A method for predicting battery health status, electronic device and readable storage medium

Abstract

The present disclosure provides a method for predicting the state of health of a battery, an electronic device, and a readable storage medium. The method includes: obtaining, based on a preset target operating condition, the cycle time, storage time, and average temperature of a target battery within a target period; determining the cycle capacity loss of the target battery based on the cycle time, the average temperature, and a first preset relationship; determining the storage capacity loss of the target battery based on the storage time, the average temperature, and a second preset relationship; obtaining a cycle health standard deviation based on the cycle capacity loss and a third preset relationship; obtaining a storage health standard deviation based on the storage capacity loss and a fourth preset relationship; and determining the predicted state of health of the target battery under the target operating condition based on the cycle capacity loss, the storage capacity loss, the cycle health standard deviation, and the storage health standard deviation. This method can more accurately predict the state of health of a battery.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of battery technology, and more specifically, to a method for predicting a battery health state, an electronic device, and a computer-readable storage medium. Background Art

[0002] Battery state of health (SOH) characterizes a battery's ability to store energy relative to a new battery. It expresses the battery's state from the beginning to the end of its life as a percentage, quantitatively describing the current battery performance. Battery SOH assessment provides guidance for battery usage, maintenance, and economic analysis.

[0003] In the existing technology, the battery health status can be evaluated in the following ways: first, using a battery simulation model to simulate the temperature changes of batteries at various locations inside the actual battery, and obtaining the battery capacity through screening and calculation, and the life of the actual battery at different locations; second, establishing an attenuation basic model with reference to existing battery usage data and life data, and obtaining the life data of the target battery through the attenuation basic model according to the usage data of the target battery; third, analyzing the correlation between the battery's calendar life capacity attenuation, AC internal resistance and DC internal resistance changes and temperature and voltage, and inferring the true life status of the battery cell from the accelerated aging test of the battery cell through a fitting formula.

[0004] However, the first method mentioned above only considers the impact of the temperature field on the life of the battery cell. However, in the battery life assessment, in addition to the different capacity attenuation caused by the difference in temperature distribution inside the battery, the inconsistent capacity attenuation caused by the factory manufacturing process of the battery cell itself cannot be ignored. The second method is to build a model based on existing battery data, and does not conduct life assessment from the principle. The assessment accuracy is poor and difficult to promote. The third method is mainly for the life prediction of single-cell accelerated testing, and does not consider the impact of the difference in temperature field inside the battery and the difference in cell consistency.

[0005] Therefore, it is very valuable to propose a method that can comprehensively evaluate battery temperature, cycle, storage, and consistency to accurately assess the health status of batteries. Summary of the Invention

[0006] One purpose of the embodiments of the present disclosure is to provide a new technical solution for accurately evaluating the health status of a battery.

[0007] According to a first aspect of the present disclosure, a method for predicting a battery health state is provided, comprising:

[0008] According to the preset target working conditions, obtain the cycle time, storage time and average temperature of the target battery within the target period;

[0009] Determining the cycle capacity loss of the target battery according to the cycle time, the average temperature, and a first preset relationship; determining the storage capacity loss of the target battery according to the storage time, the average temperature, and a second preset relationship;

[0010] Obtaining a circulation health state standard deviation based on the circulation capacity loss and the third preset relationship; obtaining a storage health state standard deviation based on the storage capacity loss and the fourth preset relationship;

[0011] The predicted health state of the target battery under the target operating condition is determined according to the cycle capacity loss, the storage capacity loss, the cycle health state standard deviation, and the storage health state standard deviation.

[0012] Optionally, determining the predicted health state of the target battery under the target operating condition according to the cycle capacity loss, the storage capacity loss, the cycle health state standard deviation, and the storage health state standard deviation includes:

[0013] Obtaining a median value of the health state of the battery cells of the target battery under the target operating condition according to the cycle capacity loss and the storage capacity loss;

[0014] Obtaining a distribution standard deviation of the health state of the target battery cells under the target operating condition according to the cycle health state standard deviation and the storage health state standard deviation;

[0015] The predicted health state of the target battery is obtained according to the median value of the health state and the standard deviation of the distribution of the health state.

