Battery health state determination method and device, storage medium and electronic device

Abstract

The invention discloses a battery health state determination method and device, a storage medium and an electronic device, and the method comprises the steps: determining the single body difference characteristics of a to-be-detected battery, and the first precondition characteristics and the first internal resistance type characteristics of the to-be-detected battery at each internal resistance calculation point; constructing a first combined feature of the to-be-tested battery according to the monomer difference feature, the first precondition feature and the first internal resistance type feature; and determining a first health state of the to-be-tested battery according to the first combined feature and a health state feature library, the health state feature library being used for indicating a corresponding relationship between the combined feature and the health state of a reference battery. Through the method, the problem that the accuracy of the evaluated health state of the battery is low in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and more specifically, to a method and device for determining a battery health status, a storage medium, and an electronic device. Background Art

[0002] The battery's state of health (SOH) can be calculated in two main ways: based on capacity and based on internal resistance. In actual application scenarios, the true capacity of an aging battery cannot be easily obtained. In addition to the aging state, the battery's internal resistance is also related to the battery's state of charge (SOC), temperature, pre-operating conditions, internal resistance type, and so on. In the related technologies for calculating SOH based on internal resistance, most of them use experimental table lookups to exclude the influence of factors such as SOC and temperature, so that the battery's internal resistance is only related to the aging state. However, the pre-operating conditions of the internal resistance calculation point and the type of internal resistance calculated also have a strong influence on the calculation of the internal resistance. The related technologies have not taken into account the influence of these two on the calculation of SOH based on internal resistance, which leads to low accuracy of the calculated SOH.

[0003] Currently, no effective solution has been proposed to address the problem of low accuracy in evaluating the health status of batteries in related technologies.

[0004] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0005] The embodiments of the present application provide a method and device for determining the health status of a battery, a storage medium, and an electronic device, so as to at least solve the problem of low accuracy in evaluating the health status of a battery in the related art.

[0006] According to one aspect of an embodiment of the present application, a method for determining a battery health state is provided, comprising: determining a single cell difference characteristic of a battery to be tested, a first precondition characteristic and a first internal resistance type characteristic of the battery to be tested at each internal resistance calculation point; constructing a first combination characteristic of the battery to be tested according to the single cell difference characteristic, the first precondition characteristic and the first internal resistance type characteristic; determining a first health state of the battery to be tested according to the first combination characteristic and a health state characteristic library, wherein the health state characteristic library is used to indicate a correspondence between a combination characteristic and a health state of a reference battery.

[0007] In an exemplary embodiment, determining the first precondition characteristic of the battery to be tested at each internal resistance calculation point includes: determining the cumulative voltage rise, cumulative voltage drop, cumulative current increment and cumulative negative current increment corresponding to the battery to be tested according to an exponential moving average algorithm; determining the voltage change characteristic corresponding to the battery to be tested according to the cumulative voltage rise and the cumulative voltage drop; and determining the current change characteristic corresponding to the battery to be tested according to the cumulative current increment and the cumulative negative current increment, wherein the first precondition characteristic includes: the voltage change characteristic and the current change characteristic.

[0008] In an exemplary embodiment, determining the first internal resistance type characteristic of the battery to be tested at each internal resistance calculation point includes: determining the current rate, terminal voltage and internal resistance value of the battery to be tested at each internal resistance calculation point, and determining the sampling period corresponding to the battery to be tested at each internal resistance calculation point; determining the first internal resistance type characteristic according to the current rate, the terminal voltage, the internal resistance value and the sampling period.

[0009] In an exemplary embodiment, the first combination feature of the battery to be tested is constructed according to the single cell difference feature, the first precondition feature and the first internal resistance type feature, including: determining multiple sets among multiple internal resistance calculation points, wherein each set includes multiple target internal resistance calculation points, and each target internal resistance calculation point is any internal resistance calculation point among the multiple internal resistance calculation points; determining the second precondition feature and the second internal resistance type feature of each target internal resistance calculation point of the battery to be tested in each set; splicing the multiple second precondition features and the multiple second internal resistance type features in each set to generate a splicing result corresponding to each set; and splicing each splicing result with the single cell difference feature respectively to generate multiple first combination features of the battery to be tested.

[0010] In an exemplary embodiment, before determining the first health state of the battery to be tested based on the first combination feature and the health state feature library, the method also includes: determining multiple second combination features corresponding to the reference battery, and obtaining the second health state corresponding to the reference battery; combining each second combination feature with the second health state respectively to generate multiple first health state features, wherein each first health state feature is used to indicate the corresponding relationship between each second combination feature and the second health state; and constructing the health state feature library based on the multiple first health state features.

[0011] In an exemplary embodiment, determining the first health state of the battery to be tested based on the first combined feature and the health state feature library includes: matching the first combined feature with the health state feature library to determine a plurality of second health state features matching the first combined feature in the health state feature library; denoising the plurality of second health state features to determine a plurality of third health state features; integrating the health states in the plurality of third health state features, and determining the first health state of the battery to be tested based on the integration result.

[0012] In an exemplary embodiment, determining the first health state of the battery to be tested based on the first combined feature and a health state feature library includes: training a relationship model to be trained based on the health state feature library to generate a relationship model to fit the relationship between the combined feature and the health state of the battery; inputting the first combined feature into the relationship model so that the relationship model outputs the first health state based on the first combined feature.

