WO2026016366A1 - 电池的循环寿命预测方法、电子设备及存储介质 - Google Patents

电池的循环寿命预测方法、电子设备及存储介质

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Publication number
WO2026016366A1
WO2026016366A1 PCT/CN2024/134721 CN2024134721W WO2026016366A1 WO 2026016366 A1 WO2026016366 A1 WO 2026016366A1 CN 2024134721 W CN2024134721 W CN 2024134721W WO 2026016366 A1 WO2026016366 A1 WO 2026016366A1
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WO
WIPO (PCT)
Prior art keywords
battery
rate model
capacity
decay rate
measured data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/134721
Other languages
English (en)
French (fr)
Inventor
刘宁
冯苗苗
谭勇
韩威振
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Eve Power Co Ltd
Original Assignee
Eve Power Co Ltd
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Filing date
Publication date
Application filed by Eve Power Co Ltd filed Critical Eve Power Co Ltd
Publication of WO2026016366A1 publication Critical patent/WO2026016366A1/zh
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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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]
    • 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]
    • G01R31/367—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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • This application relates to the field of battery technology, specifically to methods for predicting the cycle life of batteries, electronic devices, and storage media.
  • Low-current operating conditions refer to battery operation where the charging and discharging current is less than a preset threshold current. For example, if the preset threshold current is 0.5C, then the charging and discharging current of a battery under low-current operating conditions is less than 0.5C.
  • Low-current operating conditions exist in battery applications, and battery cycle life is a crucial performance evaluation indicator that directly affects battery life and quality. Therefore, accurately assessing the cycle life of a battery under low-current operating conditions is essential for evaluating its health status.
  • the embodiments of this application provide a method, electronic device, and storage medium for predicting the cycle life of a battery, which can quickly predict the cycle life of a battery, reduce testing costs, and also ensure the accuracy of the prediction.
  • embodiments of this application provide a method for predicting the cycle life of a battery, the method comprising:
  • the battery to be predicted is subjected to charge-discharge cycle test to obtain the first measured data, and the battery is subjected to storage cycle test to obtain the second measured data.
  • the test current of the charge-discharge cycle test is greater than or equal to the current of the battery operating condition.
  • the cycle life of the battery is determined based on the capacity decay rate model.
  • embodiments of this application provide a battery cycle life prediction device, the battery cycle life prediction device comprising:
  • the test module is used to perform charge-discharge cycle tests on the battery to be predicted, obtain the first measured data, and perform storage cycle tests on the battery to obtain the second measured data.
  • the test current of the charge-discharge cycle test is greater than or equal to the current of the battery operating condition.
  • the first determining module is used to determine the battery capacity decay rate model based on the first measured data and the second measured data.
  • the second determination module is used to determine the cycle life of the battery based on the capacity decay rate model.
  • embodiments of this application provide an electronic device, the electronic device comprising:
  • One or more processors are One or more processors;
  • One or more applications wherein the applications are stored in memory and configured to be executed by a processor to implement the steps in the battery cycle life prediction method of any of the first aspects.
  • this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the battery cycle life prediction method of any one of the first aspects.
  • this application also provides a computer program product, including a computer program/instructions, which, when executed by a processor, are used to perform the steps in the battery cycle life prediction method of any of the first aspects described above.
  • a first measured data is obtained by performing charge-discharge cycle tests on the battery to be predicted
  • a second measured data is obtained by performing storage cycle tests on the battery.
  • FIG. 1 is a schematic flowchart of an embodiment of the battery cycle life prediction method provided in this application.
  • Figure 2 is a schematic diagram of the first capacity retention model provided in the embodiments of this application.
  • Figure 3 is a schematic diagram of the second capacity retention model provided in the embodiments of this application.
  • Figure 4 is a schematic diagram of the comprehensive capacity decay rate model, the first capacity decay rate model, and the second capacity decay rate model of the battery provided in the embodiments of this application at 25°C and a test current of 0.5C.
  • Figure 5 is a schematic diagram of the comprehensive capacity decay rate model, the first capacity decay rate model and the second capacity decay rate model of the battery provided in the embodiments of this application at 25°C and a test current of 1C.
  • Figure 6 is a schematic diagram of the cycle life of the battery provided in the embodiment of this application when the working charge and discharge current is 0.1C;
  • FIG. 7 is a schematic diagram of an embodiment of the battery cycle life prediction device provided in this application.
  • Figure 8 is a schematic diagram of an embodiment of the electronic device provided in this application.
  • FIG. 1 shows a flowchart of an embodiment of the battery cycle life prediction method in this application.
  • the execution subject in this embodiment is an electrical device or a control module within the electrical device.
  • This control module can be a Battery Management System (BMS), a Vehicle Control Unit (VCU), etc.
  • BMS Battery Management System
  • VCU Vehicle Control Unit
  • This embodiment uses a BMS as an example for detailed explanation.
  • the battery cycle life prediction method includes:
  • test 101 Perform charge-discharge cycle tests on the battery to be predicted to obtain the first measured data, and perform storage cycle tests on the battery to obtain the second measured data.
  • the test current of the charge-discharge cycle test is greater than or equal to the current of the battery under operating conditions.
  • the battery to be predicted can be a lithium-ion battery operating under low current conditions, such as a lithium iron phosphate battery.
  • the test current refers to the set charge and discharge current during the cyclic test of the battery. This charge and discharge current is greater than the current under the battery's operating conditions. For example, if the current under the battery's operating conditions is 0.4C, then the test current can be set to 0.5C, 0.8C, or 1C, etc.
  • Charge-discharge cycle testing is a test method that integrates the cyclic degradation of a battery during charge-discharge cycles and storage time cycles; it is the conventional cyclic degradation test.
  • the first measured data is experimental data obtained after performing a high-current charge-discharge cycle test on the battery. This high current is greater than the current under the battery's operating conditions. Therefore, the first measured data can reflect the information on the cyclic degradation of the battery during both charge-discharge cycles and storage time cycles.
  • Storage cycle testing is a test method used to test battery storage degradation under different storage times.
  • the second measured data is experimental data obtained after performing storage cycle testing on the battery; therefore, the second measured data provides information on the cyclic degradation of the battery during the storage cycle process.
  • a charge-discharge cycle test is performed on the battery to be predicted to obtain first measured data
  • a storage cycle test is performed on the battery to obtain second measured data.
  • the test current of the charge-discharge cycle test being greater than the current of the battery's operating condition
  • the combined method for the capacity retention rate (capacity decay rate) of the charge/discharge cycle process and the storage time cycle process can be either the sum of the capacity retention rates (capacity decay rates) of the two processes or a weighted sum of the capacity retention rates (capacity decay rates) of the two processes; no limitation is made here.