[0016] Optionally, the method further includes:

[0017] Obtaining first health status data obtained by performing a charge-discharge cycle test on a first number of first battery cells at a corresponding cycle temperature, and second health status data obtained by performing a storage test on a second number of second battery cells at a corresponding storage temperature; wherein the first health status data is data indicating the health status of the first battery cells after performing a charge-discharge cycle test for a preset cycle time, and the second health status data is data indicating the health status of the second battery cells after performing a storage test for a preset storage time;

[0018] Determine first median data of the first number of the first battery cells based on the first health status data, and determine second median data of the second number of the second battery cells based on the second health status data, wherein the first median data is data representing an average value of the health status of the first number of the first battery cells after a charge-discharge cycle test for a preset cycle time, and the second median data is data representing an average value of the health status of the second number of the second battery cells after a storage test for a preset storage time;

[0019] The first preset relationship is obtained according to the cycle temperature and the first median data; and the second preset relationship is obtained according to the storage temperature and the second median data.

[0020] Optionally, the method further includes:

[0021] A first expression representing a corresponding relationship between the health state of the first battery cell and the cycle time is obtained based on the first health state data; and a second expression representing a corresponding relationship between the health state of the second battery cell and the storage time is obtained based on the second health state data.

[0022] Determining reference cycle times corresponding to a plurality of preset health states according to the first preset relationship, and determining reference storage times corresponding to the plurality of preset health states according to the second preset relationship;

[0023] Obtaining a first reference state of health corresponding to the first battery cell according to the reference cycle time and the first expression; and obtaining a second reference state of health corresponding to the battery cell according to the reference storage time and the second expression;

[0024] The third preset relationship is obtained based on the first reference health state, and the fourth preset relationship is obtained based on the second reference health state.

[0025] Optionally, obtaining the third preset relationship based on the first reference health state and obtaining the fourth preset relationship based on the second reference health state includes:

[0026] Determining a first standard deviation of a first reference health state corresponding to each of the preset health states at each cycle temperature; determining a second standard deviation of a second reference health state corresponding to each of the preset health states at each storage temperature;

[0027] The third preset relationship is obtained based on the first standard deviation, and the fourth preset relationship is obtained based on the second standard deviation.

[0028] Optionally, the first preset relationship is:

[0029] Q1=1-(c1*T1 d1 +e1)*t1 f1

[0030] Where Q1 is the cycle capacity loss, T1 is the cycle temperature, t1 is the cycle time, c1, d1, e1, and f1 are fitting coefficients;

[0031] The second preset relationship is:

[0032] Q2=1-(c2*T2 d2 +e2)*t2 f2

[0033] Among them, Q2 is the storage capacity loss, T2 is the storage temperature, t2 is the storage time, c2, d2, e2, and f2 are fitting coefficients.

[0034] Optionally, the third preset relationship is:

[0035] SD1=A1*exp(SOH1 B1 +C1)+D1

[0036] SOH1=1-Q1

[0037] Among them, SD1 is the standard deviation of the circulation health status, Q1 is the circulation capacity loss, A1, B1, C1, and D1 are fitting coefficients;

[0038] The fourth preset relationship is:

[0039] SD2=A2*exp(SOH2 B2 +C2)+C2

[0040] SOH2=1-Q2

[0041] Among them, SD2 is the standard deviation of storage health status, Q2 is the storage capacity loss, and A2, B2, C2, and D2 are fitting coefficients.

[0042] Optionally, obtaining the average temperature of the target battery within a target time period includes:

[0043] Obtaining a cell temperature of each cell contained in the target battery within the target time period;

[0044] An average value of the battery cell temperatures is determined as the average temperature.

[0045] According to a second aspect of the present disclosure, an electronic device is further provided, including a memory and a processor, wherein the memory is used to store a computer program; the processor is used to execute the computer program to implement the method according to the first aspect of the present disclosure.

[0046] According to a third aspect of the present disclosure, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0047] Through this embodiment, the target battery's cycle time, storage time, and average temperature are used to determine the target battery's cycle capacity loss and storage capacity loss. The target battery's cycle health standard deviation and storage health standard deviation are then obtained based on the cycle capacity loss and storage capacity loss. The target battery's predicted health state is then predicted based on the cycle capacity loss, storage capacity loss, cycle health standard deviation, and storage health standard deviation. This embodiment, starting from the perspective of the battery cell, fully considers the cycle and storage consistency deviations caused by the battery cell manufacturing process, enabling a more accurate prediction of the target battery's predicted health state with lower computational cost.

[0048] Other features and advantages of the embodiments of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the embodiments of the present disclosure.

[0050] Figure 1 is a schematic block diagram of a hardware configuration of an electronic device that can be used to implement an embodiment of the present disclosure;

[0051] Figure 2 is a flowchart of a method for predicting battery health status according to one embodiment;

[0052] Figure 3 is a flowchart of a method for predicting battery health status according to another embodiment;

[0053] Figure 4 is a block diagram of an electronic device according to an embodiment. DETAILED DESCRIPTION

[0054] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0055] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0056] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0057] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0058] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0059] <Hardware Configuration>

[0060] Figure 1 It is a structural diagram of an electronic device that can be used to implement the embodiments of the present disclosure.