[0013] According to another aspect of an embodiment of the present application, a device for determining a battery health status is also provided, including: a first determination module, used to determine a single cell difference characteristic of a battery to be tested, a first precondition characteristic and a first internal resistance type characteristic of the battery to be tested at each internal resistance calculation point; a construction module, used to construct a first combination characteristic of the battery to be tested based on the single cell difference characteristic, the first precondition characteristic and the first internal resistance type characteristic; a second determination module, used to determine a first health status of the battery to be tested based on the first combination characteristic and a health status characteristic library, wherein the health status characteristic library is used to indicate the correspondence between the combination characteristic and the health status of a reference battery.

[0014] According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the battery health status when running.

[0015] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the battery health status through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method for determining the battery health status is implemented.

[0017] Through the embodiment of the present application, the single-cell difference characteristics of the resistor to be tested, the first precondition characteristics at each internal resistance calculation point, and the first internal resistance type characteristics are determined; the first combination characteristics of the battery to be tested are constructed according to the single-cell difference characteristics, the first precondition characteristics at each internal resistance calculation point, and the first internal resistance type characteristics; the first health state of the battery to be tested is determined according to the first combination characteristics and a health status characteristic library determined based on a reference battery for indicating the relationship between the combination characteristics of the battery and the health state of the battery. That is to say, the embodiment of the present application obtains the single-cell difference characteristics, the first precondition characteristics, and the first internal resistance type characteristics of the battery to be tested, and then determines the first combination characteristics of the battery to be tested according to the single-cell difference characteristics, the first precondition characteristics, and the first internal resistance type characteristics, and the first health state of the battery to be tested can be determined according to the first combination characteristics and the health status characteristic library for indicating the corresponding relationship between the combination characteristics of the battery and the health state of the battery. Most of the methods for calculating SOH in the related art eliminate the influence of temperature and SOC on the calculation of SOH through experimental table lookup, so that the internal resistance value of the battery is only related to the aging state. However, the pre-conditions of the internal resistance calculation point and the calculated internal resistance type also have a strong influence on the calculation of the internal resistance value. The related art does not take them into account as influencing factors in the calculation of SOH, which leads to low SOH calculation accuracy. The embodiment of the present application takes into account the influence of the pre-conditions (i.e., the first pre-condition characteristics) and the calculated internal resistance type (i.e., the first internal resistance type characteristics) on the health state of the battery, thereby improving the calculation accuracy of the health state of the battery. Therefore, the embodiment of the present application can solve the problem of low accuracy of the health state of the battery evaluated in the related art. Thereby improving the accuracy of the evaluation of the health state of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The exemplary embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of the method for determining the battery health status of an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for determining a battery health status according to an embodiment of the present application;

[0021] Figure 3 is a flow chart of a method for calculating a health state based on preconditions and internal resistance type characteristics according to an optional embodiment of the present application;

[0022] Figure 4 It is a structural block diagram of a device for determining a battery health status according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal of the method for determining the battery health status of an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 (processor 102 may include but is not limited to a microprocessor (Microprocessor Unit, referred to as MPU) or a programmable logic device (Programmable logic device, referred to as PLD)) and a memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0026] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the battery health status in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0027] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] In order to solve the above problems, a method for determining the health status of a battery is provided in this embodiment. Figure 1 Computer terminal in Figure 2 : is a flow chart of a method for determining a battery health status according to an embodiment of the present application, the process comprising the following steps:

[0029] Step S202, determining a single cell difference characteristic of the battery to be tested, a first precondition characteristic of the battery to be tested at each internal resistance calculation point, and a first internal resistance type characteristic;

[0030] The single cell difference characteristics of the battery to be tested may include: the nominal (rated) capacity, calibrated capacity, lower cut-off voltage, upper cut-off voltage, rated voltage, etc. of the battery to be tested.

[0031] The above internal resistance calculation points are pre-set calculation points. There is a certain correlation between the internal resistance calculation point and the SOH calculation point: the SOH calculation point refers to the moment when the SOH needs to be calculated in the actual scene, and calculating the SOH once requires calculating the internal resistance values ​​calculated at multiple internal resistance calculation points.

[0032] For example: the point where the remaining power of the battery under test is 10% in a charge and discharge cycle, the point where the remaining power of the battery under test is 20% in a charge and discharge cycle, ..., the point where the remaining power of the battery under test is 90% in a charge and discharge cycle, etc. You can set different internal resistance calculation points according to different SOC intervals (5%, 2%, etc.), or customize multiple internal resistance calculation points.

[0033] Step S204, constructing a first combination feature of the battery to be tested according to the cell difference feature, the first precondition feature and the first internal resistance type feature;

[0034] Step S206, determining a first health state of the battery to be tested according to the first combined feature and a health state feature library, wherein the health state feature library is used to indicate a corresponding relationship between a combined feature and a health state of a reference battery.

[0035] The first health state is the health state of the battery to be tested. The battery health state (SOH) is an indicator that measures the current performance of the battery compared to its performance when it was brand new. SOH is usually given in the form of a percentage, reflecting the degree of battery aging and the remaining service life.

[0036] The embodiment of the present application obtains the single cell difference feature, the first precondition feature and the first internal resistance type feature of the battery to be tested, and then determines the first combination feature of the battery to be tested according to the single cell difference feature, the first precondition feature and the first internal resistance type feature, and determines the first health state of the battery to be tested according to the first combination feature and the health state feature library used to indicate the corresponding relationship between the combination feature of the battery and the health state of the battery. Most of the methods for calculating SOH in the related art eliminate the influence of temperature and SOC on the calculation of SOH through experimental table lookup, so that the battery internal resistance value is only related to the aging state. However, the precondition of the internal resistance calculation point and the calculated internal resistance type also have a strong influence on the calculation of the internal resistance value. In the related art, they are not considered as influencing factors in the calculation of SOH, which leads to low SOH calculation accuracy. The embodiment of the present application takes into account the influence of the precondition (i.e., the first precondition feature) and the calculated internal resistance type (i.e., the first internal resistance type feature) on the health state of the battery, thereby improving the calculation accuracy of the health state of the battery. Therefore, the embodiment of the present application can solve the problem of low accuracy of the health state of the battery evaluated in the related art. This will improve the accuracy of battery health status assessment.

