WO2024093269A1 - 电池健康状态的预测方法、电子设备及可读存储介质 - Google Patents

电池健康状态的预测方法、电子设备及可读存储介质 Download PDF

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Publication number
WO2024093269A1
WO2024093269A1 PCT/CN2023/102163 CN2023102163W WO2024093269A1 WO 2024093269 A1 WO2024093269 A1 WO 2024093269A1 CN 2023102163 W CN2023102163 W CN 2023102163W WO 2024093269 A1 WO2024093269 A1 WO 2024093269A1
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Prior art keywords
storage
cycle
health
health state
standard deviation
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PCT/CN2023/102163
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English (en)
French (fr)
Inventor
齐天煜
焦新艳
雷松
郭佳威
张文千
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比亚迪股份有限公司
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Publication of WO2024093269A1 publication Critical patent/WO2024093269A1/zh

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery

Definitions

  • 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.
  • the battery health status is generally evaluated in the following ways: First, use a battery simulation model to simulate the temperature changes of batteries at various locations inside the actual battery, obtain the battery capacity through screening and calculation, and obtain the life of the actual battery at different locations; Second, establish an attenuation basic model with reference to the existing battery usage data and life data, and obtain the life data of the target battery through the attenuation basic model based on the usage data of the target battery; Third, analyze the correlation between the battery's calendar life capacity attenuation, AC internal resistance and DC internal resistance changes and temperature and voltage, and infer the true life status of the battery cell from the accelerated aging test through the fitting formula.
  • the first method mentioned above only considers the impact of the temperature field on the life of the battery cell, and ignores the inconsistent capacity attenuation of the battery cell itself due to the factory manufacturing process.
  • the second method is to build a model based on existing battery data, and does not conduct life assessment in principle. The assessment accuracy is poor and difficult to promote.
  • the third method is mainly aimed at the life prediction of single-cell accelerated testing, and does not consider the impact of differences in temperature fields within the battery and differences in cell consistency.
  • 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.
  • a method for predicting a battery health state comprising:
  • the cycle time, storage time and average temperature of the target battery in the target period are obtained;
  • a cycle health state standard deviation is obtained;
  • a storage health state standard deviation is obtained;
  • the storage capacity loss According to the cycle capacity loss, the storage capacity loss, the cycle health state standard deviation, the storage The target battery is stored in a state of health standard deviation to determine the predicted state of health of the target battery under the target operating condition.
  • 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:
  • the predicted health state of the target battery is obtained according to the median of the health state and the distribution standard deviation of the health state.
  • the method further includes:
  • 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
  • second health status data obtained by performing a storage test on a second number of second battery cells at a corresponding storage temperature
  • first health status data is data indicating the health status of the first battery cell after performing a charge-discharge cycle test for a preset cycle time
  • second health status data is data indicating the health status of the second battery cell after performing a storage test for a preset storage time
  • 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 of a preset cycle time
  • 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 of 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.
  • the method further includes:
  • a first expression representing the corresponding relationship between the health status of the first battery cell and the cycle time is obtained;
  • a second expression representing the corresponding relationship between the health status of the second battery cell and the storage time is obtained;
  • a first reference health state corresponding to the first battery cell is obtained;
  • a second reference health state corresponding to the battery cell is obtained;
  • the third preset relationship is obtained according to the first reference health state
  • the fourth preset relationship is obtained according to the second reference health state.
  • the third preset relationship is obtained according to the first reference health state, 2.
  • a fourth preset relationship is obtained, including:
  • the third preset relationship is obtained according to the first standard deviation
  • the fourth preset relationship is obtained according to the second standard deviation.
  • Q1 is the cycle capacity loss
  • T1 is the cycle temperature
  • t1 is the cycle time
  • c1, d1, e1, f1 are fitting coefficients
  • Q2 is the storage capacity loss
  • T2 is the storage temperature
  • t2 is the storage time
  • c2 d2, e2, f2 are fitting coefficients.
  • SD1 is the standard deviation of the circulation health status
  • Q1 is the circulation capacity loss
  • A1, B1, C1, and D1 are fitting coefficients
  • SD2 is the standard deviation of storage health status
  • Q2 is the storage capacity loss
  • A2, B2, C2, and D2 are fitting coefficients.
  • obtaining the average temperature of the target battery within a target period includes:
  • An average value of the battery cell temperatures is determined as the average temperature.
  • an electronic device including a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the computer program to implement the method according to the first aspect of the present disclosure.
  • a computer-readable storage medium is further provided, on which a computer program is stored.
  • the computer program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.
  • the target battery is determined according to the cycle time, storage time and average temperature of the target battery.
  • the cycle capacity loss and storage capacity loss of the target battery are calculated, and then the cycle health state standard deviation and storage health state standard deviation of the target battery are obtained based on the cycle capacity loss and storage capacity loss.
  • the predicted health state of the target battery is predicted based on the cycle capacity loss, storage capacity loss, cycle health state standard deviation and storage health state standard deviation.
  • FIG1 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
  • FIG2 is a flow chart of a method for predicting a battery health state according to an embodiment
  • FIG3 is a flow chart of a method for predicting a battery health state according to another embodiment
  • FIG. 4 is a block diagram of an electronic device according to an embodiment.
  • FIG. 1 is a schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present disclosure.
  • 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.
  • the electronic device 1000 may include but is not limited to a processor 1100, a memory 1200, an interface device 1300, Communication device 1400, display device 1500, input device 1600, speaker 1700, microphone 1800, etc.
  • processor 1100 can be a central processing unit CPU, a graphics processing unit GPU, a microprocessor MCU, etc., for executing a computer program, which can be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc.
  • Memory 1200 for example, includes ROM (read-only memory), RAM (random access memory), non-volatile memory such as a hard disk, etc.
  • Interface device 1300 for example, includes a USB interface, a serial interface, a parallel interface, etc.
  • Communication device 1400 can use optical fiber or cable for wired communication, or wireless communication, specifically, can include WiFi communication, Bluetooth communication, 2G/3G/4G/5G communication, etc.
  • Display device 1500 for example, is a liquid crystal display screen, a touch display screen, etc.
  • Input device 1600 for example, may include a touch screen, a keyboard, somatosensory input, etc.
  • Speaker 1700 is used to output audio signals.
  • Microphone 1800 is used to collect audio signals.
  • 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.
  • a technician 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, so it 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.
  • the electronic device 1000 of the embodiment of the present disclosure may involve only some of the devices, for example, only the processor 1100 and the memory 1200 .
  • Fig. 2 is a flowchart of a method for predicting a battery health state according to an embodiment, which may be implemented by an electronic device.