[0061] The electronic device 1000 can be a smart phone, a portable computer, a desktop computer, a tablet computer, a server, etc., which is not limited here.

[0062] The electronic device 1000 may include, but is not limited to, a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and the like. The processor 1100 may be, for example, a central processing unit (CPU), a graphics processing unit (GPU), or a microprocessor (MCU), and is configured to execute a computer program. The computer program may be written using an instruction set such as an x86, Arm, RISC, MIPS, or SSE architecture. The memory 1200 may include, for example, ROM (read-only memory), RAM (random access memory), or a non-volatile memory such as a hard disk. The interface device 1300 may include, for example, a USB interface, a serial interface, or a parallel interface. The communication device 1400 may be capable of wired communication using optical fiber or cable, or wireless communication, and may specifically include WiFi, Bluetooth, 2G / 3G / 4G / 5G, and the like. The display device 1500 may be, for example, an LCD display or a touchscreen display. The input device 1600 may include, for example, a touchscreen, a keyboard, or somatosensory input. The speaker 1700 is used to output audio signals, and the microphone 1800 is used to collect audio signals.

[0063] As used in the embodiments of the present disclosure, the memory 1200 of the electronic device 1000 is used to store a computer program, which is used to control the processor 1100 to operate to implement the method according to the embodiments of the present disclosure. Technicians can design the computer program according to the scheme disclosed in the present disclosure. How the computer program controls the processor to operate is well known in the art and will not be described in detail here. The electronic device 1000 can be installed with an intelligent operating system (such as Windows, Linux, Android, IOS, etc.) and application software.

[0064] It should be understood by those skilled in the art that although Figure 1 , multiple devices of the electronic device 1000 are shown; however, the electronic device 1000 of the embodiment of the present disclosure may only involve some of the devices, for example, only the processor 1100 and the memory 1200.

[0065] Hereinafter, various embodiments and examples according to the present invention will be described with reference to the accompanying drawings.

[0066] <Method Example>

[0067] Figure 2 FIG. 1 is a flow chart of a method for predicting a battery health state according to an embodiment, which may be implemented by an electronic device. For example, the electronic device may be Figure 1 The electronic device 1000 is shown.

[0068] like Figure 2 As shown, the battery health status prediction method of this embodiment may include steps S2100 to S2400 as shown below:

[0069] Step S2100 , obtaining the cycle time, storage time, and average temperature of the target battery within the target period according to the preset target operating conditions.

[0070] In this embodiment, the target operating condition may be disassembled to obtain the cycle time, storage time, and average temperature of the target battery.

[0071] The target period may be pre-set based on the application scenario or specific needs. For example, the target period may be within the past day.

[0072] The target battery of this embodiment may include at least one battery cell. In the case where the target battery includes multiple battery cells, the multiple battery cells may be connected in series and / or in parallel.

[0073] In one embodiment, obtaining the average temperature of the target battery during the target period may include: obtaining the cell temperature of each cell included in the target battery during the target period; and determining an average value of the cell temperatures as the average temperature of the target battery during the target period.

[0074] In this embodiment, the cell temperature of each cell within the target time period may be obtained based on thermal simulation.

[0075] The cycle in this embodiment is the charge and discharge cycle of the target battery. Then, the cycle time is the time of the charge and discharge cycle.

[0076] This embodiment predicts the health state of the target battery according to the average value of the cell temperatures of the cells in the target battery within the target time period, which can make the prediction result more accurate.

[0077] Step S2200, determining the cycle capacity loss of the target battery according to the cycle time, average temperature and a first preset relationship; determining the storage capacity loss of the target battery according to the storage time, average temperature and a second preset relationship.

[0078] In this embodiment, cycle capacity loss refers to the capacity loss of the target battery due to charge and discharge cycles, and storage capacity loss refers to the capacity loss of the target battery due to storage. The first preset relationship may represent the corresponding relationship between cycle time, cycle temperature, and the capacity loss of the battery due to charge and discharge cycles. The second preset relationship may represent the corresponding relationship between storage time, storage temperature, and the capacity loss of the battery due to storage.

[0079] In one embodiment, the first preset relationship can be expressed as:

[0080] Q1=1-(c1*T1 d1 +e1)*t1 f1

[0081] Among them, Q1 is the cycle capacity loss, T1 is the cycle temperature, t1 is the cycle time, c1, d1, e1, and f1 are fitting coefficients.

