Battery health state determination method and device, electronic equipment and program product

Abstract

The invention is suitable for the technical field of batteries, and provides a battery health state determination method and device, electronic equipment and a program product. The method for determining the state of health of the battery comprises the following steps: acquiring the duration of the voltage of a first battery within a target voltage range in a first charging process; acquiring a capacity increment curve of the first battery in the first charging process; and determining a target battery health state of the first battery according to the duration time and the capacity increment curve. According to the method, the health state of the battery can be determined only by determining a small number of parameters, and the practicability of the method for determining the health state of the battery is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of batteries, and particularly relates to a battery state of health determination method and device, an electronic device, and a program product. BACKGROUND

[0002] Determining the state of health (SOH) of a battery can effectively manage the battery and improve the safety and practicality of the battery. Currently, determining the state of health of a battery usually requires obtaining many parameters and performing complex calculations, and therefore, the existing method for determining the state of health of a battery has low practicality. SUMMARY

[0003] Therefore, the embodiments of the application provide a battery state of health determination method and device, an electronic device, and a program product to solve the technical problem of low practicality of the existing method for determining the state of health of a battery.

[0004] In a first aspect, the embodiments of the application provide a battery state of health determination method, comprising: obtaining a duration during which a voltage of a first battery is within a target voltage range in a first charging process; obtaining a capacity increment curve of the first battery in the first charging process; determining a target state of health of the first battery according to the duration and the capacity increment curve.

[0005] Optionally, the determining the target state of health of the first battery according to the duration and the capacity increment curve comprises: extracting a target feature of the capacity increment curve; inputting the duration and the target feature into a trained battery state of health prediction model, and determining the target state of health of the first battery according to a first state of health of the first battery output by the battery state of health prediction model according to the duration and the target feature.

[0006] Optionally, the extracting the target feature of the capacity increment curve comprises: performing filtering processing on the capacity increment curve; extracting a feature for describing capacity attenuation and / or internal impedance change of the first battery in the capacity increment curve after the filtering processing as the target feature.

[0007] Optionally, the determining the target state of health of the first battery according to the first state of health of the first battery output by the battery state of health prediction model according to the duration and the target feature comprises: acquire a preset parameter of the first battery, wherein the preset parameter comprises any one or more of a charge-discharge cycle number, a use time, and the duration; input the preset parameter into an experience model trained for predicting a battery health state, and acquire a second battery health state output by the experience model according to the preset parameter; determine the target battery health state according to the first battery health state and the second battery health state.

[0008] Optionally, the determining the target battery health state according to the first battery health state and the second battery health state comprises: determining a deviation coefficient between the first battery health state and the second battery health state; if the deviation coefficient is less than or equal to a deviation threshold, determining the first battery health state as the target battery health state; if the deviation coefficient is greater than the deviation threshold, correcting the first battery health state according to the second battery health state, and determining the corrected first battery health state as the target battery health state.

[0009] Optionally, the method further comprises: acquiring a target battery health state corresponding to each of a plurality of first batteries; correcting a model parameter of the experience model according to the target battery health state corresponding to each of the first batteries.

[0010] Optionally, the target voltage range is determined in the following manner: acquiring a voltage-capacity change curve of a second battery in a second charging process, the voltage-capacity change curve being used to describe a relationship between a voltage of the second battery and a capacity of the second battery; determining a plurality of initial voltage ranges according to the voltage-capacity change curve; for each of the initial voltage ranges, determining a relevance degree of the initial voltage range and the target battery health state; determining the initial voltage range with the relevance degree greater than a relevance threshold as the target voltage range.

[0011] In a second aspect, an embodiment of the present application provides a battery health state determination apparatus, comprising: a first acquisition unit configured to acquire a duration during which a voltage of a first battery is in a target voltage range in a first charging process; a second acquisition unit configured to acquire a capacity increment curve of the first battery in the first charging process; a battery health state determination unit configured to determine a target battery health state of the first battery according to the duration and the capacity increment curve.

[0012] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements each step of the battery health state determination method according to any one of the first aspect.