  • the sum of the capacity retention rates (capacity decay rates) of the two processes is selected to improve the calculation efficiency of subsequent data fitting.
  • a charge-discharge cycle test is performed on the battery to be predicted to obtain the first measured data, including: setting the test temperature of the battery and at least two test currents, wherein the test current is greater than or equal to the current of the battery operating condition; and performing a charge-discharge cycle test on the battery at the test temperature and according to the test current to obtain the first measured data.
  • the test temperature is the normal operating temperature of the battery, such as 25°C.
  • the test current is the set test charge/discharge current. At least two test currents are required to ensure that the relationship curve between charge/discharge current and cycle life can be fitted based on the test data corresponding to different test currents.
  • the lower limit of the test current can be the current under the battery's operating conditions, and the upper limit current can be consistent with the battery's charge/discharge MAP table, ensuring that abnormal degradation caused by lithium plating is avoided during charge/discharge cycle testing.
  • the battery can be placed in a constant temperature chamber at the test temperature, and the battery can be repeatedly charged and discharged using different test currents.
  • the number of times the battery cycles through charging and discharging is called the cycle number.
  • step 101 the battery is subjected to a storage cycle test to obtain the second measured data, including: setting the test temperature and test state of charge of the battery; and performing a storage test on the battery according to the test state of charge at the test temperature to obtain the second measured data.
  • the State of Charge (SOC) test is a set state of charge in the storage cycle test. Since the average SOC during the cycle is 50%, the test SOC in this embodiment can be selected as 50%.
  • the test temperature in this embodiment is the same as the test temperature in the charge-discharge cycle test.
  • storage cycle tests are conducted at different storage times under test temperature and test SOC. After storage cycles, the capacity retention rate of the battery will decrease. The capacity retention rate at different storage times under test temperature and test SOC is recorded to obtain the second measured data, so as to build a capacity retention rate model characterizing the storage life of the battery based on the second measured data.
  • the capacity decay rate model is a model of the capacity decay rate associated with the battery during charge-discharge cycle testing and/or storage testing, used to predict the battery's cycle life.
  • a capacity retention rate model can be obtained by curve fitting based on the first and second measured data. Then, based on the relationship between capacity retention rate and capacity decay rate, a capacity decay rate model can be determined. Understandably, since the first measured data reflects the overall cyclic decay information of the battery during charge-discharge cycles and storage time cycles, and the second measured data reflects the cyclic decay information during storage cycles, the capacity decay rate model determined based on the first and second measured data comprehensively considers the cyclic decay of the battery during charge-discharge cycles and storage time cycles. Compared to a single charge-discharge test or storage cycle test, this model fully considers the impact of cycle life decay rate and storage life decay rate on battery cycle life prediction, which helps improve the accuracy of battery cycle life prediction.
  • the capacity decay rate model of the battery is determined based on the first measured data and the second measured data, including: obtaining the capacity retention rate model of the battery based on the first measured data and the second measured data; and determining the capacity decay rate model of the battery based on the capacity retention rate model.
  • the capacity retention rate model is a model of the capacity retention rate related to the battery during charge-discharge cycle testing and/or storage testing, which is used to predict the battery's capacity retention rate.
  • curve fitting can be performed on the first and second measured data respectively to obtain the capacity retention rate model corresponding to the first and second measured data.
  • the capacity decay rate model can be calculated through the 1-capacity retention rate model.
  • the capacity retention rate model includes a first capacity retention rate model and a second capacity retention rate model.
  • the first capacity retention rate model is a capacity retention rate model that comprehensively characterizes the battery's storage life and cycle life
  • the second capacity retention rate model is a capacity retention rate model that characterizes the battery's cycle life.
  • the battery's capacity retention rate model is obtained by: performing curve fitting based on the first measured data to obtain the first capacity retention rate model; and performing curve fitting based on the second measured data to obtain the second capacity retention rate model.
  • the first capacity retention rate model is a comprehensive capacity retention rate model that characterizes both the storage life and cycle life of a battery. It is used to predict the capacity retention rate of a battery during charge-discharge cycle testing, i.e., the cycle life in the conventional sense, combining the cycle life of charge-discharge cycles and the storage life of storage time.
  • This first capacity retention rate model can be a functional expression representing the relationship between capacity retention rate and the number of cycles, test temperature, and test current.
  • the second capacity retention model is a capacity retention model characterizing the battery's storage life, used to predict the battery's capacity retention rate during storage cycle testing.
  • This second capacity retention model can be a functional expression representing the relationship between capacity retention rate and storage time, test temperature, and test state of charge (SOC).
  • Figure 2 shows a schematic diagram of the first capacity retention rate model, where the curves are obtained at 25°C and test currents of 0.5C and 1C, respectively.
  • Figure 3 shows a schematic diagram of the second capacity retention rate model, where the curves are obtained at 25°C and a state of charge of 50%.
  • curve fitting is performed to obtain the first capacity retention rate model of the battery, including: solving the preset first Gaussian degradation equation using the first measured data to obtain the first capacity retention rate model.
  • Q1 is the first capacity retention rate at the test temperature
  • C is the test charge and discharge current in the first measured data
  • n is the number of cycles in the first measured data
  • f(C) and h(C) are functions related to C, such as linear functions or multiple functions related to C.
  • the parameters in f(C) and h(C) are obtained, and thus the first capacity retention rate model can be obtained.
  • curve fitting is performed to obtain the second capacity retention model of the battery, including: solving the preset second Gaussian degradation equation using the second measured data to obtain the second capacity retention model of the battery.
  • Q2 is the second capacity retention rate under test temperature and test SOC
  • t is the storage time in the second measured data
  • a and b are the parameters to be solved.
  • the capacity decay rate model includes a first capacity decay rate model and a second capacity decay rate model.
  • the first capacity decay rate model is a capacity decay rate model characterizing the cycle life of the battery
  • the first capacity decay rate model is a capacity decay rate model characterizing the cycle life of the battery. It is used to predict the capacity decay rate of the battery during charge-discharge cycles. It is a non-conventional cycle decay model, that is, it removes (does not include) the capacity decay rate during storage cycles, which is the capacity decay rate of a complete charge-discharge cycle.
  • This first capacity decay rate model can be a functional expression characterizing the capacity decay rate with the number of cycles, test temperature, and test current.
  • the second capacity decay rate model is a model characterizing the capacity decay rate of a battery during its storage life, used to predict the capacity decay rate of a battery during storage cycles.
  • This second capacity decay rate model can be a functional expression representing the relationship between the capacity decay rate and storage time, test temperature, and test state of charge (SOC).
  • the second capacity decay rate model can be calculated using the 1-second capacity retention rate model, and the comprehensive capacity decay rate model can be calculated using the 1-first capacity retention rate model. Then, based on the second capacity decay rate model, the comprehensive capacity decay rate model is decomposed, such as subtracting the second capacity decay rate model from the comprehensive capacity decay rate model, to obtain the first capacity decay rate model.