[0082] In this embodiment, the average temperature may be used as the cycle temperature, and the cycle time and the cycle temperature may be substituted into the first preset relationship to obtain the cycle capacity loss.

[0083] In one embodiment, the second preset relationship can be expressed as:

[0084] Q2=1-(c2*T2 d2 +e2)*t2 f2

[0085] Among them, Q2 is the storage capacity loss, T2 is the storage temperature, t2 is the storage time, c2, d2, e2, and f2 are fitting coefficients.

[0086] In this embodiment, the average temperature may be used as the storage temperature, and the storage time and the storage temperature may be substituted into the second preset relationship to obtain the storage capacity loss.

[0087] Step S2300: Obtain a circulation health state standard deviation based on the circulation capacity loss and the third preset relationship; obtain a storage health state standard deviation based on the storage capacity loss and the fourth preset relationship.

[0088] In this embodiment, the third preset relationship may represent a corresponding relationship between the circulatory health status and the standard deviation, and the fourth preset relationship may represent a corresponding relationship between the storage health status and the standard deviation.

[0089] Among them, the cycle health status can be the predicted health status of the target battery after the capacity loss caused by cycling, specifically the difference between 1 and the cycle capacity loss; the storage health status can be the predicted health status of the target battery after the capacity loss caused by storage, specifically the difference between 1 and the storage capacity loss.

[0090] The standard deviation of the cycle health state corresponding to the cycle health state may represent the standard deviation of the distribution of the target battery cells at the cycle health state due to the manufacturing process. The standard deviation of the storage health state corresponding to the storage health state may represent the standard deviation of the distribution of the target battery cells at the storage health state due to the manufacturing process.

[0091] The state of health (SOH) of a battery can be the percentage of the battery's fully charged capacity relative to its rated capacity.

[0092] In one embodiment, the third preset relationship can be expressed as:

[0093] SD1=A1*exp(SOH1 B1 +C1)+D1

[0094] SOH1=1-Q1

[0095] Among them, SD1 is the standard deviation of the circulatory health status, Q1 is the cycle capacity loss, SOH1 is the circulatory health status, A1, B1, C1, and D1 are fitting coefficients;

[0096] In one embodiment, the fourth preset relationship can be expressed as:

[0097] SD2=A2*exp(SOH2 B2 +C2)+C2

[0098] SOH2=1-Q2

[0099] Among them, SD2 is the standard deviation of storage health status, Q2 is the storage capacity loss, SOH2 is the storage health status, and A2, B2, C2, and D2 are fitting coefficients.

[0100] Step S2400 , determining the predicted health state of the target battery under the target operating condition based on the cycle capacity loss, storage capacity loss, cycle health state standard deviation, and storage health state standard deviation.

[0101] In one embodiment of the present disclosure, determining the predicted health state of the target battery based on the cycle capacity loss, storage capacity loss, cycle health state standard deviation, and storage health state standard deviation may include steps S2410 to S2430 as shown below:

[0102] Step S2410 , obtaining a median value of the health status of the target battery cells under the target operating conditions based on the cycle capacity loss and the storage capacity loss.

[0103] In this embodiment, the median value of the health state SOH3 can be expressed as:

[0104] SOH3=1-Q1-Q2

[0105] Step S2420 , obtaining the distribution standard deviation of the health state of the target battery cells under the target operating condition according to the cycle health state standard deviation and the storage health state standard deviation.

[0106] Among them, the standard deviation of the health status distribution can reflect the consistency deviation of the health status of each cell in the target battery due to the manufacturing process.

[0107] In this embodiment, the standard deviation of the circulatory health state and the standard deviation of the stored health state may be weightedly summed to obtain the distribution standard deviation of the health state.

[0108] In an example, the standard deviation SD3 of the distribution of health states can be expressed as:

[0109] SD3=SD1*λ1+SD2*λ2

[0110] Among them, SD1 is the standard deviation of the circulation health state, SD2 is the standard deviation of the storage health state, and λ1 and λ2 are preset weights.

[0111] In another example, the standard deviation SD3 of the distribution of health states can be expressed as:

[0112]

[0113] Among them, Q1 is the cycle capacity loss, Q2 is the storage capacity loss, SD1 is the cycle health state standard deviation, and SD2 is the storage health state standard deviation.

[0114] Step S2430 : Obtain the predicted health state of the target battery according to the median of the health state and the standard deviation of the health state distribution.

[0115] In one embodiment, the predicted health state of the target battery may be obtained based on the median of the health state, the standard deviation of the health state distribution, and a fifth preset relationship.