  • the electronic device may be the electronic device 1000 shown in Fig. 1 .
  • the method for predicting the battery health status of this embodiment may include steps S2100 to S2400 as shown below:
  • 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.
  • the target operating condition may be disassembled to obtain the cycle time, storage time and average temperature of the target battery.
  • the target period may be pre-set according to an application scenario or specific requirements.
  • the target period may be within the past day.
  • the target battery of this embodiment may include at least one battery cell.
  • the multiple battery cells may be connected in series and/or in parallel.
  • obtaining the average temperature of the target battery within the target period may include: obtaining the cell temperature of each cell included in the target battery within the target period; determining the average cell temperature as the target The average temperature of the target battery during the target period.
  • the cell temperature of each cell within the target time period may be obtained according to thermal simulation.
  • 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.
  • This embodiment predicts the predicted health state of the target battery according to the average value of the battery cell temperature in the target battery within the target time period, which can make the prediction result more accurate.
  • Step S2200 determining the cycle capacity loss of the target battery according to the cycle time, the average temperature and the first preset relationship; determining the storage capacity loss of the target battery according to the storage time, the average temperature and the second preset relationship.
  • the cycle capacity loss is the capacity loss of the target battery caused by the charge and discharge cycle
  • the storage capacity loss is the capacity loss of the target battery caused by storage.
  • the first preset relationship may be a corresponding relationship between the cycle time, the cycle temperature, and the capacity loss of the battery caused by the charge and discharge cycle.
  • the second preset relationship may be a corresponding relationship between the storage time, the storage temperature, and the capacity loss of the battery caused by storage.
  • Q1 is the cycle capacity loss
  • T1 is the cycle temperature
  • t1 is the cycle time
  • c1 d1, e1, and f1 are fitting coefficients.
  • 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.
  • Q2 is the storage capacity loss
  • T2 is the storage temperature
  • t2 is the storage time
  • c2 d2, e2, f2 are fitting coefficients.
  • 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.
  • Step S2300 obtaining the standard deviation of the circulation health state according to the circulation capacity loss and the third preset relationship; obtaining the standard deviation of the storage health state according to the storage capacity loss and the fourth preset relationship.
  • the third preset relationship may represent the corresponding relationship between the circulatory health status and the standard deviation
  • the fourth preset relationship may represent the corresponding relationship between the storage health status and the standard deviation
  • 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.
  • the standard deviation of the cycle health state corresponding to the cycle health state may represent the standard deviation of the distribution of the cycle health state of the target battery cells caused by the manufacturing process.
  • the state standard deviation may be a distribution standard deviation of the storage health state of the target battery cells caused by the manufacturing process.
  • the state of health (SOH) of a battery can be the percentage of the battery's fully charged capacity relative to its rated capacity.
  • SD1 is the standard deviation of the circulation health status
  • Q1 is the cycle capacity loss
  • SOH1 is the circulation health status
  • A1, B1, C1, and D1 are fitting coefficients
  • SD2 is the standard deviation of storage health status
  • Q2 is the storage capacity loss
  • SOH2 is the storage health status
  • A2, B2, C2, and D2 are fitting coefficients.
  • Step S2400 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.
  • determining the predicted health state of the target battery according to the cycle capacity loss, the storage capacity loss, the cycle health state standard deviation and the storage health state standard deviation may include steps S2410 to S2430 as shown below:
  • Step S2410 obtaining the median value of the health state of the target battery cells under the target operating conditions according to the cycle capacity loss and the storage capacity loss.
  • 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.
  • the distribution standard deviation of the health status can reflect the consistency deviation of the health status of each cell in the target battery caused by the manufacturing process.
  • the standard deviation of the circulation health state and the standard deviation of the storage health state may be weightedly summed to obtain the distribution standard deviation of the health state.
  • SD1 is the standard deviation of the circulation health state
  • SD2 is the standard deviation of the storage health state
  • ⁇ 1 and ⁇ 2 are preset weights.
  • the standard deviation SD3 of the distribution of health states can be expressed as:
  • Q1 is the cycle capacity loss
  • Q2 is the storage capacity loss
  • SD1 is the standard deviation of the cycle health state
  • SD2 is the standard deviation of the storage health state.
  • Step S2430 obtaining the predicted health state of the target battery according to the median of the health state and the standard deviation of the distribution of the health state.
  • the predicted health state of the target battery may be obtained according to the median of the health state, the standard deviation of the distribution of the health state, and a fifth preset relationship.
  • 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
  • n is the number of standard deviations corresponding to the preset confidence interval.
  • 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%.
  • n is the number of standard deviations corresponding to the confidence interval. For example, when the confidence interval is 68%, the corresponding number of standard deviations n may be 1. For another example, when the confidence interval is 95%, the corresponding number of standard deviations n may be 2. For another example, when the confidence interval is 99%, the corresponding number of standard deviations n may be 3.
  • the cycle capacity loss and storage capacity loss of the target battery are determined according to the cycle time, storage time and average temperature of the target battery, and then the cycle health state standard deviation and storage health state standard deviation of the target battery are obtained according to the cycle capacity loss and storage capacity loss, and then the predicted health state of the target battery is predicted according to the cycle capacity loss, storage capacity loss, cycle health state standard deviation and storage health state standard deviation.
  • this embodiment fully considers the consistency deviation of the cycle and storage caused by the battery cell manufacturing process, and can more accurately predict the predicted health state of the target battery with low calculation cost.
  • the method before executing step S2200, the method further includes steps S3100 to S3300 as shown in FIG3 :
  • Step S3100 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.
  • the first health status data is data indicating the health status of the first battery cell after a charge-discharge cycle test for a preset cycle time
  • the second health status data is data indicating the health status of the second battery cell after a storage test for a preset storage time.
  • the first battery cell and the second battery cell may be battery cells of the same model, and the production batches of any two of the first battery cell and the second battery cell may be the same or different.
  • the first battery cell and the second number of second battery cells may cover all batches of battery cells of this model.
  • At least one cycle temperature and at least one storage temperature may be set in advance according to the application scenario or specific needs.
  • the cycle temperature may include 25°C, 35°C and 45°C
  • the storage temperature may include 25°C, 45°C and 60°C.
  • the first number of first cells may be divided into three parts in advance, and the first part of the first 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 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 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, 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.
  • SOC state of charge
  • SOC state of charge
  • the first health status data of each first battery cell can be obtained.
  • the second health status data of each second battery cell can be obtained.
  • the preset cycle time and preset storage time in this embodiment can be multiple times set in advance according to application scenarios or specific needs.
  • the preset cycle time can include 1 day, 2 days, ..., 200 days
  • the preset storage time can include 1 day, 2 days, ..., 300 days.