  • Figure 4 shows a schematic diagram of the comprehensive capacity decay rate model, the first capacity decay rate model, and the second capacity decay rate model of the battery at 25°C and a test current of 0.5C. The three curves from top to bottom represent the curves of the comprehensive capacity decay rate model, the first capacity decay rate model, and the second capacity decay rate model, respectively.
  • Figure 5 shows a schematic diagram of the comprehensive capacity decay rate model, the first capacity decay rate model, and the second capacity decay rate model of the battery at 25°C and a test current of 1C.
  • the three curves from top to bottom represent the curves of the comprehensive capacity decay rate model, the first capacity decay rate model, and the second capacity decay rate model, respectively.
  • the capacity decay rate of the battery's cycle life can be predicted, that is, the capacity decay of a full charge-discharge cycle (excluding storage cycles) can be predicted, so that the battery's cycle life can be predicted subsequently based on the first capacity decay rate model.
  • the cycle life of a battery can be characterized by the battery's capacity retention rate, which is the result calculated by subtracting the capacity decay rate from 1, such as the state of health (SOH).
  • SOH state of health
  • the battery's health status can be determined based on the capacity decay rate model, thus determining the battery's cycle life.
  • the capacity decay rate model comprehensively considers the battery's cyclic degradation during charge-discharge cycles and storage cycles, compared to a single charge-discharge test or storage cycle test, it fully considers the impact of both cycle life decay rate and storage life decay rate on the battery's cycle life prediction, resulting in higher accuracy.
  • predicting the battery's cycle life based on the capacity decay rate model improves the accuracy of battery cycle life prediction compared to predicting it based on a single capacity decay rate model.
  • the first expression corresponding to the first capacity decay rate model is summed with the expression corresponding to the second capacity decay rate model to obtain the third expression corresponding to the cumulative decay rate model of the battery; the cycle life of the battery is determined based on the battery's working charge and discharge current and the third expression corresponding to the cumulative decay rate model.
  • both the first and second capacity decay rate models have high accuracy, and predicting the battery's cycle life using both models improves accuracy compared to using a single capacity decay rate model.
  • the expression for the first capacity decay rate model is as follows:
  • C is the operating charge/discharge current. and These are C-related functions.
  • C is the operating charge/discharge current. and These are C-related functions.
  • C can be a linear function
  • the first capacity decay rate model :
  • the aforementioned method for predicting the cycle life of a battery involves conducting charge-discharge cycle tests on the battery to be predicted to obtain first measured data, and then conducting storage cycle tests to obtain second measured data.
  • the method avoids the long test cycles and poor operability caused by lower current charge-discharge cycle tests.
  • this method better reflects the actual operating conditions of the battery.
  • a capacity decay rate model is determined, enabling the prediction of both cycle life decay rate and storage life decay rate.
  • this application embodiment also provides a battery cycle life prediction device 200, which includes:
  • the test module is used to perform charge-discharge cycle tests on the battery to be predicted, obtain the first measured data, and perform storage cycle tests on the battery to obtain the second measured data.
  • the test current of the charge-discharge cycle test is greater than or equal to the current of the battery operating condition.
  • the first determining module is used to determine the battery capacity decay rate model based on the first measured data and the second measured data.
  • the first determining module 202 is specifically used for:
  • the capacity decay rate model of the battery is determined based on the capacity retention rate model.
  • the capacity retention model includes a first capacity retention model and a second capacity retention model, wherein the first capacity retention model is a capacity retention model that comprehensively characterizes the battery's storage life and cycle life, and the second capacity retention model is a capacity retention model that characterizes the battery's cycle life; the first determining module 202 is further specifically used for:
  • curve fitting was performed to obtain the first capacity retention model of the battery.
  • the capacity decay rate model includes a first capacity decay rate model and a second capacity decay rate model, wherein the first capacity decay rate model is a capacity decay rate model characterizing the cycle life of the battery, and the second capacity decay rate model is a capacity decay rate model characterizing the storage life of the battery; the second determining module 203 is specifically used for:
  • the second capacity decay rate model is determined based on the second capacity retention rate model.
  • the capacity decay rate of the battery during the charge-discharge cycle test is decomposed to obtain the first capacity decay rate model.
  • the second determining module 203 is further configured to:
  • the second capacity decay rate model is determined based on the second capacity retention rate model.
  • the comprehensive capacity decay rate model is determined based on the first capacity retention rate model.
  • the comprehensive capacity decay rate model is a capacity decay rate model that comprehensively characterizes the storage life and cycle life of the battery.
  • the comprehensive capacity decay rate model is decomposed to obtain the first capacity decay rate model.
  • test module 201 is specifically used for:
  • test current is greater than or equal to the battery's operating current.
  • the battery was subjected to charge-discharge cycle tests according to the test charge-discharge current to obtain the first measured data.
  • test module 201 is further configured to:
  • the battery was stored according to the test state of charge to obtain the second measured data.
  • the first determining module 202 is further configured to:
  • the first Gaussian degradation equation is solved using the first measured data to obtain the first capacity retention model.
  • the first determining module 202 is further configured to:
  • the second measured data is used to solve the preset second Gaussian degradation equation to obtain the second capacity retention model of the battery.
  • the second determining module 203 is specifically used for:
  • the cycle life of the battery is determined by the cumulative sum of the battery's operating charge/discharge current and decay rate, and the corresponding third expression in the model.
  • This application also provides an electronic device that integrates any of the battery cycle life prediction devices provided in this application.
  • the electronic device includes:
  • One or more processors are One or more processors;
  • One or more applications wherein the applications are stored in memory and configured to be executed by a processor, wherein the battery cycle life prediction method in any of the embodiments described above is a method for predicting the cycle life of a battery.
  • FIG. 8 shows a schematic diagram of the electronic device involved in this application. Specifically:
  • the electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304.
  • a processor 301 with one or more processing cores
  • a memory 302 with one or more computer-readable storage media
  • a power supply 303 with one or more computer-readable storage media
  • an input unit 304 may be included in the electronic device.
  • the processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and/or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device.
  • the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.
  • the memory 302 can be used to store software programs and modules.
  • the processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302.
  • the memory 302 may mainly include a program storage area and a data storage area.
  • the program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc.
  • the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
  • the electronic device also includes a power supply 303 that supplies power to various components.
  • the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.
  • the power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.
  • the electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
  • an input unit 304 which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
  • the electronic device may also include a display unit, etc., which will not be described in detail here.
  • the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows:
  • the battery to be predicted is subjected to charge-discharge cycle test to obtain the first measured data, and the battery is subjected to storage cycle test to obtain the second measured data.