[0116] Among them, the fifth preset relationship can be expressed as:

[0117] SOH=SOH3±n*SD3

[0118] Among them, SOH represents the predicted health state of the target battery, SOH3 is the median of the health state, SD3 is the standard deviation of the health state distribution, and n is the number of standard deviations corresponding to the preset confidence interval.

[0119] The confidence interval may be pre-set according to the application scenario or specific requirements, for example, the confidence interval may be 68%, 95%, or 99%.

[0120] n is the number of standard deviations corresponding to the confidence interval. For example, if the confidence interval is 68%, the corresponding number of standard deviations n may be 1. For another example, if the confidence interval is 95%, the corresponding number of standard deviations n may be 2. For another example, if the confidence interval is 99%, the corresponding number of standard deviations n may be 3.

[0121] Through this embodiment, the target battery's cycle time, storage time, and average temperature are used to determine the target battery's cycle capacity loss and storage capacity loss. The target battery's cycle health standard deviation and storage health standard deviation are then obtained based on the cycle capacity loss and storage capacity loss. The target battery's predicted health state is then predicted based on the cycle capacity loss, storage capacity loss, cycle health standard deviation, and storage health standard deviation. This embodiment, starting from the perspective of the battery cell, fully considers the cycle and storage consistency deviations caused by the battery cell manufacturing process, enabling a more accurate prediction of the target battery's predicted health state with lower computational cost.

[0122] In one embodiment of the present disclosure, before executing step S2200, the method further includes: Figure 3 Steps S3100 to S3300 are shown:

[0123] Step S3100: Acquire first health status data of a first number of first battery cells obtained by performing a charge-discharge cycle test at a corresponding cycle temperature, and obtain second health status data of a second number of second battery cells obtained by performing a storage test at a corresponding storage temperature.

[0124] Among them, the first health status data is data representing the health status of the first battery cell after a charge and discharge cycle test of a preset cycle time, and the second health status data is data representing the health status of the second battery cell after a storage test of a preset storage time.

[0125] In this embodiment, the first battery cell and the second battery cell may be battery cells of the same model, and any two of the first battery cell and the second battery cell may be produced in the same or different batches. In one example, the first number of first battery cells and the second number of second battery cells may be all batches covering the battery cells of the same model.

[0126] Furthermore, at least one cycle temperature and at least one storage temperature can be set in advance according to the application scenario or specific needs. For example, the cycle temperature may include 25°C, 35°C and 45°C, and the storage temperature may include 25°C, 45°C and 60°C. Then, the first number of first battery cells may be divided into three parts in advance. The first part of the first battery cells may be set to perform a charge and discharge cycle with a discharge depth of 100% and a charge and discharge current of 0.5 times in a 25°C environment; the second part of the first battery cells may be set to perform a charge and discharge cycle with a discharge depth of 100% and a charge and discharge current of 0.5 times in a 35°C environment; the third part of the first battery cells may be set to perform a charge and discharge cycle test with a discharge depth of 100% and a charge and discharge current of 0.5 times in a 45°C environment. The second number of second battery cells may be divided into three parts in advance, and the second battery cells in the first part may be set to perform a storage test with a state of charge (SOC) of 100% in a 25°C environment; the second battery cells in the second part may be set to perform a storage test with a state of charge (SOC) of 100% in a 45°C environment; and the second battery cells in the third part may be set to perform a storage test with a state of charge of 100% in a 60°C environment.

[0127] When the charge-discharge cycle test is completed, the first health status data of each first battery cell can be obtained. When the storage test is completed, the second health status data of each second battery cell can be obtained.

[0128] The preset cycle time and the preset storage time in this embodiment can be multiple times set in advance according to application scenarios or specific needs. For example, the preset cycle time can include 1 day, 2 days, ..., 200 days, and the preset storage time can include 1 day, 2 days, ..., 300 days.

[0129] Step S3200: Determine the first median data of a first number of first battery cells based on the first health status data, and determine the second median data of a second number of second battery cells based on the second health status data, wherein the first median data is data representing the average value of the health status of the first number of first battery cells after a charge and discharge cycle test for a preset cycle time, and the second median data is data representing the average value of the health status of the second number of second battery cells after a storage test for a preset storage time.

[0130] In this embodiment, for each preset cycle time, the average health status of a first number of first battery cells after the charge and discharge cycle test for the preset cycle time is determined to obtain first median data; for each preset storage time, the average health status of a second number of second battery cells after the storage test for the storage time is determined to obtain second median data.