[0037] Optionally, the above-mentioned step S202 of determining the first precondition characteristic of the battery to be tested at each internal resistance calculation point includes: determining the cumulative voltage rise, cumulative voltage drop, cumulative current increment and cumulative negative current increment corresponding to the battery to be tested according to an exponential moving average algorithm; determining the voltage change characteristic corresponding to the battery to be tested according to the cumulative voltage rise and the cumulative voltage drop; and determining the current change characteristic corresponding to the battery to be tested according to the cumulative current increment and the cumulative negative current increment, wherein the first precondition characteristic includes: the voltage change characteristic and the current change characteristic.

[0038] It is understandable that the internal resistance of the battery is closely related to the preceding cycle conditions, but if the preceding cycle conditions are directly stored and substituted into the preceding conditions to calculate the internal resistance of the battery, the calculation and storage costs are too high (the specific manifestation of the preceding conditions is the sequence data of current and voltage, which may be hundreds or thousands in dimension, with a high dimensionality), and it is difficult to implement. In order to reduce the calculation and storage costs, a lightweight strategy is used to record the preceding conditions, making it possible to calculate the internal resistance of the battery considering the preceding conditions. The embodiment of the present application defines a technical solution for determining the first precondition feature by an exponential moving average algorithm. The exponential moving average is essentially a recursive form that can achieve weighted accumulation and reduce calculation complexity and storage costs. Specifically:

[0039] The exponential moving average can be used to calculate the cumulative voltage rise, cumulative voltage drop, cumulative current increment, and cumulative negative current increment of the reference battery at the current internal resistance calculation point.

[0040] Among them, in the embodiment of the present application, only the first precondition feature is shown to include: current change feature and voltage change feature. The first precondition feature may also include: temperature change feature corresponding to the battery to be tested, that is:

[0041] The cumulative temperature increment and the cumulative negative temperature increment corresponding to the battery to be tested are determined according to an exponential moving average algorithm; and the temperature change characteristics corresponding to the battery to be tested are determined according to the cumulative temperature increment and the cumulative negative temperature increment.

[0042] Among them, the exponential moving average algorithm (EMA): EMA can weight the previous historical sequence through the cumulative weight α, and generally give higher weights to recent data points, while earlier data points are given lower weights. The specific formula is:

[0043] EMA t =α×x t +(1-α)×EMA t-1 .

[0044] Among them, EMAt-1 The exponential moving average result of the previous cumulative recursive calculation point; EMA t is the exponential moving average result of the current cumulative recursive calculation point (i.e., the cumulative statistic, which may be the cumulative voltage rise, cumulative voltage drop, cumulative current increment, and cumulative current negative increment in the embodiment of the present application); x t It is the incremental data between the current cumulative recursive calculation point and the previous cumulative recursive calculation point, which may be the voltage increment, voltage reduction, current reduction, current increment, etc. between the current cumulative recursive calculation point and the previous cumulative recursive calculation point.

[0045] The cumulative recursive calculation point refers to the calculation point set periodically or under specific conditions during the use of the battery, which is usually used to collect and update the precondition characteristics of the battery. The cumulative recursive calculation point and the internal resistance calculation point can be at the same time, that is, the precondition characteristics are updated while calculating the internal resistance value; or they can be at different times, for example: the precondition characteristics may continue to accumulate during the use of the battery, and the internal resistance value is the result of calculation in a specific period or under conditions.

[0046] It is understandable that the cumulative voltage rise, cumulative voltage drop, cumulative current increment and cumulative negative current increment can be calculated by the above exponential moving average. The following is a specific description of the meaning of the cumulative voltage rise, cumulative voltage drop, cumulative current increment and cumulative negative current increment.

[0047] 1) Cumulative voltage rise and cumulative voltage drop: Cumulative voltage rise refers to the cumulative change of battery voltage rise over a period of time, while cumulative voltage drop refers to the cumulative change of battery voltage drop over the same time range. The EMA algorithm is essentially a recursive method, so it can be used to obtain the cumulative change of battery voltage drop / rise over a period of time before the current internal resistance calculation point at a lower calculation and storage cost.

[0048] 2) Cumulative current increment and cumulative negative current increment: The cumulative current increment refers to the cumulative change in the battery's current increase over a period of time; while the cumulative negative current increment refers to the cumulative change in the battery's current decrease over a period of time. Similar to the cumulative voltage rise and cumulative voltage drop mentioned above, the EMA algorithm can also be used to obtain the cumulative change in the battery's cumulative current increment / negative increment over a period of time before the current internal resistance calculation point at a lower calculation and storage cost.

[0049] 3) The corresponding cumulative voltage rise (voltage drop) and cumulative current increment (negative increment) under different pre-operating conditions are different. The cumulative voltage rise (voltage drop) can be used to represent the voltage change characteristics, and the cumulative current increment (negative increment) can be used to represent the current change characteristics. By recording four cumulative variables, the operating condition sequence data containing hundreds or thousands of data points can be identified from the side.