  • the test current of the charge-discharge cycle test is greater than or equal to the current of the battery operating condition.
  • the cycle life of the battery is determined based on the capacity decay rate model.
  • embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc.
  • ROM read-only memory
  • RAM random access memory
  • a computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any of the battery cycle life prediction methods provided in embodiments of this application.
  • the computer program loaded by the processor can execute the following steps:
  • the battery to be predicted is subjected to charge-discharge cycle test to obtain the first measured data, and the battery is subjected to storage cycle test to obtain the second measured data.
  • the test current of the charge-discharge cycle test is greater than or equal to the current of the battery operating condition.
  • the cycle life of the battery is determined based on the capacity decay rate model.
  • This application also provides a computer program product, including a computer program/instructions, which, when executed by a processor, are used to perform steps in any of the battery cycle life prediction methods provided in the application embodiments.
  • each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities.

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Abstract

本申请提供一种电池的循环寿命预测方法、电子设备及存储介质，通过对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，基于第一实测数据和第二实测数据，确定电池的容量衰减率模型，根据容量衰减率模型确定电池的循环寿命，提高了对电池的循环寿命预测的准确性。

Description

电池的循环寿命预测方法、电子设备及存储介质
本申请要求在2024年7月15日提交中国专利局、申请号为202410948851.2的中国专利申请的优先权，以上申请的全部内容通过引用结合在本申请中。
技术领域
本申请涉及电池技术领域，具体涉及电池的循环寿命预测方法、电子设备及存储介质。
 背景技术
小电流工况是指电池运行工况的充放电电流小于预设阈值电流，例如，预设阈值电流为0.5C，则小电流工况电池的充放电电流小于0.5C。电池的应用场景中，存在电池的小电流运行工况，并且电池的循环寿命是一个重要的性能评价指标，其直接影响电池的使用时间和品质，因此，正确评估小电流工况电池的循环寿命，对于电池健康状态的评估至关重要。
对于小电流工况电池的循环寿命的评估，相关技术中，主要的方法有两类：电化学模型法及数据驱动法。
 发明概述
然而，电化学模型法评估循环寿命的准确度依赖于其模型复杂度，电化学参数较多，且测试复杂，成本昂贵。数据驱动的方法需要结合实测数据，但小电流工况电池测试周期较长，充放电一圈的时间超过20h，整体测试周期超过1年，可操作性低。
本申请的实施例提供了一种电池的循环寿命预测方法、电子设备及存储介质，可以快速预测电池的循环寿命，降低测试成本，同时还能够兼顾预测的准确性。
第一方面，本申请的实施例提供了一种电池的循环寿命预测方法，方法包括：
对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，充放电循环测试的测试电流大于或者等于电池运行工况的电流；
基于第一实测数据和第二实测数据，确定电池的容量衰减率模型；
基于容量衰减率模型，确定电池的循环寿命。
第二方面，本申请的实施例提供了一种电池的循环寿命预测装置，电池的循环寿命预测装置包括：
测试模块，用于对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，充放电循环测试的测试电流大于或者等于电池运行工况的电流；
第一确定模块，用于基于第一实测数据和第二实测数据，确定电池的容量衰减率模型；
第二确定模块，用于基于容量衰减率模型，确定电池的循环寿命。
第三方面，本申请的实施例提供了一种电子设备，电子设备包括：
一个或多个处理器；
存储器；以及
一个或多个应用程序，其中一个或多个应用程序被存储于存储器中，并配置为由处理器执行以实现第一方面任一项的电池的循环寿命预测方法中的步骤。
第四方面，本申请还提供一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器进行加载，以执行第一方面任一项的电池的循环寿命预测方法中的步骤。
第五方面，本申请还提供一种计算机程序产品，包括计算机程序/指令，该计算机程序/指令被处理器执行时其用于执行上述第一方面任一项的电池的循环寿命预测方法中的步骤。
有益效果
本申请的实施例的有益效果：
在本申请的实施例中，通过对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，通过对待预测的电池充放电循环测试，且充放电循环测试的测试电流大于电池运行工况的电流，避免了较小电流的充放电循环测试导致的测试周期长、可操作性差，并且相较于单一的充放电测试或者存储循环测试，更加符合电池的实际运行工况，基于第一实测数据和第二实测数据，确定电池的容量衰减率模型，实现了对电池的循环寿命衰减率的预测以及存储寿命衰减率的预测，充分考虑了循环寿命衰减率和存储寿命衰减率对电池的循环寿命预测的影响，使得容量衰减率模型具有较高的准确性，并且根据容量衰减率模型，相较于根据单一测试所得到的容量衰减率模型进行预测，提高了对电池的循环寿命预测的准确性。