[0013] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executable on a processor to implement each step of the battery health state determination method according to any one of the first aspect.

[0014] In a fifth aspect, a computer program product is provided, which, when executed on a terminal device, causes the terminal device to perform each step of the battery health state determination method according to any one of the first aspect.

[0015] The battery health state determination method, device, electronic device, and program product provided by the embodiments of the present application have the following beneficial effects: In the battery health state determination method provided by the embodiments of the present application, the duration that the voltage of the first battery is in the target voltage range in the first charging process is first obtained, then the capacity increment curve of the first battery in the first charging process is obtained, and finally the target battery health state of the first battery is determined according to the duration and the capacity increment curve. By this method, only the duration that the voltage of the battery is in the target voltage range in the charging process and the capacity increment curve of the battery in the charging process need to be obtained, and the battery health state can be determined. It can be seen that by this method, only a small number of parameters need to be determined to determine the battery health state, and the practicability of the method for determining the battery health state is improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 An implementation flowchart of the battery health state determination method provided by the embodiments of the present application is provided. Figure 2A structural schematic diagram of a battery health state determination apparatus provided by an embodiment of the present application is shown in FIG. 1. Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0018] It should be noted that the terms used in the embodiments of the present application are only used to explain the embodiments of the present application, and are not intended to limit the present application. In the description of the embodiments of the present application, "a plurality of" means two or more than two, and "at least one" or "one or more" means one, two or more than two. The terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the "first", "second" features can explicitly or implicitly include one or more features.

[0019] In the present specification, the phrase "one embodiment" or "some embodiments" means that a particular feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Therefore, the phrases "in one embodiment", "in some embodiments", "in other some embodiments", "in further some embodiments" appearing in different places in the present specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "include", "contain", "have" and their variants mean "including but not limited to", unless otherwise specifically emphasized.

[0020] The execution subject of the battery health state determination method provided by the embodiments of the present application can be an electronic device, which can include but is not limited to a battery health state determination apparatus, a mobile phone, a tablet computer, a notebook computer, a desktop computer, a battery management device (such as a control device in a battery management system), and the like.

[0021] The battery health state determination method provided by the embodiments of the present application can be applied to any scenario where the battery health state needs to be determined. When the battery health state needs to be determined, each step of the battery health state determination method provided by the embodiments of the present application can be executed by the electronic device, so that the battery health state can be determined with a small number of parameters, and the practicability of the method for determining the battery health state is improved.

[0022] For example, the battery health state determination method provided by the embodiments of the present application can be applied to determine the battery health state of a lithium battery.

[0023] Please refer to Figure 1 , Figure 1An implementation flowchart of a battery health state determination method provided in an embodiment of the present application can include S101-S103, which are described in detail as follows. In S101, a duration in which the voltage of the first battery is in a target voltage range in a first charging process is acquired.

[0024] In the embodiment of the present application, the first battery is a battery whose battery health state is to be determined, and the first charging process is a process of charging the first battery.

[0025] When a user needs to determine the battery health state of the first battery, the user can acquire, through an electronic device, the duration in which the voltage of the first battery is in a target voltage range in a first charging process.

[0026] Specifically, the electronic device can charge the first battery through a preset manner, and then in the first charging process of charging the first battery, the electronic device can acquire the voltage of the first battery in real time, thereby acquiring the voltage of the first battery in the first charging process.

[0027] After acquiring the voltage of the first battery in the first charging process, the electronic device can determine the duration in which the voltage of the first battery is in a target voltage range in the first charging process according to the voltage of the first battery in the first charging process.

[0028] The target voltage range can be determined and stored by the electronic device in advance.

[0029] In a possible implementation manner, the electronic device can determine the target voltage range in the following manner: First, the electronic device can acquire a voltage-capacity change curve of a second battery in a second charging process, wherein the voltage-capacity change curve is used to describe the relationship between the voltage of the second battery and the capacity of the second battery. Specifically, the electronic device can charge the second battery through a preset manner, and then in a second charging process of charging the second battery, the electronic device can acquire the voltage of the second battery in real time and acquire the capacity of the second battery in real time, thereby acquiring the voltage-capacity change curve of the second battery in the second charging process according to the voltage of the second battery and the capacity of the second battery.