  • Step S3200 determining first median data of a first number of first battery cells based on the first health status data, and determining 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.
  • the average value of the 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 the first median data; for each preset storage time, the average value of the health status of a second number of second battery cells after the storage test for the storage time is determined to obtain the second median data.
  • Step S3300 obtaining a first preset relationship according to the cycle temperature and the first median data; obtaining a second preset relationship according to the storage temperature and the second median data.
  • g1 is the first fitting coefficient.
  • the first fitting coefficient is different, and at different storage temperatures, the second fitting coefficient is different.
  • a third expression representing the correspondence between the first fitting coefficient g1 and the cycle temperature T1 can be established based on the first health status data of each first battery cell and the corresponding cycle temperature
  • 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.
  • the median value of the cycle capacity loss of the target battery due to charge and discharge cycles and the median value of the storage capacity loss of the target battery due to storage can be determined, and then the median value of the predicted health status of the target battery can be determined based on the median value of the cycle capacity loss and the median value of the storage capacity loss.
  • the method before executing step S2200, the method further includes steps S3400 to S3700 as shown in FIG3 :
  • Step S3400 based on the first health status data, obtain a first expression representing the corresponding relationship between the health status of the first battery cell and the cycle time; based on the second health status data, obtain a second expression representing the corresponding relationship between the health status of the second battery cell and the storage time.
  • the first expression of each first battery cell may be obtained according to the first health status data of the first battery cell.
  • SOH1 is the health status of the first battery cell
  • t1 is the cycle time
  • a1 and b1 are fitting coefficients.
  • the second expression of each second battery cell may be obtained according to the second health status data of the second battery cell.
  • SOH2 is the health status of the first battery cell
  • t2 is the storage time
  • a2 and b2 are fitting coefficients.
  • 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.
  • the preset health status in this embodiment may be pre-set according to the application scenario or specific requirements.
  • the preset health status may include 95%, 90%, 85%, 80%, 75%, 70%, 65%, and 60%.
  • 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.
  • Step S3600 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 corresponding to the battery cell according to the reference storage time and the second expression.
  • each reference cycle time may be substituted into the first expression of each first battery cell to obtain the first reference health state of each first battery cell after the charge-discharge cycle test of 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 health state of each second battery cell after the storage test of each reference storage time.
  • Step S3700 obtaining a third preset relationship based on the first reference health state, and obtaining a fourth preset relationship based on the second reference health state.
  • 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
  • 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.
  • obtaining the third preset relationship according to the first reference health state and obtaining the fourth preset relationship according to the second reference health state may include steps S3710 to S3720 as shown in FIG. 3 :
  • 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.
  • 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.
  • Each group of first reference health states can be determined by the reference cycle time corresponding to the same preset health state, and the corresponding cycle temperature of the first battery cells is the same.
  • the standard deviation of each group of first reference health states may be determined as the first standard deviation.
  • 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.
  • Each group of second reference health states can be determined by the reference cycle time corresponding to the same preset health state, and the corresponding storage temperature of the second battery cells is the same.
  • the standard deviation of each group of second reference health states may be determined as the second standard deviation.
  • Step S3720 obtaining a third preset relationship based on the first standard deviation, and obtaining a fourth preset relationship based on the second standard deviation.
  • the third preset relationship representing the corresponding relationship between the circulatory health state and the standard deviation may be obtained based on the standard deviation of each group of first reference health states and the corresponding preset health state.
  • the fourth preset relationship representing the corresponding relationship between the storage health state and the standard deviation may be obtained based on the standard deviation of each group of second reference health states and the corresponding preset health state.
  • the consistency deviation of the target battery in reaching the circulation health status caused by the manufacturing process and the consistency deviation of the target battery in reaching the storage health status caused by the manufacturing process can be determined, and then according to the consistency deviation of the target battery in reaching the circulation health status caused by the manufacturing process and the consistency deviation of the target battery in reaching the storage health status caused by the manufacturing process, the consistency deviation of the health status of the target battery caused by the manufacturing process is determined.
  • the third preset relationship is obtained based on the first standard deviation
  • 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.
  • FIG. 4 is a schematic diagram of a hardware structure of an electronic device according to another embodiment.
  • 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 such as any of the above method embodiments under the control of the computer program.
  • the electronic device 4000 may be an electronic product such as a smart phone, a portable computer, a desktop computer, a tablet computer, a server, a computer cluster, etc.
  • 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.
  • This embodiment provides a computer-readable storage medium, in which executable commands are stored.
  • executable commands are stored.
  • the executable commands are executed by a processor, the method described in any method embodiment of this specification is executed.
  • 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.
  • a computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device.
  • a computer-readable storage medium may 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 of the foregoing.
  • 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 disk read-only memory (CD-ROM), a digital multifunction device, a portable hard disk, a portable hard disk, a portable compact disk read-only memory (CD-ROM), ...
  • the computer readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (e.g., an optical pulse by an optical fiber cable), or an electrical signal transmitted by a wire.
  • 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, optical fiber transmissions, wireless transmissions, 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 for storage in the computer-readable storage medium in each computing/processing device.
  • the computer program instructions for performing the operation of the present invention may be assembler 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++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages.
  • Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server.
  • the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.
  • 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 that implements the functions/actions specified in one or more boxes in the flowchart and/or block diagram is generated.
  • These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and/or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions/actions specified in one or more boxes in the flowchart and/or block diagram.
  • the computer readable program instructions may also be loaded into a computer, other programmable data processing apparatus, or other device to cause a series of operation steps to be performed on the computer, other programmable data processing apparatus, or other device.
  • each box in the flowchart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the specified logical function.
  • the functions marked in the box can also occur in a different order from the order 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.