 附图说明
图1是本申请实施例中提供的电池的循环寿命预测方法的一个实施例流程示意图；
图2是本申请实施例中提供的第一容量保持率模型的示意图；
图3是本申请实施例中提供的第二容量保持率模型的示意图；
图4是本申请实施例中提供的电池在25℃，测试电流为0.5C下的综合容量衰减率模型、第一容量衰减率模型和第二容量衰减率模型的示意图；
图5是本申请实施例中提供的电池在25℃，测试电流为1C下的综合容量衰减率模型、第一容量衰减率模型和第二容量衰减率模型的示意图；
图6是本申请实施例中提供的电池在工作充放电电流为0.1C时的循环寿命的示意图；
图7是本申请实施例中提供的电池的循环寿命预测装置的一个实施例结构示意图；
图8是本申请实施例中提供的电子设备的一个实施例结构示意图。
 本发明的实施方式
发明人发现，常规认知中的充放电电流越小，电池内部的化学反应越少，电池循环寿命性能越好，同时电池小电流使用时温升较低，进一步提高其寿命性能，对于电池相同吞吐量情况下，与大电流工况电池相比，小电流工况电池充放电由于使用时间过长，并不完全符合电流越小电池寿命性能越好的规律，因此，提供一种正确的能够表征电池的循环寿命预测模型至关重要，本申请提供电池的循环寿命预测方法、电子设备及存储介质。
如图1所示，为本申请实施例中电池的循环寿命预测方法的一个实施例流程示意图，本申请实施例的执行主体为用电设备或用电设备中的控制模块等。该控制模块可以为电池管理系统(Battery Management System，BMS)、整车控制器(Vehicle Control Unit，VCU)等。本申请实施例以执行主体为BMS为例进行详细说明。该电池的循环寿命预测方法包括：
101、对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，充放电循环测试的测试电流大于或者等于电池运行工况的电流。
其中，待预测的电池可以是小电流运行工况的锂离子电池，如磷酸铁锂锂离子电池。测试电流是指电池在充放电过程循环测试过程中的设定的充放电电流，该充放电电流大于电池运行工况的电流，例如，电池运行工况的电流为0.4C，则测试电流可以设定为0.5C、0.8C或者1C等。
充放电循环测试是一种综合了电池在充放电循环过程和存储时间循环过程的循环衰减的测试方法，即常规意义的循环衰减测试。第一实测数据为对电池进行大电流的充放电循环测试后获得的实验数据，该大电流大于电池运行工况的电流，因此，使得第一实测数据能够反映综合电池在充放电循环过程和存储时间循环过程的循环衰减的信息。
存储循环测试是一种用于测试在不同的存储时间下，电池存储衰减的测试方法。第二实测数据为对电池进行存储循环测试后获得的实验数据，因此，使得第二实测数据能够电池在存储循环过程的循环衰减的信息。
具体地，对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，本实施例中，通过对待预测的电池充放电循环测试，且充放电循环测试的测试电流大于电池运行工况的电流，避免了较小电流的充放电循环测试导致的测试周期长、可操作性差，有利于缩短测试周期，同时，本实施例中通过对电池进行充放电循环测试和存储循环测试，相较于单一的充放电测试或者存储循环测试，更加符合电池的实际运行工况，以便后续基于第一实测数据和第二实测数据提高对电池的循环寿命预测的准确性。
值得说明的是，充放电循环过程与存储时间循环过程的容量保持率（容量衰减率）综合方式可以是二者的容量保持率（容量衰减率）的累加，也可以是二者的容量保持率（容量衰减率）的加权累加，此处不做限制。作为本实施例的优选，选取二者的容量保持率（容量衰减率）的累加，以提高后续数据拟合的计算效率。
步骤101中，对待预测的电池进行充放电循环测试，得到第一实测数据，包括：设定电池的测试温度和至少2个测试电流，其中，测试电流大于或者等于电池运行工况的电流；在测试温度下，按照测试电流对电池进行充放电循环测试，得到第一实测数据。
其中，测试温度为电池正常工作的温度，如25℃。测试电流即为设定的测试充放电电流，测试电流需要至少2个，以确保后续根据不同测试电流对应的测试数据，实现对充放电电流与循环寿命之间的关系曲线的拟合。测试电流的下限可以是电池运行工况的电流，其上限电流可以是符合电池的充放电MAP表，确保充放电循环测试过程中，能够避免析锂导致异常衰减即可。
具体地，可以将电池置于温度为测试温度的恒温箱中，采用不同测试电流对电池进行重复多次的充电及放电测试，电池循环充放电的次数称为循环次数，电池在经过充放电循环后，其容量保持率会减小，记录测试温度和测试充放电电流下，不同循环次数的容量保持率，得到第一实测数据，以便后续基于第一实测数据构建综合表征电池的循环寿命和存储寿命的容量保持率模型。
步骤101中，对电池进行存储循环测试，得到第二实测数据，包括：设定电池的测试温度和测试荷电状态；在测试温度下，按照测试荷电状态对电池进行存储测试，得到第二实测数据。
其中，测试荷电状态（State of Charge，SOC）为存储循环测试中的设定的一个荷电状态，由于循环过程的平均SOC为50%，因此，本实施例中测试SOC可以选取50%。本实施例中的测试温度与充放电循环测试中测试温度一致。
具体地，在测试温度和测试SOC下，进行不同存储时间的存储循环测试，电池在经过存储循环后，其容量保持率会减小，记录测试温度和测试SOC下，不同存储时间的容量保持率，得到第二实测数据，以便后续基于第二实测数据构建表征电池的存储寿命的容量保持率模型。
102、基于第一实测数据和第二实测数据，确定电池的容量衰减率模型。
其中，容量衰减率模型为与电池在充放电循环测试和/或存储测试过程中相关的容量衰减率的模型，用于预测电池的循环寿命。
具体地，可以根据第一实测数据和第二实测数据进行曲线拟合，得到容量保持率模型，根据容量保持率与容量衰减率的关系，确定电池的容量衰减率模型。可以理解地，由于第一实测数据能够反映综合电池在充放电循环过程和存储时间循环过程的循环衰减的信息，第二实测数据能够电池在存储循环过程的循环衰减的信息，因此，根据第一实测数据和第二实测数据，确定的容量衰减率模型综合考虑了电池在充放电循环过程和存储时间循环过程的循环衰减，相较于单一的充放电测试或者存储循环测试，充分考虑了循环寿命衰减率和存储寿命衰减率对电池的循环寿命预测的影响，有利于提高电池的循环寿命预测的准确性。
步骤102中，基于第一实测数据和第二实测数据，确定电池的容量衰减率模型，包括：基于第一实测数据和第二实测数据，得到电池的容量保持率模型；基于容量保持率模型确定电池的容量衰减率模型。
其中，容量保持率模型为与电池在充放电循环测试和/或存储测试过程中相关的容量保持率的模型，用于预测电池的容量保持率。
具体地，可以根据第一实测数据和第二实测数据分别进行曲线拟合，得到第一实测数据和第二实测数据各自对应的容量保持率模型，根据容量保持率模型与容量衰减率模型的关系，即通过1-容量保持率模型计算得到容量衰减率模型。