[0030] It should be noted that the difference between the first battery and the second battery is: The first battery is a battery whose battery health state is to be determined by the electronic device at this time, and the second battery is a battery used by the electronic device in the process of determining the target voltage range in advance.

[0031] After the voltage-capacity change curve of the second battery in the second charging process is acquired, the electronic device can determine a plurality of initial voltage ranges according to the voltage-capacity change curve.

[0032] After the plurality of initial voltage ranges are determined, the electronic device can determine, for each initial voltage range, a correlation degree of the initial voltage range and the target battery health state, thereby obtaining the correlation degree of each initial voltage range and the target battery health state. In actual application, the specific manner of determining the correlation degree of the initial voltage range and the target battery health state can be set according to actual needs, which is not limited here.

[0033] After the correlation degree of each initial voltage range and the target battery health state is obtained, the electronic device can determine an initial voltage range with a correlation degree greater than a correlation degree threshold as a target voltage range. The specific value of the correlation degree threshold can be set according to actual needs, which is not limited here.

[0034] In S102, a capacity increment curve of the first battery in a first charging process is acquired.

[0035] In the embodiment of the present application, in the first charging process of charging the first battery, the electronic device can also acquire the capacity increment curve of the first battery in the first charging process.

[0036] Specifically, the electronic device can charge the first battery through a preset manner, and then in the first charging process of charging the first battery, the electronic device can acquire the capacity of the first battery in real time, thereby acquiring the capacity increment curve of the first battery in the first charging process.

[0037] In S103, the target battery health state of the first battery is determined according to the duration and the capacity increment curve.

[0038] In the embodiment of the present application, after the duration of the voltage of the first battery in the first charging process being in the target voltage range and the capacity increment curve of the first battery in the first charging process are acquired, the electronic device can determine the target battery health state of the first battery according to the duration of the voltage of the first battery in the first charging process being in the target voltage range and the capacity increment curve of the first battery in the first charging process.

[0039] In a possible implementation, the electronic device can determine the target battery health state of the first battery through steps a to b, which are described as follows: In step a, a target feature of the capacity increment curve is extracted.

[0040] In the present implementation, after the duration that the voltage of the first battery is within the target voltage range during the first charging process and the capacity increment curve of the first battery during the first charging process are obtained, the electronic device can extract the target feature of the capacity increment curve.

[0041] Specifically, the electronic device can first perform filtering processing on the capacity increment curve to obtain a filtered capacity increment curve, and then extract a feature in the filtered capacity increment curve for describing the capacity attenuation and / or internal impedance change of the first battery as the target feature.

[0042] In actual applications, the feature in the capacity increment curve for describing the capacity attenuation and / or internal impedance change of the first battery can be determined through experiments and related knowledge in the field of batteries. The specific method for determining the feature in the capacity increment curve for describing the capacity attenuation and / or internal impedance change of the first battery can be set according to actual needs, which is not limited here.

[0043] In step b, the duration and the target feature are input to the trained battery health state prediction model, and the target battery health state is determined based on the first battery health state output by the battery health state prediction model according to the duration and the target feature.

[0044] In the present implementation, after the target feature of the capacity increment curve is extracted, the electronic device can input the duration that the voltage of the first battery is within the target voltage range during the first charging process and the target feature of the capacity increment curve extracted in step a to the trained battery health state prediction model, so that the target battery health state can be determined based on the first battery health state output by the battery health state prediction model according to the duration and the target feature.

[0045] The battery health state prediction model can be used to output the first battery health state according to the input duration and target feature.

[0046] For example, the battery health state prediction model can be trained by using the Gaussian Process Regression (GPR) method. Specifically, the training data can be input to the battery health state prediction model to enable the battery health state prediction model to learn the mapping relationship between the duration, the target feature and the first battery health state.