[0050] In summary, the embodiment of the present application determines the cumulative voltage rise (voltage drop) and the cumulative current increment (negative increment) through the exponential moving average algorithm. The cumulative voltage rise (voltage drop) can be combined or used as a feature alone to reflect the dynamic change trend of the battery voltage. This dynamic change trend is the voltage change feature. That is, the voltage change feature can be the direct value of the EMA cumulative voltage rise and voltage drop, or it can be a function of them, such as: their ratio, difference or average value, etc., which depends on the specific application requirements. Similarly, the cumulative current increment (negative increment) can be combined or used as a feature alone to reflect the dynamic change trend of the battery current. This dynamic change trend is the current change feature. That is, the current change feature can be the direct value of the EMA cumulative current increment and negative increment, or it can be a function of them, such as: their ratio, difference or average value, etc., which also depends on the specific application requirements.

[0051] In addition, the above cumulative amount only records the pre-change conditions of the internal resistance calculation point, but different starting points will get different change end states after the same pre-condition, and they correspond to different change processes. Therefore, it is necessary to record the starting state of the pre-condition. In order to reduce the number of features, the SOC of the battery at the internal resistance calculation point can be used to record the end state of the pre-condition change, which is equivalent to recording the starting state of the pre-condition. Since the SOC is estimated by the algorithm in practice, there may be estimation errors, so the terminal voltage can be used to identify the true SOC of the battery to avoid estimation errors. For the convenience of description, the terminal voltage is placed in the internal resistance type feature for illustration.

[0052] Optionally, the above-mentioned step S202 of determining the first internal resistance type characteristic of the battery to be tested at each internal resistance calculation point includes: determining the current rate, terminal voltage and internal resistance value of the battery to be tested at each internal resistance calculation point, and determining the sampling period corresponding to the battery to be tested at each internal resistance calculation point; determining the first internal resistance type characteristic according to the current rate, the terminal voltage, the internal resistance value and the sampling period.

[0053] It is understandable that the first internal resistance type characteristics may include: terminal voltage, temperature, current, rate×sampling period, internal resistance value (expressed as voltage change / current value at the current internal resistance calculation point), etc. Among them, since the charge and discharge internal resistance of the reference battery is different, the current and rate need to be distinguished between positive and negative.

[0054] It should be noted that the principle of calculating SOH based on the internal resistance of the battery is that the internal resistance can correspond to the aging state of the battery one-to-one. However, in actual applications, the factors related to the calculated internal resistance include: the pre-conditions of the internal resistance calculation point, terminal voltage, temperature, current, rate, sampling period (which can also be multiplied with the rate and combined into one feature), and aging state. If the influence of factors other than the aging state on the internal resistance value is not excluded, then the internal resistance value and the aging state cannot correspond one-to-one, and thus the SOH cannot be calculated.

[0055] For example: If the influence of any factors other than the aging state on the calculation of the internal resistance value is not excluded (these factors are not distinguished in the features): Assume that there are two batteries of the same type, A and B (the former is 20% SOH and the latter is 80% SOH), both of which calculate the internal resistance value at a certain internal resistance calculation point. If the ambient temperature of A is 45 degrees and that of B is 10 degrees, the calculated internal resistance values ​​of A and B are the same. Then when using feature matching or model fitting to calculate SOH, the SOH of A and B are both fuzzy and centered at 50% (the model does not know that temperature will affect the calculation of the battery internal resistance, and the model will assume that under the same conditions of other factors, the internal resistance calculated at different temperatures is the same, and their aging states are the same), but the predicted value of 50% SOH is far from the actual SOH values ​​of A and B.

[0056] If the temperature is recorded as a feature, the effect of temperature on the internal resistance calculation will be considered during the matching or fitting calculation. Even if the internal resistance is the same at different temperatures, it represents different aging states, and the calculated SOH will not be a vague middle value, but an accurate and close to the actual SOH value.

[0057] In the embodiment of the present application, it is assumed that the internal resistance value calculated by the battery at a certain internal resistance calculation point = W0×R0+W1×R1+W2×R2 (the above formula is only for illustration, and the actual relationship may be more complicated). Among them, R1, R2, and R0 are used to indicate the internal resistance values ​​of the internal resistance of different internal resistance types, and W0, W1, and W2 are used to indicate different coefficients corresponding to different internal resistance types.

[0058] When a certain current rate is applied to the battery at a certain moment, the internal resistance of the battery will change over time. The W0, W1, and W2 coefficients at each moment may be different, which means that the type of internal resistance calculated will change over time. Existing research has difficulty in accurately finding out the changing rules of W0, W1, and W2 with current rate and cycle time, and it is difficult to rely on these coefficients to represent the type of internal resistance.

[0059] But it is clear that the internal resistance types calculated under different current magnifications and sampling periods are different. Therefore, the current magnification and sampling period can be used as features to indirectly identify the type of calculated internal resistance. In form, they can be separated into two features, or multiplied and combined into one feature.

[0060] The calculated internal resistance value, as a variable directly related to the aging state, also needs to be recorded as a feature. For the sake of simplicity in description, it is embedded in the internal resistance type feature and recorded together.

[0061] Therefore, the current multiplication factor and the sampling period can be used as features respectively or combined into one feature, and they and the internal resistance value form a first internal resistance type feature.

[0062] Specifically, the method for generating the first internal resistance type feature may be:

[0063] 1) Determine the current rate of the battery under test at each internal resistance calculation point: At each internal resistance calculation point, measure or obtain the charge and discharge current of the battery under test, and compare the charge and discharge current with the rated capacity of the battery under test to determine the current rate.

[0064] 2) Determine the terminal voltage and internal resistance value: At each internal resistance calculation point, the internal resistance value is calculated by measuring the terminal voltage of the battery to be tested and the current of the battery to be tested.

[0065] 3) Determine the sampling period.