容量保持率模型包括第一容量保持率模型和第二容量保持率模型，其中，第一容量保持率模型为综合表征电池的存储寿命和循环寿命的容量保持率模型，第二容量保持率模型为表征电池的循环寿命的容量保持率模型；基于第一实测数据和第二实测数据，得到电池的容量保持率模型，包括：基于第一实测数据进行曲线拟合，得到电池的第一容量保持率模型；基于第二实测数据进行曲线拟合，得到电池的第二容量保持率模型。
其中，第一容量保持率模型为综合表征电池的存储寿命和循环寿命的容量保持率模型，用于预测电池在进行充放电循环测试过程的容量保持率，即常规意义下的循环寿命，综合了循环充放电的循环寿命和存储时间的存储寿命。该第一容量保持率模型可以是一个表征容量保持率与循环次数、测试温度、测试电流之间的函数表达式。
第二容量保持率模型为表征电池的存储寿命的容量保持率模型，用于预测电池在进行存储循环测试过程的容量保持率。该第二容量保持率模型可以是一个表征容量保持率与存储时间、测试温度、测试SOC之间的函数表达式。
具体地，由于电池的容量衰减率符合高斯退化方程，可以根据容量保持率和容量衰减率之间的关系，即容量保持率=1-容量衰减率，利用高斯退化方程对第一实测数据、第二实测数据进行拟合，得到第一容量保持率模型和第二容量保持率模型。如图2所示，为第一容量保持率模型的示意图，其中，该第一容量保持率模型为在25℃，测试电流分别为0.5C和1C下的曲线，如图3所示，为第二容量保持率模型的示意图，其中，该第二容量保持率模型为在25℃，荷电状态为50%下的曲线。
基于第一实测数据进行曲线拟合，得到电池的第一容量保持率模型，包括：利用第一实测数据对预设的第一高斯退化方程进行求解，得到第一容量保持率模型。
其中，预设的第一高斯退化方程的表达式如下：
（1）
公式（1）中，Q1为在测试温度下的第一容量保持率，C为第一实测数据中的测试充放电电流，n为第一实测数据中的循环次数，f(C)、h(C)均为与C相关的函数，如可以是如C相关的一次函数或者多次函数等。
具体地，将第一实测数据代入公式（1）中，求解得到f(C)、h(C)的中的参数，从而可以得到第一容量保持率模型。
基于第二实测数据进行曲线拟合，得到电池的第二容量保持率模型，包括：利用第二实测数据对预设的第二高斯退化方程进行求解，得到电池的第二容量保持率模型。
其中，预设的第二高斯退化方程的表达式如下：
（2）
公式（2）中，Q2为在测试温度和测试SOC下的第二容量保持率，t为第二实测数据中的存储时间，a和b为待求解的参数。
具体地，将第二实测数据代入公式（2）中，求解得到a和b，从而可以得到第二容量保持率模型。
容量衰减率模型包括第一容量衰减率模型和第二容量衰减率模型，其中，第一容量衰减率模型为表征电池的循环寿命的容量衰减率模型，第二容量衰减率模型为表征电池的存储寿命的容量减率模型；基于容量保持率模型确定电池的容量衰减率模型，包括：根据第二容量保持率模型确定第二容量衰减率模型；根据第一容量保持率模型和第二容量保持率模型，对电池在充放电循环测试过程中的容量衰减率进行分解处理，得到第一容量衰减率模型。
其中，第一容量衰减率模型为表征电池的循环寿命的容量衰减率模型，用于预测电池在进行充放电循环过程的容量衰减率，为非常规意义下的循环衰减，即去掉了（不包含）存储循环过程中的容量衰减率，也即为完全充放电循环的容量衰减率。该第一容量衰减率模型可以是一个表征容量衰减率与循环次数、测试温度、测试电流之间的函数表达式。
第二容量衰减率模型为表征电池的存储寿命的容量衰减率的模型，用于预测电池在进行存储循环过程的容量衰减率。该第二容量衰减率模型可以是一个表征容量衰减率与存储时间、测试温度、测试SOC之间的函数表达式。
具体地，由于容量保持率与容量衰减率满足如下关系：容量衰减率+容量保持率=1，因此，可以根据该关系式，将第一容量保持率模型、第二容量保持率模型转换为各自对应的容量衰减率模型。由于第二容量保持率模型表征电池的存储寿命的容量衰减率，因此，第二容量保持率模型对应的容量衰减率模型，即为第二容量衰减率模型。同理，由于第一容量保持率模型综合表征小电流工况电池的存储寿命和循环寿命的容量保持率，因此，第一容量保持率模型对应的容量衰减率模型，则对应综合表征电池的存储寿命和循环寿命的容量衰减率，因此，只需要对第一容量保持率模型对应的容量衰减率模型进行分解，如去掉第二容量衰减率模型，即可得到第一容量衰减率模型。
具体地，可以通过1-第二容量保持率模型，计算得到第二容量衰减率模型，通过1-第一容量保持率模型，计算得到综合容量衰减率模型，然后，根据第二容量衰减率模型，对综合容量衰减率模型进行分解处理，如综合容量衰减率模型减去第二容量衰减率模型，即可计算得到第一容量衰减率模型。如图4所示，为电池在25℃，测试电流为0.5C下的综合容量衰减率模型、第一容量衰减率模型和第二容量衰减率模型的示意图，其中，从上至下的3条曲线分别为综合容量衰减率模型、第一容量衰减率模型和第二容量衰减率模型的曲线，如图5所示，为电池在在25℃，测试电流为1C下的综合容量衰减率模型、第一容量衰减率模型和第二容量衰减率模型的示意图，其中，从上至下的3条曲线分别为综合容量衰减率模型、第一容量衰减率模型和第二容量衰减率模型的曲线。
可以理解地，本实施例中，通过确定表征电池的循环寿命的容量衰减率的第一容量衰减率模型，即可实现对电池的循环寿命的容量衰减率的预测，也即实现了完全充放电循环（不包含存储循环）的容量衰减的预测，以便后续基于该第一容量衰减率模型，实现对电池的循环寿命的预测。
103、基于容量衰减率模型，确定电池的循环寿命。
其中，电池的循环寿命可以通过电池的容量保持率表征，也即通过1-容量衰减率，计算得到的结果，如健康状态（state of health，SOH）表征。
具体地，可以根据容量衰减率模型确定电池的健康状态，即可确定电池的循环寿命。可以理解地，由于容量衰减率模型综合考虑了电池在充放电循环过程和存储时间循环过程的循环衰减，相较于单一的充放电测试或者存储循环测试，充分考虑了循环寿命衰减率和存储寿命衰减率对电池的循环寿命预测的影响，具有较高的准确性，并且根据容量衰减率模型预测电池的循环寿命，相较于根据单一的容量衰减率模型进行预测，提高了对电池的循环寿命预测的准确性。
，将第一容量衰减率模型对应的第一表达式与第二容量衰减率模型对应的表达式进行求和运算，得到电池的衰减率累计和模型对应的第三表达式；根据电池的工作充放电电流和衰减率累计和模型对应的第三表达式确定电池的循环寿命。
具体地，可以根据第一容量衰减率模型和第二容量衰减率模型，确定电池的循环寿命，如通过如下公式：Q SOH= ，其中，Q SOH表示为电池的循环寿命， 表示第一容量衰减率模型， 表示第二容量衰减率模型，从而实现对电池的循环寿命的预测。可以理解地，本实施例中，第一容量衰减率模型和第二容量衰减率模型具有较高的准确性，并且根据第一容量衰减率模型和第二容量衰减率模型，相较于根据单一的容量衰减率模型进行预测，提高了对电池的循环寿命预测的准确性。
在一个具体实施方式中，第一容量衰减率模型的表达式如下：
（3）
其中，C为工作充放电电流， 及 为C相关的函数。示例性地， 及 可为一次函数，第一容量衰减率模型：
 （4）
第二容量衰减率模型的表达式如下：
（5）