[0047] In addition, the battery health state prediction model can also output the mean and variance of the first battery health state, so that the electronic device can obtain the confidence interval of the first battery health state.

[0048] In a possible implementation, the electronic device can determine the target battery health state of the first battery through steps c to e.

[0049] In step c, the preset parameter of the first battery is acquired.

[0050] In the implementation, when the electronic device needs to determine the target battery health state of the first battery, the electronic device can first acquire the preset parameter of the first battery.

[0051] The preset parameter can include any one or more of the number of charge-discharge cycles, the use time, and the duration.

[0052] The preset parameter can be input into the trained experience model for predicting the battery health state, so that the experience model outputs the second battery health state of the first battery according to the preset parameter.

[0053] In step d, the preset parameter is input into the trained experience model for predicting the battery health state, and the second battery health state output by the experience model according to the preset parameter is acquired.

[0054] In the implementation, after the preset parameter is acquired, the electronic device can input the preset parameter into the trained experience model for predicting the battery health state, so that the experience model can output the second battery health state of the first battery according to the preset parameter.

[0055] For example, the experience model can learn the mapping relationship between the preset parameter and the second battery health state, so that the experience model can output the second battery health state of the first battery according to the preset parameter.

[0056] It should be noted that, compared with the battery health state prediction model, the task of the experience model is relatively simple, and therefore the experience model has the characteristics of simple form, few parameters, and easy training. Therefore, the efficiency of the experience model in outputting the second battery health state of the first battery according to the preset parameter is higher than the efficiency of the battery health state prediction model in outputting the first battery health state of the first battery according to the duration and the target feature.

[0057] It should be noted that the first battery health state and the second battery health state are different in that: The first battery health state is determined and output by the battery health state prediction model according to the duration and the target feature input by the electronic device.

[0058] The second battery health state is determined and output by the experience model according to the preset parameter input by the electronic device.

[0059] Both the first battery health state and the second battery health state can be used to determine the target battery health state of the first battery.

[0060] In step e, a target battery health state is determined according to the first battery health state and the second battery health state.

[0061] In the implementation manner, after obtaining the first battery health state output by the battery health state prediction model and obtaining the second battery health state output by the experience model, the electronic device can determine a target battery health state according to the first battery health state and the second battery health state.

[0062] Specifically, the electronic device can first determine a deviation coefficient between the first battery health state and the second battery health state. After determining the deviation coefficient between the first battery health state and the second battery health state, the electronic device can determine a size relationship between the deviation coefficient and a deviation threshold, where the deviation threshold is used to evaluate whether the deviation coefficient between the first battery health state and the second battery health state is too large or too small.

[0063] If the electronic device determines that the deviation coefficient between the first battery health state and the second battery health state is less than or equal to the deviation threshold (that is, the electronic device considers that the deviation coefficient between the first battery health state and the second battery health state is small), the electronic device can determine the first battery health state as the target battery health state.

[0064] If the electronic device determines that the deviation coefficient between the first battery health state and the second battery health state is greater than the deviation threshold (that is, the electronic device considers that the deviation coefficient between the first battery health state and the second battery health state is large), the electronic device can correct the first battery health state, and determine the corrected first battery health state as the target battery health state.

[0065] As can be seen from the above, in the embodiment of the present application, the target battery health state of the first battery is determined according to the first battery health state output by the battery health state prediction model and the second battery health state output by the experience model, which can make the target battery health state more accurate.

[0066] In a possible implementation manner, since the second battery health state output by the experience model may have a large deviation, the experience model can be corrected through steps f to g. Details are as follows: In step f, a target battery health state corresponding to each of a plurality of first batteries is obtained.

[0067] In the implementation manner, when the experience model needs to be corrected, a target battery health state corresponding to each of a plurality of first batteries can be obtained. Each of the first batteries is a first battery whose target battery health state has been determined.

[0068] In step g, the model parameters of the empirical model are corrected according to the health status of the target battery corresponding to each first battery.

[0069] In this implementation, after obtaining the target battery health status corresponding to each of the several first batteries, the electronic device can correct the model parameters of the empirical model according to the target battery health status corresponding to each first battery.