  • each box in the block diagram and/or flowchart, and the combination of the 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 it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

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Abstract

一种电池健康状态的预测方法、电子设备及可读存储介质，涉及电池技术领域，该方法包括：根据预设的目标工况，获取目标电池的循环时间、存储时间及在目标时段内的平均温度（S2100）；根据循环时间、平均温度和第一预设关系，确定目标电池的循环容量损失；根据存储时间、平均温度和第二预设关系，确定目标电池的存储容量损失（S2200）；根据循环容量损失和第三预设关系，得到循环健康状态标准差；根据存储容量损失和第四预设关系，得到存储健康状态标准差（S2300）；根据循环容量损失、存储容量损失、循环健康状态标准差和存储健康状态标准差，确定目标电池在目标工况下的预测健康状态（S2400）。

Description

电池健康状态的预测方法、电子设备及可读存储介质
相关申请的交叉引用
本公开要求于2022年10月31日提交的申请号为202211348958.0、名称为“电池健康状态的预测方法、电子设备及可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开实施例涉及电池技术领域，更具体地，涉及一种电池健康状态的预测方法、一种电子设备及一种计算机可读存储介质。
背景技术
目前，一般通过以下方式来评估电池健康状态：第一种，利用电池仿真模型模拟实际电池内部各个位置电池的温度变化，通过筛选、运算得到电池容量，获得实际电池不同位置的寿命；第二种，参照现有的电池使用数据和寿命数据建立衰减基础模型，根据目标电池的使用数据，通过衰减基础模型得到目标电池的寿命数据；第三种，对电池的日历寿命容量衰减、交流内阻和直流内阻变化与温度和电压的相关性分析，通过拟合公式，从电芯加速老化试验中推断电芯真实寿命状态。
然而，上述第一种方式仅考虑了温度场对电芯寿命的影响，忽略了电芯自身因工厂制造工艺而造成的容量衰减不一致。第二种方式是根据现有电池数据建立模型，并没有从原理上进行寿命评估，评估准确性较差，且难以推广。第三种方式主要针对单电芯加速测试的寿命预测，没有考虑电池内温度场差异与电芯一致性差异的影响。
发明内容
本公开实施例的一个目的是提供一种准确评估电池健康状态的新的技术方案。
根据本公开的第一方面，提供了一种电池健康状态的预测方法，包括：
根据预设的目标工况，获取目标电池的循环时间、存储时间及在目标时段内的平均温度；
根据所述循环时间、所述平均温度和第一预设关系，确定所述目标电池的循环容量损失；根据所述存储时间、所述平均温度和第二预设关系，确定所述目标电池的存储容量损失；
根据所述循环容量损失和第三预设关系，得到循环健康状态标准差；根据所述存储容量损失和第四预设关系，得到存储健康状态标准差；
根据所述循环容量损失、所述存储容量损失、所述循环健康状态标准差、所述存 储健康状态标准差，确定所述目标电池在所述目标工况下的预测健康状态。
可选的，所述根据所述循环容量损失、所述存储容量损失、所述循环健康状态标准差、所述存储健康状态标准差，确定所述目标电池在所述目标工况下的预测健康状态，包括：
根据所述循环容量损失和所述存储容量损失，得到所述目标电池的电芯在所述目标工况下的健康状态的中值；
根据所述循环健康状态标准差和所述存储健康状态标准差，得到所述目标电池的电芯在所述目标工况下的健康状态的分布标准差；
根据所述健康状态的中值和所述健康状态的分布标准差，得到所述目标电池的预测健康状态。
可选的，所述方法还包括：
获取第一数量个第一电芯在对应的循环温度下进行充放电循环测试所得到的第一健康状态数据、以及第二数量个第二电芯在对应的存储温度下进行存储测试所得到的第二健康状态数据；其中，所述第一健康状态数据为表示所述第一电芯在进行预设循环时间的充放电循环测试后的健康状态的数据，所述第二健康状态数据为表示所述第二电芯在进行预设存储时间的存储测试后的健康状态的数据；
根据所述第一健康状态数据，确定所述第一数量个所述第一电芯的第一中值数据，根据所述第二健康状态数据，确定所述第二数量个所述第二电芯的第二中值数据，其中，所述第一中值数据为表示所述第一数量个所述第一电芯在进行预设循环时间的充放电循环测试后的健康状态的平均值的数据，所述第二中值数据为表示所述第二数量个所述第二电芯在进行预设存储时间的存储测试后的健康状态的平均值的数据；
根据所述循环温度、所述第一中值数据，得到所述第一预设关系；根据所述存储温度、所述第二中值数据，得到所述第二预设关系。
可选的，所述方法还包括：
根据所述第一健康状态数据，得到表示所述第一电芯的健康状态与循环时间之间对应关系的第一表达式；根据所述第二健康状态数据，得到表示所述第二电芯的健康状态与存储时间之间对应关系的第二表达式；
根据所述第一预设关系，确定多个预设健康状态所对应的参考循环时间，根据所述第二预设关系，确定多个所述预设健康状态所对应的参考存储时间；
根据所述参考循环时间和所述第一表达式，得到对应第一电芯的第一参考健康状态；根据所述参考存储时间和所述第二表达式，得到对应电芯的第二参考健康状态；
根据所述第一参考健康状态，得到所述第三预设关系，根据所述第二参考健康状态，得到第四预设关系。
可选的，所述根据所述第一参考健康状态，得到所述第三预设关系，根据所述第 二参考健康状态，得到第四预设关系，包括：
确定每一所述预设健康状态在每一循环温度所对应的第一参考健康状态的第一标准差；确定每一所述预设健康状态在每一存储温度所对应的第二参考健康状态的第二标准差；
根据所述第一标准差，得到所述第三预设关系，根据所述第二标准差，得到所述第四预设关系。
可选的，所述第一预设关系为：
Q1＝1-(c1*T1d1+e1)*t1f1
其中，Q1为循环容量损失，T1为循环温度，t1为循环时间，c1、d1、e1、f1为拟合系数；
所述第二预设关系为：
Q2＝1-(c2*T2d2+e2)*t2f2
其中，Q2为存储容量损失，T2为存储温度，t2为存储时间，c2、d2、e2、f2为拟合系数。
可选的，所述第三预设关系为：
SD1＝A1*exp(SOH1B1+C1)+D1
SOH1＝1-Q1
其中，SD1为循环健康状态标准差，Q1为循环容量损失，A1、B1、C1、D1为拟合系数；
所述第四预设关系为：
SD2＝A2*exp(SOH2B2+C2)+D2
SOH2＝1-Q2
其中，SD2为存储健康状态标准差，Q2为存储容量损失，A2、B2、C2、D2为拟合系数。
可选的，获取所述目标电池在目标时段内的平均温度，包括：