[0066] 4) Generate the first internal resistance type feature: The three parameters of current multiplication rate, terminal voltage, internal resistance value and sampling period are combined together to generate the first internal resistance type feature.

[0067] Optionally, the above-mentioned step S204 constructs the first combination feature of the battery to be tested according to the single cell difference feature, the first precondition feature and the first internal resistance type feature, including: determining multiple sets among multiple internal resistance calculation points, wherein each set includes multiple target internal resistance calculation points, and each target internal resistance calculation point is any internal resistance calculation point among the multiple internal resistance calculation points; determining the second precondition feature and the second internal resistance type feature of each target internal resistance calculation point of the battery to be tested in each set; splicing the multiple second precondition features and the multiple second internal resistance type features in each set to generate the splicing results corresponding to each set; and splicing each splicing result with the single cell difference feature respectively to generate multiple first combination features of the battery to be tested.

[0068] It can be understood that the method for determining the first combination feature is:

[0069] A combined feature is constructed in the form of monomer difference feature + n×(first precondition feature + first internal resistance type feature) (wherein, the embodiment of the present application is only an example. In actual applications, the above combined feature may not only include monomer difference feature and n×(first precondition feature + first internal resistance type feature, but also include, for example, charging time, number of charging cycles, etc.), n represents a plurality of predefined internal resistance calculation points in the cycle. Among them, the selection of n may include the following strategies: the internal resistance calculation point may be set according to a certain interval, for example: the interval is assumed to be 10%, then there are 10 internal resistance calculation points in total. For example, the remaining power of the battery to be tested in a charge and discharge cycle is 5%, 15%, 25%, 35%, ..., 95%, etc. In order to exclude the influence of temperature on the combined feature In response, each combined feature may include the precondition features and internal resistance type features of the internal resistance calculation points at multiple different temperatures; taking the above as an example, if the internal resistance values ​​at three temperatures need to be recorded in the combined feature, three internal resistance calculation points can be arbitrarily selected from the above 10 internal resistance calculation points as a set, and there are a total of 120 possible selection combinations, that is, 120 sets can be obtained; then the second precondition feature and the second internal resistance type feature of each target internal resistance calculation point are determined in each set; the three second precondition features and the three second internal resistance type features in each set are spliced ​​to generate the splicing results corresponding to each set, and 120 splicing results can be obtained; each splicing result is spliced ​​with the monomer difference feature respectively to generate multiple first combined features of the battery to be tested.

[0070] It should be noted that the subsequent second combination feature is also constructed in a similar way, but the second combination feature needs to use multiple or even all possible 120 combination features to build the SOH feature library; when using the first combination feature to calculate SOH, only any one of the first combination features can be used for matching and prediction.

[0071] It should be noted that the order in the combined features needs to be arranged from small to large and from left to right according to the temperature; in addition, if the features need to be further expanded, multiple different calculation intervals can be set to generate internal resistance calculation points, and then generate combined features.

[0072] The purpose of selecting n SOC points is to consider the influence of temperature and SOC on the internal resistance calculation. In a cycle, the battery generally has a certain temperature difference at different SOCs. Therefore, the embodiment of the present application only needs to select different SOC points as internal resistance calculation points to calculate the internal resistance, so that the influence of temperature and SOC can be considered at the same time.

[0073] It should be noted that if the influence of temperature and SOC on the internal resistance calculation is strictly considered, the cycle time points of the same SOC but different temperatures and the same temperature but different SOC can be selected to calculate the internal resistance. In the cycle process of the reference battery, there will be multiple cycles under the same SOH, so the cycle time points of the same SOC but different temperatures and the same temperature but different SOC can be selected as the internal resistance calculation points.

[0074] In summary, the internal resistance of the battery is closely related to the front cycle condition, but it is not realistic to directly store the front cycle condition, and its calculation and storage costs are too high. The embodiment of the present application uses EMA to record the voltage change and current change process in the front condition. EMA can not only realize weighted accumulation but is essentially a recursive form with low computational complexity. Therefore, the embodiment of the present application can realize the recording of the front condition at a low computational and storage cost, making it possible to calculate the online SOH considering the front condition.

[0075] Optionally, before determining the first health state of the battery to be tested according to the first combination feature and the health state feature library in the above step S206, the method also includes: determining multiple second combination features corresponding to the reference battery, and obtaining the second health state corresponding to the reference battery; combining each second combination feature with the second health state respectively to generate multiple first health state features, wherein each first health state feature is used to indicate the corresponding relationship between each second combination feature and the second health state; and constructing the health state feature library based on the multiple first health state features.

[0076] In practical applications, the first health status feature may include not only the second combination feature and the second health status, but may also include features such as the number of times the reference battery is charged.

[0077] It can be seen from the above embodiments that the embodiments of the present application need to use reference batteries to construct a health status feature library. The battery testing process is very time-consuming and labor-intensive. If the battery is tested using different set working conditions and the combined features are extracted from the test data, more resources are required. In reality, both battery manufacturers and vehicle manufacturers need to use a large number of batteries for various types of tests. The health status feature library can be constructed based on these existing test data, thereby avoiding high dedicated testing costs. The batteries related to the cycle and aging tests are selected as reference batteries, and their test data are used to construct the health status feature library.

[0078] Understandably, Figure 3 is a flow chart of a method for calculating a health status based on preconditions and internal resistance type characteristics according to an optional embodiment of the present application, such as Figure 3As shown, the method for calculating the health status based on the precondition and the internal resistance type characteristics includes steps S301 to S304, wherein steps S301 to S302 define a method for constructing a health status feature library, specifically:

[0079] Step S301, extracting basic features:

[0080] Extract basic features of the reference battery, where the basic features may specifically include: monomer type features, precondition features, internal resistance type features, etc.