其中，t为存储时间，a和b为常数，第二容量衰减率模型：
（6）  （7）
示例性地，当电池的工作充放电电流为0.1C，循环次数n=1，充放电时间t=20h，温度为25℃，其循环寿命的模型如图6所示，图6中的曲线为电池在工作充放电电流为0.1C时的循环寿命的示意图。
更具体地，将第一容量衰减率模型对应的第一表达式与第二容量衰减率模型对应的表达式进行求和运算，即 ， 表示衰减率累计和模型，然后根据100%容量保持率和Q L，通过如下公式：Q SOH=1-Q L，将工作充放电电流代入Q SOH中，即可计算得到电池的循环寿命。
上述电池的循环寿命预测方法，通过对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，通过对待预测的电池充放电循环测试，且充放电循环测试的测试电流大于电池运行工况的电流，避免了较小电流的充放电循环测试导致的测试周期长、可操作性差，并且相较于单一的充放电测试或者存储循环测试，更加符合电池的实际运行工况，基于第一实测数据和第二实测数据，确定电池的容量衰减率模型，实现了对电池的循环寿命衰减率的预测以及存储寿命衰减率的预测，充分考虑了循环寿命衰减率和存储寿命衰减率对电池的循环寿命预测的影响，使得容量衰减率模型具有较高的准确性，并且根据容量衰减率模型，相较于根据单一测试所得到的容量衰减率模型进行预测，提高了对电池的循环寿命预测的准确性。
如图7所示，本申请实施例还提供一种电池的循环寿命预测装置200，电池的循环寿命预测装置包括：
测试模块，用于对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，充放电循环测试的测试电流大于或者等于电池运行工况的电流；
第一确定模块，用于基于第一实测数据和第二实测数据，确定电池的容量衰减率模型；
第二确定模块，用于基于容量衰减率模型，确定电池的循环寿命。
在一实施例中，第一确定模块202具体用于：
基于第一实测数据和第二实测数据，得到电池的容量保持率模型；
基于容量保持率模型确定电池的容量衰减率模型。
在一实施例中，容量保持率模型包括第一容量保持率模型和第二容量保持率模型，其中，第一容量保持率模型为综合表征电池的存储寿命和循环寿命的容量保持率模型，第二容量保持率模型为表征电池的循环寿命的容量保持率模型；第一确定模块202具体还用于：
基于第一实测数据进行曲线拟合，得到电池的第一容量保持率模型；
基于第二实测数据进行曲线拟合，得到电池的第二容量保持率模型。
在一实施例中，容量衰减率模型包括第一容量衰减率模型和第二容量衰减率模型，其中，第一容量衰减率模型为表征电池的循环寿命的容量衰减率模型，第二容量衰减率模型为表征电池的存储寿命的容量减率模型；第二确定模块203具体用于：
根据第二容量保持率模型确定第二容量衰减率模型；
根据第一容量保持率模型和第二容量保持率模型，对电池在充放电循环测试过程中的容量衰减率进行分解处理，得到第一容量衰减率模型。
在一实施例中，第二确定模块203具体还用于：
根据第二容量保持率模型确定第二容量衰减率模型；
根据第一容量保持率模型确定综合容量衰减率模型，综合容量衰减率模型为综合表征电池的存储寿命和循环寿命的容量衰减率模型；
根据第二容量衰减率模型，对综合容量衰减率模型进行分解处理，得到第一容量衰减率模型。
在一实施例中，测试模块201具体用于：
设定电池的测试温度和至少2个测试电流，其中，测试电流大于或者等于电池运行工况的电流；
在测试温度下，按照测试充放电电流对电池进行充放电循环测试，得到第一实测数据。
在一实施例中，测试模块201具体还用于：
设定电池的测试温度和测试荷电状态；
在测试温度下，按照测试荷电状态对电池进行存储测试，得到第二实测数据。
在一实施例中，第一确定模块202具体还用于：
利用第一实测数据对预设的第一高斯退化方程进行求解，得到第一容量保持率模型。
在一实施例中，第一确定模块202具体还用于：
利用第二实测数据对预设的第二高斯退化方程进行求解，得到电池的第二容量保持率模型。
在一实施例中，第二确定模块203具体用于：
将第一容量衰减率模型对应的第一表达式与第二容量衰减率模型对应的表达式进行求和运算，得到电池的衰减率累计和模型对应的第三表达式；
根据电池的工作充放电电流和衰减率累计和模型对应的第三表达式确定电池的循环寿命。
本申请实施例还提供一种电子设备，其集成了本申请实施例所提供的任一种电池的循环寿命预测装置，电子设备包括：
一个或多个处理器；
存储器；以及
一个或多个应用程序，其中一个或多个应用程序被存储于存储器中，并配置为由处理器执行上述电池的循环寿命预测方法实施例中任一实施例中的电池的循环寿命预测方法。
本申请实施例还提供一种电子设备，其集成了本申请实施例所提供的任一种电池的循环寿命预测装置。如图8所示，其示出了本申请实施例所涉及的电子设备的结构示意图，具体来讲：
该电子设备可以包括一个或者一个以上处理核心的处理器301、一个或一个以上计算机可读存储介质的存储器302、电源303和输入单元304等部件。本领域技术人员可以理解，图8中示出的电子设备结构并不构成对电子设备的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。其中：
处理器301是该电子设备的控制中心，利用各种接口和线路连接整个电子设备的各个部分，通过运行或执行存储在存储器302内的软件程序和/或模块，以及调用存储在存储器302内的数据，执行电子设备的各种功能和处理数据，从而对电子设备进行整体监控。可选的，处理器301可包括一个或多个处理核心；优选的，处理器301可集成应用处理器和调制解调处理器，其中，应用处理器主要处理操作系统、用户界面和应用程序等，调制解调处理器主要处理无线通信。可以理解的是，上述调制解调处理器也可以不集成到处理器301中。
存储器302可用于存储软件程序以及模块，处理器301通过运行存储在存储器302的软件程序以及模块，从而执行各种功能应用以及数据处理。存储器302可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序（比如声音播放功能、图像播放功能等）等；存储数据区可存储根据电子设备的使用所创建的数据等。此外，存储器302可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。相应地，存储器302还可以包括存储器控制器，以提供处理器301对存储器302的访问。
电子设备还包括给各个部件供电的电源303，优选的，电源303可以通过电源管理系统与处理器301逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。电源303还可以包括一个或一个以上的直流或交流电源、再充电系统、电源故障检测电路、电源转换器或者逆变器、电源状态指示器等任意组件。
该电子设备还可包括输入单元304，该输入单元304可用于接收输入的数字或字符信息，以及产生与用户设置以及功能控制有关的键盘、鼠标、操作杆、光学或者轨迹球信号输入。