获取所述目标电池中所包含的每一电芯在所述目标时段内的电芯温度；
确定所述电芯温度的平均值，作为所述平均温度。
根据本公开的第二方面，还提供了一种电子设备，包括存储器和处理器，所述存储器用于存储计算机程序；所述处理器用于执行所述计算机程序，以实现根据本公开第一方面所述的方法。
根据本公开的第三方面，还提供了一种计算机可读存储介质，所述计算机可读存储介质上存储计算机程序，所述计算机程序在被处理器执行时实现根据本公开的第一方面所述的方法。
通过本实施例，根据目标电池的循环时间、存储时间和平均温度，确定目标电池 的循环容量损失和存储容量损失，再根据循环容量损失和存储容量损失，得到目标电池的循环健康状态标准差和存储健康状态标准差，再根据循环容量损失、存储容量损失、循环健康状态标准差和存储健康状态标准差，来预测目标电池的预测健康状态。本实施例从电芯的角度出发，充分考虑电芯制造工艺造成的循环和存储的一致性偏差，能够更加准确地预测目标电池的预测健康状态，且计算成本较低。
通过以下参照附图对本公开的示例性实施例的详细描述，本公开实施例的其它特征及其优点将会变得清楚。
附图说明
被结合在说明书中并构成说明书的一部分的附图示出了本公开的实施例，并且连同其说明一起用于解释本公开实施例的原理。
图1是可用于实现本公开的实施例的电子设备的硬件配置的示意性框图；
图2是根据一个实施例的电池健康状态的预测方法的流程示意图；
图3是根据另一个实施例的电池健康状态的预测方法的流程示意图；
图4是根据一个实施例的电子设备的方框原理图。
具体实施方式
现在将参照附图来详细描述本公开的各种示例性实施例。应注意到：除非另外具体说明，否则在这些实施例中阐述的部件和步骤的相对布置、数字表达式和数值不限制本发明的范围。
以下对至少一个示例性实施例的描述实际上仅仅是说明性的，决不作为对本发明及其应用或使用的任何限制。
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论，但在适当情况下，所述技术、方法和设备应当被视为说明书的一部分。
在这里示出和讨论的所有例子中，任何具体值应被解释为仅仅是示例性的，而不是作为限制。因此，示例性实施例的其它例子可以具有不同的值。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步讨论。
<硬件配置>
图1是可用于实现本公开实施例的电子设备的结构示意图。
该电子设备1000可以是智能手机、便携式电脑、台式计算机、平板电脑、服务器等，在此不做限定。
该电子设备1000可以包括但不限于处理器1100、存储器1200、接口装置1300、 通信装置1400、显示装置1500、输入装置1600、扬声器1700、麦克风1800等等。其中，处理器1100可以是中央处理器CPU、图形处理器GPU、微处理器MCU等，用于执行计算机程序，该计算机程序可以采用比如x86、Arm、RISC、MIPS、SSE等架构的指令集编写。存储器1200例如包括ROM(只读存储器)、RAM(随机存取存储器)、诸如硬盘的非易失性存储器等。接口装置1300例如包括USB接口、串行接口、并行接口等。通信装置1400例如能够利用光纤或电缆进行有线通信，或者进行无线通信，具体地可以包括WiFi通信、蓝牙通信、2G/3G/4G/5G通信等。显示装置1500例如是液晶显示屏、触摸显示屏等。输入装置1600例如可以包括触摸屏、键盘、体感输入等。扬声器1700用于输出音频信号。麦克风1800用于采集音频信号。
应用于本公开实施例中，电子设备1000的存储器1200用于存储计算机程序，该计算机程序用于控制所述处理器1100进行操作以实现根据本公开实施例的方法。技术人员可以根据本公开所公开方案设计该计算机程序。该计算机程序如何控制处理器进行操作，这是本领域公知，故在此不再详细描述。该电子设备1000可以安装有智能操作系统(例如Windows、Linux、安卓、IOS等系统)和应用软件。
本领域技术人员应当理解，尽管在图1中示出了电子设备1000的多个装置，但是，本公开实施例的电子设备1000可以仅涉及其中的部分装置，例如，只涉及处理器1100和存储器1200等。
下面，参照附图描述根据本发明的各个实施例和例子。
<方法实施例>
图2是根据一个实施例的电池健康状态的预测方法的流程示意图，该实施例可以由电子设备实施。例如，该电子设备可以是如图1所示的电子设备1000。
如图2所示，本实施例的电池健康状态的预测方法可以包括如下所示的步骤S2100～S2400：
步骤S2100，根据预设的目标工况，获取目标电池的循环时间、存储时间及在目标时段内的平均温度。
在本实施例中，可以是对目标工况进行拆解，得到目标电池的循环时间、存储时间和平均温度。
其中，目标时段可以是预先根据应用场景或具体需求所设定好的。例如，该目标时段可以是过去的一天内。
本实施例的目标电池，可以包括至少一个电芯。在目标电池中包括多个电芯的情况下，多个电芯可以是通过串联和/或并联的方式进行连接的。
在一个实施例中，获取目标电池在目标时段内的平均温度，可以包括：获取目标电池中所包含的每一电芯在目标时段内的电芯温度；确定电芯温度的平均值，作为目 标电池在目标时段内的平均温度。
本实施例中，每一电芯在目标时段内的电芯温度，可以是根据热仿真获得。
本实施例中的循环为目标电池的充放电循环。那么，循环时间为充放电循环的时间。
本实施例根据目标电池中电芯在目标时段内的电芯温度的平均值，来预测目标电池的预测健康状态，可以使得预测结果更加准确。
步骤S2200，根据循环时间、平均温度和第一预设关系，确定目标电池的循环容量损失；根据存储时间、平均温度和第二预设关系，确定目标电池的存储容量损失。
在本实施例中，循环容量损失为由于充放电循环所造成的目标电池的容量损失，存储容量损失为由于存储所造成的目标电池的容量损失。第一预设关系，可以是表示循环时间、循环温度与充放电循环所造成的电池的容量损失之间的对应关系。第二预设关系，可以是表示存储时间、存储温度与存储所造成的电池的容量损失之间的对应关系。
在一个实施例中，第一预设关系可以表示为：
Q1＝1-(c1*T1d1+e1)*t1f1
其中，Q1为循环容量损失，T1为循环温度，t1为循环时间，c1、d1、e1、f1为拟合系数。
在本实施例中，可以是将平均温度作为循环温度，将循环时间和循环温度代入第一预设关系中，得到循环容量损失。
在一个实施例中，第二预设关系可以表示为：
Q2＝1-(c2*T2d2+e2)*t2f2
其中，Q2为存储容量损失，T2为存储温度，t2为存储时间，c2、d2、e2、f2为拟合系数。
在本实施例中，可以是将平均温度作为存储温度，将存储时间和存储温度代入第二预设关系中，得到存储容量损失。
步骤S2300，根据循环容量损失和第三预设关系，得到循环健康状态标准差；根据存储容量损失和第四预设关系，得到存储健康状态标准差。
