, it indicates an abnormal increase in battery self-discharge and abnormal degradation of battery life, generating a warning message for safety alert. It should be noted that this application does not specifically limit the size of the set threshold; the specific value of the set threshold can be set according to actual needs.

[0043] In one implementation, after obtaining the battery health status output by the target model corresponding to the self-consumption change rate, the method may further include: generating a prompt message if the battery health status is less than or equal to a second threshold, wherein the prompt message is used to indicate battery replacement.

[0044] For example, the second threshold can be 80%, that is, when the battery health is less than or equal to 80%, a prompt message indicating that the battery should be replaced is generated.

[0045] In one implementation, after obtaining the battery health status output by the target model corresponding to the self-consumption change rate, the method may further include: displaying the battery health status. Visualizing battery life improves the user experience.

[0046] The battery health determination method of this application can be applied to, for example... Figure 3The illustrated fuel gauge chip performs calculations and uploads battery life estimates to the host computer control system in real time during actual battery use. Furthermore, the solution presented in this application does not require changes to the battery management system architecture; the voltage changes of the battery cells can be calculated using the existing battery management system, resulting in high economic efficiency, high sensitivity, and accurate calculations. Figure 3 In this context, "bank" can represent a group of battery cells.

[0047] Furthermore, by adjusting and validating the model parameters for different chemical systems, the solution proposed in this application can be applied to various batteries, such as solid-state batteries, lithium-ion batteries, and sodium-ion batteries.

[0048] This application discloses a method for determining battery health, comprising the following steps: when the battery's cycle count is less than a preset threshold (i.e., when the battery is fresh), the battery's SOC is adjusted to 50±1%, and the battery is left to stand for 72 hours to collect battery data, determining the self-discharge data of the fresh battery. After the battery ages, data is collected in the same manner to determine the self-discharge data of the aged battery. Then, based on the self-discharge data of the fresh and aged batteries, the self-discharge change rate is determined. By inputting this self-discharge change rate into a target model, the battery health, i.e., battery life, is obtained from the target model output. Since the method of this application is used to determine battery health, it is not necessary to perform multiple deep charge-discharge cycles on the battery, thus reducing the impact on battery life.

[0049] Alternatively, the health of each cell in the battery can be determined based on the above scheme, and then the minimum value among the health values ​​of each cell can be determined as the health of the battery.

[0050] The battery health determination method provided in this application can be executed by a battery health determination device. This application uses an example of a battery health determination device executing the battery health determination method to illustrate the battery health determination device provided in this application.

[0051] Figure 4 This is a schematic diagram of a battery health determination device disclosed in an embodiment of this application. Figure 4 As shown, the battery health determination device 400 includes: an acquisition module 410, a determination module 420, and a obtaining module 430.

[0052] In this application, the acquisition module 410 is used to acquire the actual self-consumption data of the battery; the determination module 420 is used to determine the self-consumption change rate of the battery based on the actual self-consumption data and the initial self-consumption data of the battery, wherein the initial self-consumption data is the self-consumption data of the battery when the number of cycles is less than a preset threshold; and the obtaining module 430 is used to obtain the battery health corresponding to the self-consumption change rate by inputting the self-consumption change rate into a target model, wherein the target model is used to output the corresponding battery health based on the input battery self-consumption change rate.

[0053] In one implementation, the acquisition module 410 acquires the actual self-consumption data of the battery, including: adjusting the battery's SOC to a target SOC; allowing the battery to rest for a first resting time and acquiring the capacity change value of the battery during the first resting time; and determining the actual self-consumption data of the battery based on the capacity change value and the first resting time.

[0054] In one implementation, the acquisition module 410 determines the actual self-consumption data of the battery based on the capacity change value and the first resting time, including: determining the actual self-consumption data of the battery based on the capacity change value, the temperature coefficient and the first resting time, wherein the temperature coefficient corresponds to the temperature data of the battery.

[0055] In one implementation, the above-mentioned device further includes: a settling module, configured to set the battery for a second settling time before setting the battery for a first settling time and obtaining the capacity change value of the battery during the first settling time.

[0056] In one implementation, the above-mentioned apparatus further includes: the acquisition module 410, which is further configured to acquire initial battery data and multiple sets of aging data before obtaining the battery health corresponding to the self-discharge rate of change by inputting the self-discharge rate of change into the target model, wherein the initial data includes initial maximum discharge capacity and initial self-discharge data, the initial maximum discharge capacity being the maximum discharge capacity of the battery when the number of cycles is less than a preset threshold, and the aging data including the maximum discharge capacity after aging and the self-discharge data after aging, wherein the number of aging cycles corresponding to the multiple sets of aging data is different; the determination module 420, which is further configured to determine multiple sets of training data based on the initial data and the multiple sets of aging data, wherein the training data includes the battery self-discharge rate of change and battery health, the battery self-discharge rate of change being determined based on the initial self-discharge data and the self-discharge data after aging, and the battery health being determined based on the maximum discharge capacity after aging and the initial maximum discharge capacity; and a training module, which is configured to iteratively train the target model to be trained using the multiple sets of training data.