[0070] As can be seen from the above, in the battery health status determination method provided in this application embodiment, the duration for which the voltage of the first battery is within the target voltage range during the first charging process is first obtained, then the capacity increment curve of the first battery during the first charging process is obtained, and finally the target battery health status of the first battery is determined based on the duration and the capacity increment curve. This method only requires obtaining the duration for which the battery voltage is within the target voltage range during charging and obtaining the capacity increment curve of the battery during charging to determine the battery health status. It can be seen that this method only requires determining a small number of parameters to determine the battery health status, thus improving the practicality of the method for determining battery health status.

[0071] Based on the battery health status determination method provided in the above embodiments, this application further provides a battery health status determination apparatus for implementing the above method embodiments. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a schematic diagram of a device for determining battery health status provided in an embodiment of this application. Figure 2 As shown, the battery health status determination device 20 may include: a first acquisition unit 21, a second acquisition unit 22, and a battery health status determination unit 23. Wherein: The first acquisition unit 21 is used to acquire the duration during which the voltage of the first battery is within the target voltage range during the first charging process.

[0072] The second acquisition unit 22 is used to acquire the capacity increment curve of the first battery during the first charging process.

[0073] The battery health status determination unit 23 is used to determine the target battery health status of the first battery based on the duration and the capacity increment curve.

[0074] Optionally, the battery health status determination unit 23 is specifically used for: Extract the target features from the capacity increment curve; input the duration and the target feature into a battery state of health prediction model trained, and determine the target battery state of health according to a first battery state of health output by the battery state of health prediction model according to the duration and the target feature.

[0075] Optionally, the battery state of health determination unit 23 is specifically configured to: perform filtering processing on the capacity increment curve; extract a feature for describing capacity attenuation and / or internal impedance change of the first battery in the capacity increment curve after filtering processing as the target feature.

[0076] Optionally, the battery state of health determination unit 23 is specifically configured to: obtain a preset parameter of the first battery; wherein the preset parameter includes any one or more of a number of charge and discharge cycles, a use time, and the duration; input the preset parameter into an experience model trained for predicting a battery state of health, and obtain a second battery state of health output by the experience model according to the preset parameter; determine the target battery state of health according to the first battery state of health and the second battery state of health.

[0077] Optionally, the battery state of health determination unit 23 is specifically configured to: determine a deviation coefficient between the first battery state of health and the second battery state of health; if the deviation coefficient is less than or equal to a deviation threshold, determine the first battery state of health as the target battery state of health; if the deviation coefficient is greater than the deviation threshold, correct the first battery state of health according to the second battery state of health, and determine the first battery state of health after correction as the target battery state of health.

[0078] Optionally, the battery state of health determination apparatus 20 can further include a correction unit. Wherein: the correction unit is specifically configured to: obtain a target battery state of health corresponding to each of the first batteries; correct a model parameter of the experience model according to the target battery state of health corresponding to each of the first batteries.

[0079] Optionally, the battery state of health determination apparatus 20 can further include a voltage range determination unit. Wherein: the voltage range determination unit is specifically configured to: Obtain the voltage and capacity change curve of the second battery during the second charging process. The voltage and capacity change curve is used to describe the relationship between the voltage and capacity of the second battery. Based on the voltage and capacity change curves, several initial voltage ranges are determined; For each of the initial voltage ranges, determine the correlation between the initial voltage range and the target battery health state; The initial voltage range with a correlation greater than the correlation threshold is determined as the target voltage range.

[0080] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 3 provided in this embodiment may include: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program corresponding to a method for determining battery health status. When the processor 30 executes the computer program 32, it implements the steps described above in the embodiment of the method for determining battery health status, for example... Figure 1 S101~S103 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described battery health status determination device embodiment, for example... Figure 2 The functions of units 21-23 shown.