[0081] Among them, in the process of constructing the internal resistance type feature, when the voltage change in one sampling period is not obvious enough, multiple sampling periods t can be used to calculate the internal resistance, and at this time the current value I needs to use the current average value of multiple sampling periods. That is, multiple sampling periods can be used to extract the internal resistance type feature of the reference battery.

[0082] Step S302, constructing a SOH feature library (i.e., the health status feature library of this application):

[0083] The second combined feature is constructed from the basic feature, and the SOH feature library is constructed in the form of combined feature + SOH value (ie, the second health state).

[0084] The specific process of constructing a combined feature from basic features is: concatenating the precondition features and internal resistance type features of multiple internal resistance calculation points, and concatenating the concatenated features with the monomer type feature identifier to construct a second combined feature.

[0085] Optionally, the step S206 of determining the first health state of the battery to be tested according to the first combined feature and the health state feature library includes: determining the first health state of the battery to be tested by one of the following methods:

[0086] (1) determining a first health state of the battery to be tested by matching the first combined feature with the health state feature library; specifically:

[0087] Match the first combined feature with the health status feature library to determine multiple second health status features that match the first combined feature in the health status feature library; denoise the multiple second health status features to determine multiple third health status features; integrate the health states in the multiple third health status features, and determine the first health state of the battery to be tested based on the integration result.

[0088] It is understandable that after the SOH feature library (i.e., the health state feature library) is constructed, the first health state of the battery to be tested can be determined by matching the first combined feature with the health state feature library. Specifically:

[0089] The first combination feature is matched with the SOH feature library, and the m SOH features (i.e., the second health state features) closest to the first combination feature are searched. The SOH (i.e., the second health state) corresponding to the m SOH features are integrated based on a certain strategy to obtain the first health state of the battery to be tested. The integration strategy may be an integration method such as mean value, weighted average, etc.

[0090] In practical applications, the second health status feature may be redundant or erroneous. In order to improve the robustness of SOH integration, it is necessary to sort and denoise the feature matching results before integration. Common denoising strategies include anomaly detection and maximum frequency statistics. The purpose of denoising is to eliminate the impact of outlier matching results on the final integration results.

[0091] In addition to the above methods, the denoising methods for the second health status feature also include:

[0092] Use kernel function and manifold similarity to eliminate outlier neighbor matching results. Kernel function: When the combined features are not separable in low dimensions, the combined features can be projected to high dimensions through the kernel function to improve the distinguishability of the features and find the neighbors of the features in high-dimensional space; Manifold similarity: If the feature data corresponding to the combined features are not separable in high dimensions, the combined features are projected to a low-dimensional manifold to improve the distinguishability of the combined features and find the neighbors of the combined features in the low-dimensional manifold space. Eliminate matching results with non-neighbor relationships.

[0093] In an alternative embodiment, Figure 3 Step S303 in the embodiment defines a technical solution for obtaining a first health state (i.e., SOH value) corresponding to the battery to be tested, specifically:

[0094] Step S303: denoising search.

[0095] Construct the first combination feature of the battery to be tested, match the first combination feature with the combination feature in the SOH feature library, obtain multiple second health status features, and then denoise the multiple second health status features, count the SOH values ​​corresponding to the successfully matched health status features, perform integration processing such as averaging and weighted averaging on the multiple SOH values, and then determine the SOH value of the battery to be tested.

[0096] (2) The first health state may also be determined by the following method: inputting the first combined feature into a relationship model fitted by the health state feature library to determine the first health state of the battery to be tested, wherein the relationship model is used to indicate the relationship between the first combined feature and the first health state; specifically:

[0097] The relationship model to be trained is trained according to the health status feature library to generate a relationship model to fit the relationship between the combined features of the battery and the health status; the relationship model to be trained is trained according to the model parameters to generate a relationship model; the first combined feature is input into the relationship model so that the relationship model outputs the first health status according to the first combined feature.

[0098] Based on the SOH feature library, the relationship between the second combination feature and SOH is fitted, and the first combination feature of the battery to be tested is input into the fitted relationship model to obtain the first health state of the battery to be tested. The specific model type can be selected according to the actual application scenario: if computing power is given priority, linear regression or nonlinear regression based on Taylor expansion can be used; if the fitting accuracy needs to be improved, a more complex machine learning algorithm can be used. For example: kernel function regression, decision tree regression, support vector regression, neural network, etc.

[0099] In an alternative embodiment, Figure 3 Step S304 in the embodiment defines another technical solution for obtaining the first health state (ie, SOH value) corresponding to the battery to be tested, specifically:

[0100] Step S304: relationship fitting.

[0101] The second combined feature in the SOH feature library is used as the model input, the SOH value (ie, the second health state) in the SOH feature library is used as the target value, the relationship between the combined feature and SOH is fitted, and then a relationship model is generated.

[0102] The generated relationship model can determine the SOH value of the battery to be tested, specifically:

[0103] A first combination feature of the battery to be tested is constructed, and the combination feature of the battery to be tested is input into a relational model so that the relational model outputs a SOH value of the battery to be tested.

[0104] It should be noted that the above steps S303 and S304 define two methods for determining the SOH value of the battery to be tested. In practical applications, one of them can be selected according to the actual situation, or the two methods can be selected to run in parallel. That is, the above steps S303 and S304 are not in a particular order.