尽管未示出，电子设备还可以包括显示单元等，在此不再赘述。具体在本实施例中，电子设备中的处理器301会按照如下的指令，将一个或一个以上的应用程序的进程对应的可执行文件加载到存储器302中，并由处理器301来运行存储在存储器302中的应用程序，从而实现各种功能，如下：
对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，充放电循环测试的测试电流大于或者等于电池运行工况的电流；
基于第一实测数据和第二实测数据，确定电池的容量衰减率模型；
基于容量衰减率模型，确定电池的循环寿命。
本领域普通技术人员可以理解，上述实施例的各种方法中的全部或部分步骤可以通过指令来完成，或通过指令控制相关的硬件来完成，该指令可以存储于一计算机可读存储介质中，并由处理器进行加载和执行。
为此，本申请实施例提供一种计算机可读存储介质，该存储介质可以包括：只读存储器（Read Only Memory，ROM）、随机存取记忆体（Random Access Memory，RAM）、磁盘或光盘等。其上存储有计算机程序，计算机程序被处理器进行加载，以执行本申请实施例所提供的任一种电池的循环寿命预测方法中的步骤。例如，计算机程序被处理器进行加载可以执行如下步骤：
对待预测的电池进行充放电循环测试，得到第一实测数据，并对电池进行存储循环测试，得到第二实测数据，充放电循环测试的测试电流大于或者等于电池运行工况的电流；
基于第一实测数据和第二实测数据，确定电池的容量衰减率模型；
基于容量衰减率模型，确定电池的循环寿命。
本申请实施例还提供一种计算机程序产品，包括计算机程序/指令，该计算机程序/指令被处理器执行时其用于执行申请实施例所提供的任一种电池的循环寿命预测方法中的步骤。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见上文针对其他实施例的详细描述，此处不再赘述。
具体实施时，以上各个单元或结构可以作为独立的实体来实现，也可以进行任意组合，作为同一或若干个实体来实现，以上各个单元或结构的具体实施可参见前面的方法实施例，在此不再赘述。

Claims (12)

  1. 一种电池的循环寿命预测方法，包括：
    对待预测的电池进行充放电循环测试，得到第一实测数据，并对所述电池进行存储循环测试，得到第二实测数据，所述充放电循环测试的测试电流大于或者等于所述电池运行工况的电流；
    基于所述第一实测数据和所述第二实测数据，确定所述电池的容量衰减率模型；
    基于所述容量衰减率模型，确定所述电池的循环寿命。
  2. 根据权利要求1所述的电池的循环寿命预测方法，其中，所述基于所述第一实测数据和所述第二实测数据，确定所述电池的容量衰减率模型，包括：
    基于所述第一实测数据和所述第二实测数据，得到所述电池的容量保持率模型；
    基于所述容量保持率模型确定所述电池的容量衰减率模型。
  3. 根据权利要求2所述的电池的循环寿命预测方法，其中，所述容量保持率模型包括第一容量保持率模型和第二容量保持率模型，其中，所述第一容量保持率模型为综合表征所述电池的存储寿命和循环寿命的容量保持率模型，所述第二容量保持率模型为表征所述电池的循环寿命的容量保持率模型；
    所述基于所述第一实测数据和所述第二实测数据，得到所述电池的容量保持率模型，包括：
    基于所述第一实测数据进行曲线拟合，得到所述电池的第一容量保持率模型；
    基于所述第二实测数据进行曲线拟合，得到所述电池的第二容量保持率模型。
  4. 根据权利要求3所述的电池的循环寿命预测方法，其中，所述容量衰减率模型包括第一容量衰减率模型和第二容量衰减率模型，其中，所述第一容量衰减率模型为表征所述电池的循环寿命的容量衰减率模型，所述第二容量衰减率模型为表征所述电池的存储寿命的容量减率模型；
    所述基于所述容量保持率模型确定所述电池的容量衰减率模型，包括：
    根据所述第二容量保持率模型确定所述第二容量衰减率模型；
    根据所述第一容量保持率模型和所述第二容量保持率模型，对所述电池在充放电循环测试过程中的容量衰减率进行分解处理，得到所述第一容量衰减率模型。
  5. 根据权利要求4所述的电池的循环寿命预测方法，其中，所述根据所述第一容量保持率模型和所述第二容量保持率模型，对所述电池在充放电循环测试过程中的容量衰减率进行分解处理，得到所述第一容量衰减率模型，包括：
    根据所述第二容量保持率模型确定所述第二容量衰减率模型；
    根据所述第一容量保持率模型确定综合容量衰减率模型，所述综合容量衰减率模型为综合表征电池的存储寿命和循环寿命的容量衰减率模型；
    根据所述第二容量衰减率模型，对所述综合容量衰减率模型进行分解处理，得到所述第一容量衰减率模型。
  6. 根据权利要求1所述的电池的循环寿命预测方法，其中，所述对待预测的电池进行充放电循环测试，得到第一实测数据，包括：
    设定所述电池的测试温度和至少2个测试电流，其中，所述测试电流大于或者等于所述电池运行工况的电流；
    在所述测试温度下，按照所述测试电流对所述电池进行充放电循环测试，得到所述第一实测数据。
  7. 根据权利要求1所述的电池的循环寿命预测方法，其中，所述对所述电池进行存储循环测试，得到第二实测数据，包括：
    设定所述电池的测试温度和测试荷电状态；
    在所述测试温度下，按照所述测试荷电状态对所述电池进行存储测试，得到所述第二实测数据。
  8. 根据权利要求3所述的电池的循环寿命预测方法，其中，所述基于所述第一实测数据进行曲线拟合，得到所述电池的第一容量保持率模型，包括：
    利用所述第一实测数据对预设的第一高斯退化方程进行求解，得到所述第一容量保持率模型。
  9. 根据权利要求3所述的电池的循环寿命预测方法，其中，所述基于所述第二实测数据进行曲线拟合，得到所述电池的第二容量保持率模型，包括：
    利用所述第二实测数据对预设的第二高斯退化方程进行求解，得到所述电池的第二容量保持率模型。
  10. 根据权利要求4所述的电池的循环寿命预测方法，其中，所述基于所述容量衰减率模型，确定所述电池的循环寿命，包括：
    将所述第一容量衰减率模型对应的第一表达式与所述第二容量衰减率模型对应的表达式进行求和运算，得到所述电池的衰减率累计和模型对应的第三表达式；
    根据所述电池的工作充放电电流和所述衰减率累计和模型对应的第三表达式确定所述电池的循环寿命。
  11. 一种电子设备，所述电子设备包括：
    一个或多个处理器；
    存储器；以及
    一个或多个应用程序，其中所述一个或多个应用程序被存储于所述存储器中，并配置为由所述处理器执行以实现权利要求1至10中任一项所述的电池的循环寿命预测方法。
  12. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器进行加载，以执行权利要求1至10任一项所述的电池的循环寿命预测方法。
PCT/CN2024/134721 2024-07-15 2024-11-27 电池的循环寿命预测方法、电子设备及存储介质 Pending WO2026016366A1 (zh)

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