本实施例中，第三预设关系可以是表示循环健康状态和标准差之间对应关系，第四预设关系可以是表示存储健康状态和标准差之间对应关系。
其中，循环健康状态，可以是循环造成容量损失后目标电池的预测健康状态，具体为1与循环容量损失的差；存储健康状态，可以是存储造成容量损失后目标电池的预测健康状态，具体为1与存储容量损失的差。
循环健康状态所对应的循环健康状态标准差，可以是表示目标电池的电芯由于制造工艺所造成的达到循环健康状态的分布标准差。存储健康状态所对应的存储健康状 态标准差，可以是表示目标电池的电芯由于制造工艺所造成的达到存储健康状态的分布标准差。
电池的健康状态(State Of Health，SOH)，可以是电池满充容量相对额定容量的百分比。
在一个实施例中，第三预设关系可以表示为：
SD1＝A1*exp(SOH1B1+C1)+D1
SOH1＝1-Q1
其中，SD1为循环健康状态标准差，Q1为循环容量损失，SOH1为循环健康状态，A1、B1、C1、D1为拟合系数；
在一个实施例中，第四预设关系可以表示为：
SD2＝A2*exp(SOH2B2+C2)+D2
SOH2＝1-Q2
其中，SD2为存储健康状态标准差，Q2为存储容量损失，SOH2为存储健康状态，A2、B2、C2、D2为拟合系数。
步骤S2400，根据循环容量损失、存储容量损失、循环健康状态标准差和存储健康状态标准差，确定目标电池在目标工况下的预测健康状态。
在本公开的一个实施例中，根据循环容量损失、存储容量损失、循环健康状态标准差和存储健康状态标准差，确定目标电池的预测健康状态，可以包括如下所示的步骤S2410～S2430：
步骤S2410，根据循环容量损失和存储容量损失，得到目标电池的电芯在目标工况下的健康状态的中值。
在本实施例中，健康状态的中值SOH3可以通过表示为：
SOH3＝1-Q1-Q2
步骤S2420，根据循环健康状态标准差和存储健康状态标准差，得到目标电池的电芯在目标工况下的健康状态的分布标准差。
其中，健康状态的分布标准差，可以反映目标电池中各电芯由于制造工艺所造成的健康状态的一致性偏差。
本实施例中，可以是对循环健康状态标准差和存储健康状态标准差进行加权求和，得到健康状态的分布标准差。
在一个例子中，健康状态的分布标准差SD3可以表示为：
SD3＝SD1*λ1+SD2*λ2
其中，SD1为循环健康状态标准差，SD2为存储健康状态标准差，λ1和λ2为预设的权重。
在另一个例子中，健康状态的分布标准差SD3可以表示为：
其中，Q1为循环容量损失，Q2为存储容量损失，SD1为循环健康状态标准差，SD2为存储健康状态标准差。
步骤S2430，根据健康状态的中值和健康状态的分布标准差，得到目标电池的预测健康状态。
在一个实施例中，可以是根据健康状态的中值、健康状态的分布标准差好饿第五预设关系，得到目标电池的预测健康状态。
其中，第五预设关系可以表示为：
SOH＝SOH3±n*SD3
其中，SOH表示目标电池的预测健康状态，SOH3为健康状态的中值，SD3为健康状态的分布标准差，n为预设的置信区间所对应的标准差的数量。
其中，置信区间可以是预先根据应用场景或具体需求所设定的。例如，该置信区间可以是68％、95％或99％。
n为置信区间所对应的标准差的数量。例如，在置信区间为68％的情况下，对应的标准差的数量n可以是1。再例如，在置信区间为95％的情况下，对应的标准差的数量n可以是2。再例如，在置信区间为99％的情况下，对应的标准差的数量n可以是3。
通过本实施例，根据目标电池的循环时间、存储时间和平均温度，确定目标电池的循环容量损失和存储容量损失，再根据循环容量损失和存储容量损失，得到目标电池的循环健康状态标准差和存储健康状态标准差，再根据循环容量损失、存储容量损失、循环健康状态标准差和存储健康状态标准差，来预测目标电池的预测健康状态。本实施例从电芯的角度出发，充分考虑电芯制造工艺造成的循环和存储的一致性偏差，能够更加准确地预测目标电池的预测健康状态，且计算成本较低。
在本公开的一个实施例中，在执行步骤S2200之前，该方法还包括如图3所示的步骤S3100～S3300：
步骤S3100，获取第一数量个第一电芯在对应的循环温度下进行充放电循环测试所得到的第一健康状态数据、以及第二数量个第二电芯在对应的存储温度下进行存储测试所得到的第二健康状态数据。
其中，第一健康状态数据为表示第一电芯在进行预设循环时间的充放电循环测试后的健康状态的数据，第二健康状态数据为表示第二电芯在进行预设存储时间的存储测试后的健康状态的数据。
在本实施例中，第一电芯和第二电芯可以是相同型号的电芯，第一电芯和第二电芯中的任意两个电芯的生产批次可以相同，也可以不同。在一个例子中，第一数量个 第一电芯和第二数量个第二电芯可以是覆盖该型号电芯的所有批次。
进一步地，可以预先根据应用场景或具体需求设置至少一个循环温度和至少一个存储温度。例如，循环温度可以包括25℃、35℃和45℃，存储温度可以包括25℃、45℃和60℃。那么，可以是预先将第一数量个第一电芯分为三部分，第一部分第一电芯可以是设置在25℃环境中进行放电深度为100％、充放电电流为0.5倍的充放电循环；第二部分第一电芯可以是设置在35℃环境中进行放电深度为100％、充放电电流为0.5倍的充放电循环；第三部分第一电芯可以是设置在45℃环境中进行放电深度为100％、充放电电流为0.5倍的充放电循环测试。可以是预先将第二数量个第二电芯分为三部分，第一部分第二电芯可以是设置在25℃环境中进行荷电状态(State Of Charge，SOC)为100％的存储测试；第二部分第二电芯可以是设置在45℃环境中进行荷电状态为100％的存储测试；第三部分第二电芯可以是设置在60℃环境中进行荷电状态为100％的存储测试。
在充放电循环测试结束的情况下，可以得到每个第一电芯的第一健康状态数据。在存储测试结束的情况下，可以得到每个第二电芯的第二健康状态数据。
本实施例中的预设循环时间和预设存储时间可以是分别预先根据应用场景或具体需求所设定的多个时间。例如，预设循环时间可以包括1天、2天、……、200天，预设存储时间可以包括1天、2天、……、300天。
步骤S3200，根据第一健康状态数据，确定第一数量个第一电芯的第一中值数据，根据第二健康状态数据，确定第二数量个第二电芯的第二中值数据，其中，第一中值数据为表示第一数量个第一电芯在进行预设循环时间的充放电循环测试后的健康状态的平均值的数据，第二中值数据为表示第二数量个第二电芯在进行预设存储时间的存储测试后的健康状态的平均值的数据。
在本实施例中，可以是针对每个预设循环时间，确定第一数量个第一电芯在进行该预设循环时间的充放电循环测试后的健康状态的平均值，得到第一中值数据；针对每个预设存储时间，确定第二数量个第二电芯在进行该存储时间的存储测试后的健康状态的平均值，得到第二中值数据。
步骤S3300，根据循环温度、第一中值数据，得到第一预设关系；根据存储温度、第二中值数据，得到第二预设关系。