[0131] Step S3300: obtaining a first preset relationship based on the cycle temperature and the first median data; and obtaining a second preset relationship based on the storage temperature and the second median data.

[0132] In this embodiment, the initial form of establishing the first preset relationship based on the first median data may be:

[0133] Q1=1-g1*t1 f1

[0134] Where g1 is the first fitting coefficient.

[0135] The initial form of establishing the first preset relationship based on the second median data is:

[0136] Q2=1-g2*t2 f2

[0137] Where g2 is the second fitting coefficient.

[0138] The first fitting coefficient is different at different cycle temperatures, and the second fitting coefficient is different at different storage temperatures.

[0139] Therefore, a third expression representing the correspondence between the first fitting coefficient g1 and the circulation temperature T1 can be established based on the first health status data of each first battery cell and the corresponding circulation temperature, and a fourth expression representing the correspondence between the second fitting coefficient g2 and the storage temperature T2 can be established based on the second health status data of each second battery cell and the corresponding storage temperature.

[0140] The third expression in this embodiment can be expressed as:

[0141] g1=c1*T1 d1+e1

[0142] The fourth expression in this embodiment can be expressed as:

[0143] g2=c2*T2 d2 +e2

[0144] When predicting the predicted health status of the target battery, based on the first preset relationship and the second preset relationship constructed through this embodiment, the median of the cycle capacity loss of the target battery caused by the charge and discharge cycle and the median of the storage capacity loss of the target battery caused by storage can be determined, and then the median of the predicted health status of the target battery can be determined based on the median of the cycle capacity loss and the median of the storage capacity loss.

[0145] In one embodiment of the present disclosure, before executing step S2200, the method further includes: Figure 3 Steps S3400 to S3700 are shown:

[0146] Step S3400: Based on the first health status data, a first expression representing the corresponding relationship between the health status of the first battery cell and the cycle time is obtained; based on the second health status data, a second expression representing the corresponding relationship between the health status of the second battery cell and the storage time is obtained.

[0147] In this embodiment, the first expression of each first battery cell may be obtained based on the first health status data of the first battery cell. The first expression may be expressed as:

[0148] SOH1=a1*t1 b1

[0149] Wherein, SOH1 is the health status of the first battery cell, t1 is the cycle time, and a1 and b1 are fitting coefficients.

[0150] In this embodiment, the second expression of each second battery cell may be obtained based on the second health status data of the second battery cell. The second expression may be expressed as:

[0151] SOH2=a2*t2 b2

[0152] Among them, SOH2 is the health status of the first battery cell, t2 is the storage time, a2 and b2 are fitting coefficients.

[0153] Step S3500: Determine reference cycle times corresponding to a plurality of preset health states according to a first preset relationship, and determine reference storage times corresponding to a plurality of preset health states according to a second preset relationship.

[0154] The preset health status in this embodiment can be pre-set according to the application scenario or specific needs. For example, the preset health status may include 95%, 90%, 85%, 80%, 75%, 70%, 65%, and 60%.

[0155] Each preset health state may be substituted into the first preset relationship to obtain the reference cycle time corresponding to each preset health state. Each preset health state may be substituted into the second preset relationship to obtain the reference cycle time corresponding to each preset health state.

[0156] Step S3600: Obtain a first reference state of health corresponding to the first battery cell according to the reference cycle time and the first expression; and obtain a second reference state of health corresponding to the battery cell according to the reference storage time and the second expression.

[0157] In this embodiment, each reference cycle time may be substituted into the first expression of each first battery cell to obtain the first reference state of health of each first battery cell after the charge-discharge cycle test for each reference cycle time. Each reference storage time may be substituted into the second expression of each second battery cell to obtain the second reference state of health of each second battery cell after the storage test for each reference storage time.

[0158] Step S3700: Obtain a third preset relationship based on the first reference health state, and obtain a fourth preset relationship based on the second reference health state.

[0159] This embodiment obtains a reference cycle time and a reference storage time corresponding to a preset health state based on the first preset relationship and the second preset relationship, and then obtains a first reference health state corresponding to the first battery cell based on the reference cycle time and the first expression, and obtains a second reference health state corresponding to the battery cell based on the reference storage time and the second expression. The third preset relationship is fitted according to the first reference health state, and the fourth preset relationship is fitted according to the second reference health state. This can shorten the test time of the first battery cell and the second battery cell and reduce the test cost.

[0160] In one embodiment of the present disclosure, the third preset relationship is obtained according to the first reference health state, and the fourth preset relationship is obtained according to the second reference health state, which may include: Figure 3 Steps S3710 to S3720 shown:

[0161] Step S3710, determining a first standard deviation of a first reference health state corresponding to each preset health state at each cycle temperature; determining a second standard deviation of a second reference health state corresponding to each preset health state at each storage temperature.