[0081] For example, computer program 32 can be divided into one or more modules / units, one or more of which are stored in memory 31 and executed by processor 30 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 32 in electronic device 3. For example, computer program 32 can be divided into a first acquisition unit 21, a second acquisition unit 22, and a battery health status determination unit 23. For the specific functions of each unit, please refer to [link / reference]. Figure 2 The relevant descriptions in the corresponding embodiments are not repeated here.

[0082] Those skilled in the art will understand that Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or use different components.

[0083] The processor 30 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0084] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or a memory of the electronic device 3. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card or a flash card, etc. equipped on the electronic device 3. Further, the memory 31 can also include both the internal storage unit and the external storage device of the electronic device 3. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0085] It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, only the division of the above functional units is taken as an example, and in actual application, the above functions can be completed by different functional units according to needs, that is, the internal structure of the battery health state determination device is divided into different functional units to complete all or part of the above described functions. Each functional unit in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific name of each functional unit is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the unit in the above system can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0086] The embodiment of the present application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps in each method embodiment.

[0087] The embodiment of the present application provides a computer program product, when the computer program product runs on a terminal device, causes the terminal device to realize steps in each method embodiment.

[0088] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments.

[0089] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software mode depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0090] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for determining the health status of a battery, characterized in that, include: The duration during which the voltage of the first battery remains within the target voltage range during the first charging process is obtained; Obtain the capacity increment curve of the first battery during the first charging process; The target battery health status of the first battery is determined based on the duration and the capacity increment curve.

2. The method according to claim 1, characterized in that, Determining the target battery health status of the first battery based on the duration and the capacity increment curve includes: Extract the target features from the capacity increment curve; The duration and the target feature are input into the trained battery health status prediction model, and the target battery health status is determined by the first battery health status output by the battery health status prediction model based on the duration and the target feature.

3. The method according to claim 2, characterized in that, The extraction of the target features of the capacity increment curve includes: The capacity increment curve is filtered. The features used to describe the capacity decay and / or internal impedance change of the first battery are extracted from the filtered capacity increment curve as the target features.

4. The method according to claim 2, characterized in that, The step of determining the target battery health state by using the battery health state prediction model to output a first battery health state based on the duration and the target characteristics includes: Obtain preset parameters of the first battery; wherein, the preset parameters include any one or more of the following: number of charge / discharge cycles, usage time, and duration; The preset parameters are input into the trained empirical model for predicting battery health status, and the second battery health status output by the empirical model based on the preset parameters is obtained. The target battery health state is determined based on the first battery health state and the second battery health state.

5. The method according to claim 4, characterized in that, Determining the target battery health state based on the first battery health state and the second battery health state includes: Determine the deviation coefficient between the first battery health state and the second battery health state; If the deviation coefficient is less than or equal to the deviation threshold, then the first battery health state is determined as the target battery health state. If the deviation coefficient is greater than the deviation threshold, the first battery health state is corrected according to the second battery health state, and the corrected first battery health state is determined as the target battery health state.

6. The method according to claim 4, characterized in that, Also includes: Obtain the health status of each of the target batteries corresponding to the first batteries; The model parameters of the empirical model are corrected based on the target battery health status corresponding to each of the first batteries.

7. The method according to any one of claims 1 to 6, characterized in that, The target voltage range is determined in the following way: Obtain the voltage and capacity change curve of the second battery during the second charging process. The voltage and capacity change curve is used to describe the relationship between the voltage and capacity of the second battery. Based on the voltage and capacity change curves, several initial voltage ranges are determined; For each of the initial voltage ranges, determine the correlation between the initial voltage range and the target battery health state; The initial voltage range with a correlation greater than the correlation threshold is determined as the target voltage range.

8. A device for determining the health status of a battery, characterized in that, include: The first acquisition unit is used to acquire the duration during which the voltage of the first battery is within the target voltage range during the first charging process; The second acquisition unit is used to acquire the capacity increment curve of the first battery during the first charging process; A battery health status determination unit is used to determine the target battery health status of the first battery based on the duration and the capacity increment curve.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the method for determining the battery health status as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, When the computer program product is executed by a processor, it implements the steps of the method for determining the battery health status as described in any one of claims 1 to 7.

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