在本实施例中，可以是根据第一中值数据，建立第一预设关系的初始形式为：
Q1＝1-g1*t1f1
其中，g1为第一拟合系数。
可以是根据第二中值数据，建立第呃预设关系的初始形式为：
Q2＝1-g2*t2f2
其中，g2为第二拟合系数。
在不同的循环温度下，第一拟合系数不同，在不同的存储温度下，第二拟合系数不同。
因此，可以是根据每个第一电芯的第一健康状态数据和对应的循环温度，建立表示第一拟合系数g1和循环温度T1之间对应关系的第三表达式，根据每个第二电芯的第二健康状态数据和对应的存储温度，建立表示第二拟合系数g2和存储温度T2之间对应关系的第四表达式。
本实施例中的第三表达式可以表示为：
g1＝c1*T1d1+e1
本实施例中的第四表达式可以表示为：
g2＝c2*T2d2+e2
在预测目标电池的预测健康状态时，根据通过本实施例构建第一预设关系和第二预设关系，可以确定目标电池由于充放电循环所造成的循环容量损失的中值、及目标电池由于存储所造成的存储容量损失的中值，进而根据循环容量损失的中值和存储容量损失的中值确定目标电池的预测健康状态的中值。
在本公开的一个实施例中，在执行步骤S2200之前，该方法还包括如图3所示的步骤S3400～S3700：
步骤S3400，根据第一健康状态数据，得到表示第一电芯的健康状态与循环时间之间对应关系的第一表达式；根据第二健康状态数据，得到表示第二电芯的健康状态与存储时间之间对应关系的第二表达式。
在本实施例中，可以是根据每个第一电芯的第一健康状态数据，得到该第一电芯的第一表达式。第一表达式可以表示为：
SOH1＝a1*t1b1
其中，SOH1为第一电芯的健康状态，t1为循环时间，a1、b1为拟合系数。
在本实施例中，可以是根据每个第二电芯的第二健康状态数据，得到该第二电芯的第二表达式。第二表达式可以表示为：
SOH2＝a2*t2b2
其中，SOH2为第一电芯的健康状态，t2为存储时间，a2、b2为拟合系数。
步骤S3500，根据第一预设关系，确定多个预设健康状态所对应的参考循环时间，根据第二预设关系，确定多个预设健康状态所对应的参考存储时间。
本实施例中的预设健康状态可以是预先根据应用场景或具体需求所设定的。例如，预设健康状态可以包括95％、90％、85％、80％、75％、70％、65％、60％。
可以是将每个预设健康状态分别代入第一预设关系中，得到每个预设健康状态所对应的参考循环时间。可以是将每个预设健康状态分别代入第二预设关系中，得到每个预设健康状态所对应的参考循环时间。
步骤S3600，根据参考循环时间和第一表达式，得到对应第一电芯的第一参考健康状态；根据参考存储时间和第二表达式，得到对应电芯的第二参考健康状态。
在本实施例中，可以是将每个参考循环时间分别代入到每个第一电芯的第一表达式中，得到每个第一电芯经过每个参考循环时间的充放电循环测试后的第一参考健康状态。可以是将每个参考存储时间分别代入到每个第二电芯的第二表达式中，得到每个第二电芯经过每个参考存储时间的存储测试后的第二参考健康状态。
步骤S3700，根据第一参考健康状态，得到第三预设关系，根据第二参考健康状态，得到第四预设关系。
本实施例根据第一预设关系和第二预设关系，得到与预设健康状态对应的参考循环时间和参考存储时间，再根据参考循环时间和第一表达式，得到对应第一电芯的第一参考健康状态，根据参考存储时间和第二表达式，得到对应电芯的第二参考健康状态，以根据第一参考健康状态来拟合第三预设关系，根据第二参考健康状态来拟合第四预设关系，可以缩短第一电芯和第二电芯的测试时间，降低测试成本。
在本公开的一个实施例中，根据第一参考健康状态，得到第三预设关系，根据第二参考健康状态，得到第四预设关系，可以包括如图3所示的步骤S3710～S3720：
步骤S3710，确定每一预设健康状态在每一循环温度所对应的第一参考健康状态的第一标准差；确定每一预设健康状态在每一存储温度所对应的第二参考健康状态的第二标准差。
在本实施例中，可以是按照预设健康状态和循环温度，对第一数量个第一电芯的第一参考健康状态进行分组，在预设健康状态的数量为8个，循环温度为3个情况下，可以得到8*3＝24组第一参考健康状态，每组第一参考健康状态，可以是由相同的预设健康状态对应的参考循环时间确定的，且对应第一电芯的循环温度相同。
进一步地，可以是确定每组第一参考健康状态的标准差，作为第一标准差。
在本实施例中，可以是按照预设健康状态和循环温度，对第二数量个第二电芯的第二参考健康状态进行分组，在预设健康状态的数量为8个，存储温度为3个情况下，可以得到8*3＝24组第二参考健康状态，每组第二参考健康状态，可以是由相同的预设健康状态对应的参考循环时间确定的，且对应第二电芯的存储温度相同。
进一步地，可以是确定每组第二参考健康状态的标准差，作为第二标准差。
步骤S3720，根据第一标准差，得到第三预设关系，根据第二标准差，得到第四预设关系。
在本实施例中，可以是根据每组第一参考健康状态的标准差和对应的预设健康状态，得到表示循环健康状态和标准差之间对应关系的第三预设关系。可以是根据每组第二参考健康状态的标准差和对应的预设健康状态，得到表示存储健康状态和标准差之间对应关系的第四预设关系。
在预测目标电池的预测健康状态时，根据通过本实施例构建第三预设关系和第四预设关系，可以确定目标电池由于制造工艺所造成的达到循环健康状态的一致性偏差、及目标电池由于制造工艺所造成的达到存储健康状态的一致性偏差，进而根据目标电池由于制造工艺所造成的达到循环健康状态的一致性偏差、及目标电池由于制造工艺所造成的达到存储健康状态的一致性偏差，确定目标电池由于制造工艺所造成的健康状态的一致性偏差。
在本公开的另一个实施例中，根据第一标准差，得到第三预设关系，根据第二标准差，得到第四预设关系，还可以包括：确定每一预设健康状态所对应的第一参考健康状态的标准差，以及每一预设健康状态所对应的第二参考健康状态的标准差；根据每一预设健康状态所对应的第一参考健康状态的标准差，得到第三预设关系，根据每一预设健康状态所对应的第二参考健康状态的标准差，得到第四预设关系。
<设备实施例>
图4是根据另一个实施例的电子设备的硬件结构示意图。
如图4所示，该电子设备4000包括处理器4100和存储器4200，该存储器4200用于存储可执行的计算机程序，该处理器4100用于根据该计算机程序的控制，执行如以上任意方法实施例的方法。
该电子设备4000可以是智能手机、便携式电脑、台式计算机、平板电脑、服务器、计算机集群等电子产品。
以上电子设备4000的各模块可以由本实施例中的处理器4100执行存储器4200存储的计算机程序实现，也可以通过其他电路结构实现，在此不做限定。
<计算机可读存储介质实施例>
本实施例提供了一种计算机可读存储介质，该存储介质中存储有可执行命令，该可执行命令被处理器执行时，执行本说明书任意方法实施例中描述的方法。