[0162] In this embodiment, the first reference health states of a first number of first battery cells can be grouped according to the preset health state and the cycle temperature. When the number of preset health states is 8 and the cycle temperature is 3, 8*3=24 groups of first reference health states can be obtained. Each group of first reference health states can be determined by the reference cycle time corresponding to the same preset health state, and the cycle temperature of the corresponding first battery cells is the same.

[0163] Furthermore, the standard deviation of each group of first reference health states may be determined as the first standard deviation.

[0164] In this embodiment, the second reference health states of the second number of second battery cells can be grouped according to the preset health state and the cycle temperature. When the number of preset health states is 8 and the storage temperature is 3, 8*3=24 groups of second reference health states can be obtained. Each group of second reference health states can be determined by the reference cycle time corresponding to the same preset health state, and the storage temperature of the corresponding second battery cells is the same.

[0165] Furthermore, the standard deviation of each group of second reference health states may be determined as the second standard deviation.

[0166] Step S3720: Obtain a third preset relationship based on the first standard deviation, and obtain a fourth preset relationship based on the second standard deviation.

[0167] In this embodiment, a third preset relationship representing the correspondence between the circulatory health state and the standard deviation may be obtained based on the standard deviation of each set of first reference health states and the corresponding preset health state. A fourth preset relationship representing the correspondence between the stored health state and the standard deviation may be obtained based on the standard deviation of each set of second reference health states and the corresponding preset health state.

[0168] When predicting the predicted health status of the target battery, based on the third preset relationship and the fourth preset relationship constructed through this embodiment, the consistency deviation of the target battery in reaching the circulation health status due to the manufacturing process and the consistency deviation of the target battery in reaching the storage health status due to the manufacturing process can be determined. Then, based on the consistency deviation of the target battery in reaching the circulation health status due to the manufacturing process and the consistency deviation of the target battery in reaching the storage health status due to the manufacturing process, the consistency deviation of the health status of the target battery caused by the manufacturing process can be determined.

[0169] In another embodiment of the present disclosure, the third preset relationship is obtained based on the first standard deviation, and the fourth preset relationship is obtained based on the second standard deviation. It may also include: determining the standard deviation of the first reference health state corresponding to each preset health state, and the standard deviation of the second reference health state corresponding to each preset health state; obtaining the third preset relationship based on the standard deviation of the first reference health state corresponding to each preset health state, and obtaining the fourth preset relationship based on the standard deviation of the second reference health state corresponding to each preset health state.

[0170] <Equipment Example>

[0171] Figure 4 is a schematic diagram of the hardware structure of an electronic device according to another embodiment.

[0172] like Figure 4 As shown, the electronic device 4000 includes a processor 4100 and a memory 4200, wherein the memory 4200 is used to store an executable computer program, and the processor 4100 is used to execute a method as any of the above method embodiments under the control of the computer program.

[0173] The electronic device 4000 can be an electronic product such as a smart phone, a portable computer, a desktop computer, a tablet computer, a server, a computer cluster, etc.

[0174] Each module of the above electronic device 4000 can be implemented by the processor 4100 in this embodiment executing a computer program stored in the memory 4200, or can be implemented by other circuit structures, which is not limited here.

[0175] <Computer-readable storage medium embodiment>

[0176] This embodiment provides a computer-readable storage medium, which stores executable commands. When the executable commands are executed by a processor, the method described in any method embodiment of this specification is executed.

[0177] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0178] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0179] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0180] The computer program instructions for performing the operation of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), is personalized by utilizing the state information of the computer readable program instructions, and the electronic circuit can execute the computer readable program instructions, thereby realizing various aspects of the present invention.

[0181] Various aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0182] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0183] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0184] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of an instruction, and the module, program segment or part of the instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0185] While various embodiments of the present invention have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A method for predicting a battery health state, comprising: According to the preset target working conditions, obtain the cycle time, storage time and average temperature of the target battery within the target period; determining a cycle capacity loss of the target battery according to the cycle time, the average temperature, and a first preset relationship; determining a storage capacity loss of the target battery according to the storage time, the average temperature, and a second preset relationship; Obtaining a circulation health state standard deviation according to the circulation capacity loss and a third preset relationship; Obtaining a storage health status standard deviation based on the storage capacity loss and a fourth preset relationship; The predicted health state of the target battery under the target operating condition is determined according to the cycle capacity loss, the storage capacity loss, the cycle health state standard deviation, and the storage health state standard deviation.