[0057] In one implementation, the above-mentioned device further includes: the determining module 420, which is further configured to determine a health difference based on the battery health corresponding to the current cycle and the battery health corresponding to the previous cycle after obtaining the battery health output by the target model corresponding to the self-consumption change rate; and the generating module, which is configured to generate a warning message when the health difference is greater than a first threshold, wherein the warning message is used to indicate abnormal degradation of the battery life.

[0058] In one implementation, the above apparatus further includes: a generation module, configured to generate a prompt message when the battery health value output by the target model corresponding to the self-consumption rate is less than or equal to a second threshold after obtaining the battery health value. The prompt message is used to indicate battery replacement.

[0059] Optionally, such as Figure 5 As shown, this application embodiment also provides an electronic device 500, including a processor 501 and a memory 502. The memory 502 stores a program or instructions that can run on the processor 501. When the program or instructions are executed by the processor 501, they implement the various steps of the above-described method embodiment for determining battery health and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0060] It should be noted that the electronic devices in the embodiments of this application include mobile electronic devices and non-mobile electronic devices.

[0061] This application also provides a computer-readable storage medium storing a computer-executable program or instructions. When the computer-executable program or instructions are executed by a computer, they implement the various processes of the above-described battery health determination method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0062] The computer-readable storage medium may be a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0063] This application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the steps of the battery health determination method described above.

[0064] The above embodiments of this application focus on describing the differences between the various embodiments. As long as the different optimization features between the various embodiments are not contradictory, they can be combined to form a better embodiment. For the sake of brevity, they will not be described in detail here.

[0065] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining battery health, characterized in that, include: Obtain actual self-discharge data of the battery; Based on the actual self-consumption data and the initial self-consumption data of the battery, the self-consumption change rate of the battery is determined, wherein the initial self-consumption data is the self-consumption data of the battery when the number of cycles is less than a preset threshold. By inputting the self-consumption rate of change into the target model, the battery health level corresponding to the self-consumption rate of change is obtained from the output of the target model. The target model is used to output the corresponding battery health level based on the input battery self-consumption rate of change.

2. The determination method according to claim 1, characterized in that, The acquisition of the battery's actual self-consumption data includes: Adjust the battery's SOC to the target SOC; The battery is left to stand for a first time, and the capacity change value of the battery during the first time is obtained. Based on the capacity change value and the first resting time, the actual self-consumption data of the battery is determined.

3. The determination method according to claim 2, characterized in that, The step of determining the actual self-discharge data of the battery based on the capacity change value and the first resting time includes: Based on the capacity change value, temperature coefficient, and the first resting time, the actual self-consumption data of the battery is determined, wherein the temperature coefficient corresponds to the temperature data of the battery.

4. The determination method according to claim 2, characterized in that, Before placing the battery in a resting state for a first resting time and obtaining the capacity change value of the battery during the first resting time, the method further includes: The battery is left to stand for a second settling time.

5. The determination method according to claim 1, characterized in that, Before obtaining the battery health status corresponding to the self-discharge rate of change by inputting the self-discharge rate of change into the target model, the method further includes: Acquire initial data and multiple sets of aging data for the battery. The initial data includes initial maximum discharge capacity and initial self-discharge data. The initial maximum discharge capacity is the maximum discharge capacity of the battery when the number of cycles is less than a preset threshold. The aging data includes the maximum discharge capacity after aging and the self-discharge data after aging. The number of aging cycles corresponding to the multiple sets of aging data are different. Based on the initial data and the multiple sets of aging data, multiple sets of training data are determined, wherein the training data includes the battery self-discharge change rate and battery health, the battery self-discharge change rate is determined based on the initial self-discharge data and the self-discharge data after aging, and the battery health is determined based on the maximum discharge capacity after aging and the initial maximum discharge capacity. The target model to be trained is iteratively trained using the multiple sets of training data.

6. The determination method according to claim 1, characterized in that, After obtaining the battery health status corresponding to the self-consumption change rate output by the target model, the method further includes: The difference in battery health is determined based on the battery health status of the current cycle and the battery health status of the previous cycle. If the health difference is greater than a first threshold, an early warning message is generated, wherein the early warning message is used to indicate abnormal degradation of the battery's lifespan.

7. The determination method according to claim 1, characterized in that, After obtaining the battery health status corresponding to the self-consumption change rate output by the target model, the method further includes: If the battery health level is less than or equal to a second threshold, a prompt message is generated, wherein the prompt message is used to indicate that the battery should be replaced.

8. A device for determining battery health, characterized in that, include: The acquisition module is used to acquire the actual self-discharge data of the battery; The determining module is used to determine the self-consumption change rate of the battery based on the actual self-consumption data and the initial self-consumption data of the battery, wherein the initial self-consumption data is the self-consumption data of the battery when the number of cycles is less than a preset threshold. The module is used to obtain the battery health status corresponding to the self-consumption change rate by inputting the self-consumption change rate into the target model, wherein the target model is used to output the corresponding battery health status based on the input battery self-consumption change rate.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of the method for determining battery health as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer-executable program or instructions, which, when executed by a computer, implement the steps of the method for determining battery health as described in any one of claims 1-7.

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