本发明可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本发明的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是――但不限于――电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功 能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本发明操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本发明的各个方面。
这里参照根据本发明实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本发明的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤， 以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本发明的多个实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。对于本领域技术人员来说公知的是，通过硬件方式实现、通过软件方式实现以及通过软件和硬件结合的方式实现都是等价的。
以上已经描述了本发明的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。本发明的范围由所附权利要求来限定。

Claims (10)

  1. 一种电池健康状态的预测方法，其中，包括：
    根据预设的目标工况，获取目标电池的循环时间、存储时间及在目标时段内的平均温度；
    根据所述循环时间、所述平均温度和第一预设关系，确定所述目标电池的循环容量损失；根据所述存储时间、所述平均温度和第二预设关系，确定所述目标电池的存储容量损失；
    根据所述循环容量损失和第三预设关系，得到循环健康状态标准差；根据所述存储容量损失和第四预设关系，得到存储健康状态标准差；
    根据所述循环容量损失、所述存储容量损失、所述循环健康状态标准差、所述存储健康状态标准差，确定所述目标电池在所述目标工况下的预测健康状态。
  2. 根据权利要求1所述的方法，其中，所述根据所述循环容量损失、所述存储容量损失、所述循环健康状态标准差、所述存储健康状态标准差，确定所述目标电池在所述目标工况下的预测健康状态，包括：
    根据所述循环容量损失和所述存储容量损失，得到所述目标电池的电芯在所述目标工况下的健康状态的中值；
    根据所述循环健康状态标准差和所述存储健康状态标准差，得到所述目标电池的电芯在所述目标工况下的健康状态的分布标准差；
    根据所述健康状态的中值和所述健康状态的分布标准差，得到所述目标电池的预测健康状态。
  3. 根据权利要求1或2所述的方法，其中，所述方法还包括：
    获取第一数量个第一电芯在对应的循环温度下进行充放电循环测试所得到的第一健康状态数据、以及第二数量个第二电芯在对应的存储温度下进行存储测试所得到的第二健康状态数据；其中，所述第一健康状态数据为表示所述第一电芯在进行预设循环时间的充放电循环测试后的健康状态的数据，所述第二健康状态数据为表示所述第二电芯在进行预设存储时间的存储测试后的健康状态的数据；
    根据所述第一健康状态数据，确定所述第一数量个所述第一电芯的第一中值数据，根据所述第二健康状态数据，确定所述第二数量个所述第二电芯的第二中值数据，其中，所述第一中值数据为表示所述第一数量个所述第一电芯在进行预设循环时间的充放电循环测试后的健康状态的平均值的数据，所述第二中值数据为表示所述第二数量个所述第二电芯在进行预设存储时间的存储测试后的健康状态的平均值的数据；
    根据所述循环温度、所述第一中值数据，得到所述第一预设关系；根据所述存储温度、所述第二中值数据，得到所述第二预设关系。
  4. 根据权利要求3所述的方法，其中，所述方法还包括：
    根据所述第一健康状态数据，得到表示所述第一电芯的健康状态与循环时间之间对应关系的第一表达式；根据所述第二健康状态数据，得到表示所述第二电芯的健康状态与存储时间之间对应关系的第二表达式；
    根据所述第一预设关系，确定多个预设健康状态所对应的参考循环时间，根据所述第二预设关系，确定多个所述预设健康状态所对应的参考存储时间；
    根据所述参考循环时间和所述第一表达式，得到对应第一电芯的第一参考健康状态；根据所述参考存储时间和所述第二表达式，得到对应电芯的第二参考健康状态；
    根据所述第一参考健康状态，得到所述第三预设关系，根据所述第二参考健康状态，得到第四预设关系。
  5. 根据权利要求4所述的方法，其中，所述根据所述第一参考健康状态，得到所述第三预设关系，根据所述第二参考健康状态，得到第四预设关系，包括：
    确定每一所述预设健康状态在每一循环温度所对应的第一参考健康状态的第一标准差；确定每一所述预设健康状态在每一存储温度所对应的第二参考健康状态的第二标准差；
    根据所述第一标准差，得到所述第三预设关系，根据所述第二标准差，得到所述第四预设关系。
  6. 根据权利要求1-4中任一项所述的方法，所述第一预设关系为：
    Q1＝1-(c1*T1d1+e1)*t1f1
    其中，Q1为循环容量损失，T1为循环温度，t1为循环时间，c1、d1、e1、f1为拟合系数；
    所述第二预设关系为：
    Q2＝1-(c2*T2d2+e2)*t2f2
    其中，Q2为存储容量损失，T2为存储温度，t2为存储时间，c2、d2、e2、f2为拟合系数。
  7. 根据权利要求1-6中任一项所述的方法，其中，所述第三预设关系为：
    SD1＝A1*exp(SOH1N1+C1)+D1
    SOH1＝1-Q1
    其中，SD1为循环健康状态标准差，Q1为循环容量损失，A1、B1、C1、D1为拟合系数；
    所述第四预设关系为：
    SD2＝A2*exp(SOH2B2+C2)+D2
    SOH2＝1-Q2
    其中，SD2为存储健康状态标准差，Q2为存储容量损失，A2、B2、C2、D2为拟 合系数。
  8. 根据权利要求1-7中任一项所述的方法，其中，获取所述目标电池在目标时段内的平均温度，包括：
    获取所述目标电池中所包含的每一电芯在所述目标时段内的电芯温度；
    确定所述电芯温度的平均值，作为所述平均温度。
  9. 一种电子设备，其特征在于，包括存储器和处理器，所述存储器用于存储计算机程序，所述处理器用于在所述计算机程序的控制下，执行如权利要求1至8中任一项所述的方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序在被处理器执行时实现如权利要求1至8中任一项所述的方法。
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