2. The method according to claim 1, wherein determining the predicted health state of the target battery under the target operating condition based on the cycle capacity loss, the storage capacity loss, the cycle health state standard deviation, and the storage health state standard deviation comprises: Obtaining a median value of the health state of the battery cells of the target battery under the target operating condition according to the cycle capacity loss and the storage capacity loss; Obtaining a distribution standard deviation of the health state of the target battery cells under the target operating condition according to the cycle health state standard deviation and the storage health state standard deviation; The predicted health state of the target battery is obtained according to the median value of the health state and the standard deviation of the distribution of the health state.

3. The method according to claim 1, further comprising: Obtaining first health status data obtained by performing a charge-discharge cycle test on a first number of first battery cells at a corresponding cycle temperature, and second health status data obtained by performing a storage test on a second number of second battery cells at a corresponding storage temperature; wherein the first health status data is data indicating the health status of the first battery cells after performing a charge-discharge cycle test for a preset cycle time, and the second health status data is data indicating the health status of the second battery cells after performing a storage test for a preset storage time; Determine first median data of the first number of the first battery cells based on the first health status data, and determine second median data of the second number of the second battery cells based on the second health status data, wherein the first median data is data representing an average value of the health status of the first number of the first battery cells after a charge-discharge cycle test for a preset cycle time, and the second median data is data representing an average value of the health status of the second number of the second battery cells after a storage test for a preset storage time; The first preset relationship is obtained according to the cycle temperature and the first median data; and the second preset relationship is obtained according to the storage temperature and the second median data.

4. The method according to claim 3, further comprising: Obtaining, according to the first health status data, a first expression representing a corresponding relationship between the health status of the first battery cell and a cycle time; obtaining, according to the second health status data, a second expression representing a corresponding relationship between the health status of the second battery cell and the storage time; Determining reference cycle times corresponding to a plurality of preset health states according to the first preset relationship, and determining reference storage times corresponding to the plurality of preset health states according to the second preset relationship; Obtaining a first reference health state corresponding to the first battery cell according to the reference cycle time and the first expression; Obtaining a second reference health state of the corresponding battery cell according to the reference storage time and the second expression; The third preset relationship is obtained based on the first reference health state, and the fourth preset relationship is obtained based on the second reference health state.

5. The method according to claim 4, wherein obtaining the third preset relationship based on the first reference health state and obtaining the fourth preset relationship based on the second reference health state comprises: Determining a first standard deviation of a first reference health state corresponding to each of the preset health states at each cycle temperature; determining a second standard deviation of a second reference health state corresponding to each of the preset health states at each storage temperature; The third preset relationship is obtained based on the first standard deviation, and the fourth preset relationship is obtained based on the second standard deviation.

6. The method according to claim 1 or 3, wherein the first preset relationship is: Q1=1-(c1*T1 d1 +e1)*t1 f1 in, Q1 is the cycle capacity loss, T1 is the cycle temperature, t1 is the cycle time, c1, d1, e1, f1 are fitting coefficients; The second preset relationship is: <h2 style=";text-align:left;direction:ltr">Q2 = 1 - (c2 * T2<h2 style=";text-align:left;direction:ltr"> d2 <h2 style=";text-align:left;direction:ltr"> +e2)*t2<h2 style=";text-align:left;direction:ltr"> f2 Among them, Q2 is the storage capacity loss, T2 is the storage temperature, t2 is the storage time, c2, d2, e2, and f2 are fitting coefficients.

7. The method according to claim 1 or 5, wherein the third preset relationship is: SD1=A1*exp(SOH1 B1 +C1)+D1 SOH1=1-Q1 in, SD1 is the standard deviation of the circulatory health status, Q1 is the circulatory capacity loss, A1, B1, C1, and D1 are fitting coefficients; The fourth preset relationship is: <h2 style=";text-align:left;direction:ltr">SD2 = A2 * exp(SOH2<h2 style=";text-align:left;direction:ltr"> B2 <h2 style=";text-align:left;direction:ltr"> +C2)+D2 SOH2=1-Q2 Among them, SD2 is the standard deviation of storage health status, Q2 is the storage capacity loss, and A2, B2, C2, and D2 are fitting coefficients.

8. The method according to claim 1, obtaining the average temperature of the target battery within a target period, comprising: Obtaining a cell temperature of each cell contained in the target battery within the target time period; An average value of the battery cell temperatures is determined as the average temperature.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the method according to any one of claims 1 to 8 under the control of the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method according to any one of claims 1 to 8 when executed by a processor.

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