[0105] In this embodiment, a device for determining the health status of a battery is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" may be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0106] Figure 4 : is a structural block diagram of a device for determining a battery health status according to an embodiment of the present application, the device comprising:

[0107] A first determination module 42, configured to determine a single cell difference characteristic of the battery to be tested, a first precondition characteristic of the battery to be tested at each internal resistance calculation point, and a first internal resistance type characteristic;

[0108] A construction module 44, configured to construct a first combination feature of the battery to be tested according to the monomer difference feature, the first precondition feature and the first internal resistance type feature;

[0109] The second determination module 46 is used to determine the first health state of the battery to be tested according to the first combined feature and a health state feature library, wherein the health state feature library is used to indicate the corresponding relationship between the combined feature and the health state of the reference battery.

[0110] Through the above-mentioned device, the single-cell difference characteristics of the resistor to be tested, the first precondition characteristics at each internal resistance calculation point, and the first internal resistance type characteristics are determined; the first combination characteristics of the battery to be tested are constructed according to the single-cell difference characteristics, the first precondition characteristics at each internal resistance calculation point, and the first internal resistance type characteristics; the first health state of the battery to be tested is determined according to the first combination characteristics and a health status characteristic library determined based on a reference battery for indicating the relationship between the combination characteristics of the battery and the health state of the battery. That is to say, the embodiment of the present application obtains the single-cell difference characteristics, the first precondition characteristics, and the first internal resistance type characteristics of the battery to be tested, and then determines the first combination characteristics of the battery to be tested according to the single-cell difference characteristics, the first precondition characteristics, and the first internal resistance type characteristics, and the first health state of the battery to be tested can be determined according to the first combination characteristics and the health status characteristic library for indicating the corresponding relationship between the combination characteristics of the battery and the health state of the battery. Most of the methods for calculating SOH in the related art eliminate the influence of temperature and SOC on the calculation of SOH through experimental table lookup, so that the internal resistance value of the battery is only related to the aging state. However, the pre-conditions of the internal resistance calculation point and the calculated internal resistance type also have a strong influence on the calculation of the internal resistance value. The related art does not take them into account as influencing factors in the calculation of SOH, which leads to low SOH calculation accuracy. The embodiment of the present application takes into account the influence of the pre-conditions (i.e., the first pre-condition characteristics) and the calculated internal resistance type (i.e., the first internal resistance type characteristics) on the health state of the battery, thereby improving the calculation accuracy of the health state of the battery. Therefore, the embodiment of the present application can solve the problem of low accuracy of the health state of the battery evaluated in the related art. Thereby improving the accuracy of the evaluation of the health state of the battery.

[0111] In an exemplary embodiment, the first determination module 42 is further used to determine the cumulative voltage rise, cumulative voltage drop, cumulative current increment and cumulative negative current increment corresponding to the battery to be tested according to an exponential moving average algorithm; determine the voltage change characteristics corresponding to the battery to be tested according to the cumulative voltage rise and the cumulative voltage drop; and determine the current change characteristics corresponding to the battery to be tested according to the cumulative current increment and the cumulative negative current increment, wherein the first precondition characteristics include: the voltage change characteristics and the current change characteristics.

[0112] In an exemplary embodiment, the first determination module 42 is further used to determine the current rate, terminal voltage and internal resistance value of the battery to be tested at each internal resistance calculation point, and determine the sampling period corresponding to each internal resistance calculation point of the battery to be tested; determine the first internal resistance type characteristic according to the current rate, the terminal voltage, the internal resistance value and the sampling period.

[0113] In an exemplary embodiment, the construction module 44 is also used to determine multiple sets among multiple internal resistance calculation points, wherein each set includes multiple target internal resistance calculation points, and each target internal resistance calculation point is any internal resistance calculation point among the multiple internal resistance calculation points; determine the second precondition feature and the second internal resistance type feature of each target internal resistance calculation point of the battery to be tested in each set; splice the multiple second precondition features and the multiple second internal resistance type features in each set to generate the splicing results corresponding to each set; splice each splicing result with the monomer difference feature respectively to generate multiple first combination features of the battery to be tested.

[0114] In an exemplary embodiment, the second determination module 46 is also used to determine multiple second combination features corresponding to the reference battery and obtain the second health state corresponding to the reference battery; combine each second combination feature with the second health state respectively to generate multiple first health state features, wherein each first health state feature is used to indicate the corresponding relationship between each second combination feature and the second health state; and construct the health state feature library based on the multiple first health state features.

[0115] In an exemplary embodiment, the second determination module 46 is further used to determine the first health state of the battery to be tested in one of the following ways: determining the first health state of the battery to be tested by feature matching the first combined feature with the health state feature library; determining the first health state of the battery to be tested by inputting the first combined feature into a relationship model generated by the health state feature library, wherein the relationship model is used to indicate the relationship between the first combined feature and the first health state.

[0116] In an exemplary embodiment, the second determination module 46 is further used to match the first combined feature with the health status feature library to determine multiple second health status features that match the first combined feature in the health status feature library; denoise the multiple second health status features to determine multiple third health status features; integrate the health states in the multiple third health status features, and determine the first health state of the battery to be tested based on the integration result.

[0117] In an exemplary embodiment, the second determination module 46 is also used to train the relationship model to be trained according to the health status feature library to generate a relationship model to fit the relationship between the combined characteristics of the battery and the health status; train the relationship model to be trained according to the model parameters to generate a relationship model; input the first combined feature into the relationship model so that the relationship model outputs the first health status according to the first combined feature.

[0118] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0119] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0120] S1, determining a single cell difference characteristic of a battery to be tested, a first precondition characteristic of the battery to be tested at each internal resistance calculation point, and a first internal resistance type characteristic;

[0121] S2, constructing a first combination feature of the battery to be tested according to the single cell difference feature, the first precondition feature and the first internal resistance type feature;

[0122] S3, determining a first health state of the battery to be tested according to the first combined feature and a health state feature library, wherein the health state feature library is used to indicate a corresponding relationship between a combined feature and a health state of a reference battery.

[0123] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0124] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0125] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0126] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0127] S1, determining a single cell difference characteristic of a battery to be tested, a first precondition characteristic of the battery to be tested at each internal resistance calculation point, and a first internal resistance type characteristic;

[0128] S2, constructing a first combination feature of the battery to be tested according to the single cell difference feature, the first precondition feature and the first internal resistance type feature;

[0129] S3, determining a first health state of the battery to be tested according to the first combined feature and a health state feature library, wherein the health state feature library is used to indicate a corresponding relationship between a combined feature and a health state of a reference battery.

[0130] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0131] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0132] Optionally, in this embodiment, the computer program product may be configured to perform the following steps through a computer program:

[0133] S1, determining a single cell difference characteristic of a battery to be tested, a first precondition characteristic of the battery to be tested at each internal resistance calculation point, and a first internal resistance type characteristic;

[0134] S2, constructing a first combination feature of the battery to be tested according to the single cell difference feature, the first precondition feature and the first internal resistance type feature;

[0135] S3, determining a first health state of the battery to be tested according to the first combined feature and a health state feature library, wherein the health state feature library is used to indicate a corresponding relationship between a combined feature and a health state of a reference battery.

[0136] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0137] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0138] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a battery health status, characterized in that: include: Determining a single cell difference characteristic of a battery to be tested, a first precondition characteristic of the battery to be tested at each internal resistance calculation point, and a first internal resistance type characteristic; Constructing a first combination feature of the battery to be tested according to the single cell difference feature, the first precondition feature and the first internal resistance type feature; The first health state of the battery to be tested is determined according to the first combined feature and a health state feature library, wherein the health state feature library is used to indicate the corresponding relationship between the combined feature and the health state of a reference battery.

2. The method according to claim 1, characterized in that: Determining the first precondition characteristic of the battery to be tested at each internal resistance calculation point includes: Determine the cumulative voltage rise, cumulative voltage drop, cumulative current increment and cumulative negative current increment corresponding to the battery to be tested according to an exponential moving average algorithm; The voltage change characteristics corresponding to the battery to be tested are determined according to the accumulated voltage rise and the accumulated voltage drop; and the current change characteristics corresponding to the battery to be tested are determined according to the accumulated current increment and the accumulated negative current increment, wherein the first precondition characteristics include: the voltage change characteristics and the current change characteristics.

3. The method according to claim 1, characterized in that Determining a first internal resistance type characteristic of the battery to be tested at each internal resistance calculation point includes: Determine the current multiplier, terminal voltage and internal resistance value of the battery to be tested at each internal resistance calculation point, and determine the sampling period corresponding to each internal resistance calculation point of the battery to be tested; The first internal resistance type characteristic is determined according to the current multiplication factor, the terminal voltage, the internal resistance value and the sampling period.

4. The method according to claim 1, characterized in that: Constructing a first combination feature of the battery to be tested according to the single cell difference feature, the first precondition feature and the first internal resistance type feature, including: Determine a plurality of sets from the plurality of internal resistance calculation points, wherein each set includes a plurality of target internal resistance calculation points, and each target internal resistance calculation point is any internal resistance calculation point from the plurality of internal resistance calculation points; Determine a second precondition feature and a second internal resistance type feature of each target internal resistance calculation point of the battery to be tested in each set; Splicing the plurality of second precondition features and the plurality of second internal resistance type features in each set to generate a splicing result corresponding to each set; Each splicing result is spliced ​​with the monomer difference feature respectively to generate a plurality of first combination features of the battery to be tested.

5. The method according to claim 1, characterized in that: Before determining the first health state of the battery to be tested according to the first combination feature and the health state feature library, the method further includes: determining a plurality of second combination features corresponding to the reference battery, and acquiring a second health state corresponding to the reference battery; Respectively combining each second combined feature with the second health state to generate a plurality of first health state features, wherein each first health state feature is used to indicate a corresponding relationship between each second combined feature and the second health state; The health status feature library is constructed according to the plurality of first health status features.

6. The method according to claim 1, characterized in that Determining a first health state of the battery to be tested according to the first combined feature and a health state feature library includes: Matching the first combined feature with the health status feature library to determine a plurality of second health status features matching the first combined feature in the health status feature library; performing denoising processing on the plurality of second health status features to determine a plurality of third health status features; The health states in the plurality of third health state characteristics are integrated, and the first health state of the battery to be tested is determined according to the integration result.

7. The method according to claim 1, characterized in that Determining a first health state of the battery to be tested according to the first combined feature and a health state feature library includes: Training the to-be-trained relationship model according to the health status feature library to generate a relationship model to fit the relationship between the combined features of the battery and the health status; The first combined feature is input into the relationship model so that the relationship model outputs the first health status according to the first combined feature.

8. A device for determining a battery health status, characterized in that: include: A first determination module is used to determine a single cell difference characteristic of a battery to be tested, a first precondition characteristic of the battery to be tested at each internal resistance calculation point, and a first internal resistance type characteristic; A construction module, configured to construct a first combination feature of the battery to be tested according to the monomer difference feature, the first precondition feature and the first internal resistance type feature; The second determination module is used to determine the first health state of the battery to be tested according to the first combined feature and a health state feature library, wherein the health state feature library is used to indicate the corresponding relationship between the combined feature and the health state of the reference battery.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

Images