Cycle life prediction method for battery, electronic device, and storage medium

Abstract

The present application provides a cycle life prediction method for a battery, an electronic device, and a storage medium. The method comprises: performing a charge-discharge cycle test on a battery to undergo prediction, to obtain first measured data, and performing a storage cycle test on the battery, to obtain second measured data; on the basis of the first measured data and the second measured data, determining a capacity degradation rate model of the battery; and on the basis of the capacity degradation rate model, determining the cycle life of the battery, thereby improving the accuracy of battery cycle life prediction.

Description

Battery cycle life prediction method, electronic device and storage medium

[0001] The present application claims priority to the Chinese patent application No. 202410948851.2, filed on July 15, 2024, to the Chinese Patent Office, the whole content of the above application being incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of battery, in particular to a battery cycle life prediction method, an electronic device and a storage medium. BACKGROUND

[0003] The small current working condition refers to that the charge and discharge current of the battery is less than a preset threshold current, for example, the preset threshold current is 0.5C, and the charge and discharge current of the small current working condition battery is less than 0.5C. In the application scenario of the battery, there is a small current working condition of the battery, and the cycle life of the battery is an important performance evaluation index, which directly affects the use time and quality of the battery. Therefore, correctly evaluating the cycle life of the small current working condition battery is crucial for the evaluation of the battery health state.

[0004] For the evaluation of the cycle life of the small current working condition battery, there are two main methods in the related art: electrochemical model method and data-driven method. SUMMARY

[0005] However, the accuracy of the electrochemical model method for evaluating the cycle life depends on the complexity of its model, there are many electrochemical parameters, and the test is complex and expensive. The data-driven method needs to combine the measured data, but the test period of the small current working condition battery is long, the time of one charge and discharge cycle is more than 20h, and the overall test period is more than 1 year, which is low in operability.

[0006] Embodiments of the present application provide a battery cycle life prediction method, an electronic device and a storage medium, which can quickly predict the cycle life of the battery, reduce the test cost, and also take into account the accuracy of the prediction.

[0007] In a first aspect, embodiments of the present application provide a battery cycle life prediction method, the method comprising:

[0008] Performing a charge and discharge cycle test on the battery to be predicted to obtain first measured data, and performing a storage cycle test on the battery to obtain second measured data, the test current of the charge and discharge cycle test being greater than or equal to the current of the battery working condition;

[0009] Determining a capacity attenuation rate model of the battery based on the first measured data and the second measured data;

[0010] Determining the cycle life of the battery based on the capacity attenuation rate model.

[0011] In a second aspect, embodiments of the present application provide a cycle life prediction device of a battery, the cycle life prediction device of the battery comprising:

[0012] a test module configured to perform a charge-discharge cycle test on the battery to be predicted to obtain first measured data, and perform a storage cycle test on the battery to obtain second measured data, wherein a test current of the charge-discharge cycle test is greater than or equal to a current of an operating condition of the battery;

[0013] a first determination module configured to determine a capacity attenuation rate model of the battery based on the first measured data and the second measured data;

[0014] a second determination module configured to determine a cycle life of the battery based on the capacity attenuation rate model.

[0015] In a third aspect, embodiments of the present application provide an electronic device, the electronic device comprising:

[0016] one or more processors;

[0017] a memory; and

[0018] one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the steps in the cycle life prediction method of the battery of any one of the first aspect.

[0019] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to execute the steps in the cycle life prediction method of the battery of any one of the first aspect.

[0020] In a fifth aspect, the present application further provides a computer program product comprising computer programs / instructions, which, when executed by a processor, are used to execute the steps in the cycle life prediction method of the battery of any one of the first aspect. Advantages

[0021] Advantages of embodiments of the present application:

[0022] In the embodiment of the present application, the first measured data is obtained by performing the charge-discharge cycle test on the battery to be predicted, and the second measured data is obtained by performing the storage cycle test on the battery. The charge-discharge cycle test is performed on the battery to be predicted, and the test current of the charge-discharge cycle test is greater than the current of the battery operating condition, thereby avoiding the long test period and poor operability caused by the charge-discharge cycle test with small current. Compared with the single charge-discharge test or the storage cycle test, the charge-discharge cycle test is more consistent with the actual operating condition of the battery. Based on the first measured data and the second measured data, the capacity decay rate model of the battery is determined, the cycle life decay rate prediction and the storage life decay rate prediction of the battery are realized, the influence of the cycle life decay rate and the storage life decay rate on the cycle life prediction of the battery is fully considered, the capacity decay rate model has high accuracy, and according to the capacity decay rate model, compared with the prediction based on the capacity decay rate model obtained by the single test, the accuracy of the cycle life prediction of the battery is improved. BRIEF DESCRIPTION OF DRAWINGS

[0023] FIG. 1 is a flow diagram of one embodiment of the cycle life prediction method of the battery provided in the embodiment of the present application;

[0024] FIG. 2 is a schematic diagram of the first capacity retention rate model provided in the embodiment of the present application;

[0025] FIG. 3 is a schematic diagram of the second capacity retention rate model provided in the embodiment of the present application;

[0026] FIG. 4 is a schematic diagram of the integrated capacity decay rate model, the first capacity decay rate model and the second capacity decay rate model of the battery at 25°C and the test current of 0.5C provided in the embodiment of the present application;

[0027] FIG. 5 is a schematic diagram of the integrated capacity decay rate model, the first capacity decay rate model and the second capacity decay rate model of the battery at 25°C and the test current of 1C provided in the embodiment of the present application;

[0028] FIG. 6 is a schematic diagram of the cycle life of the battery at the working charge-discharge current of 0.1C provided in the embodiment of the present application;

[0029] FIG. 7 is a schematic diagram of one embodiment of the cycle life prediction device of the battery provided in the embodiment of the present application;

[0030] FIG. 8 is a schematic diagram of one embodiment of the electronic device provided in the embodiment of the present application. Embodiments of the present application

[0031] The inventor finds that the smaller the charging and discharging current in the conventional cognition, the fewer the chemical reactions inside the battery, and the better the cycle life performance of the battery. At the same time, the temperature rise of the battery at small current is low, which further improves the life performance. For the same throughput of the battery, compared with the battery in large current condition, the battery in small current condition does not completely meet the rule that the smaller the current, the better the life performance of the battery, because it is used for a long time. Therefore, it is very important to provide a correct cycle life prediction model to characterize the battery. The application provides a cycle life prediction method of a battery, an electronic device and a storage medium.

[0032] As shown in FIG. 1, it is an embodiment flow diagram of the cycle life prediction method of the battery in the embodiment of the application. The execution subject of the embodiment of the application is a power consumption device or a control module in the power consumption device. The control module can be a battery management system (BMS), a vehicle control unit (VCU), etc. The embodiment of the application takes the BMS as an example for detailed description. The cycle life prediction method of the battery comprises:

[0033] 101, performing a charging and discharging cycle test on the battery to be predicted to obtain first measured data, and performing a storage cycle test on the battery to obtain second measured data. The test current of the charging and discharging cycle test is greater than or equal to the current of the battery operating condition.

[0034] The battery to be predicted can be a small current operating condition lithium ion battery, such as a lithium iron phosphate lithium ion battery. The test current refers to the set charging and discharging current of the battery in the charging and discharging cycle test process. The charging and discharging current is greater than the current of the battery operating condition. For example, the current of the battery operating condition is 0.4C, and the test current can be set to 0.5C, 0.8C or 1C, etc.

[0035] The charging and discharging cycle test is a test method that comprehensively considers the cycle attenuation of the battery in the charging and discharging cycle process and the storage time cycle process, that is, the conventional sense of cycle attenuation test. The first measured data is the experimental data obtained after the charging and discharging cycle test of the battery at a large current. The large current is greater than the current of the battery operating condition, so that the first measured data can reflect the information of the comprehensive cycle attenuation of the battery in the charging and discharging cycle process and the storage time cycle process.

[0036] The storage cycle test is a test method for testing the storage attenuation of the battery at different storage times. The second measured data is the experimental data obtained after the storage cycle test of the battery. Therefore, the second measured data can reflect the cycle attenuation information of the battery in the storage cycle process.

[0037] Specifically, the battery to be predicted is subjected to a charge-discharge cycle test to obtain first measured data, and the battery is subjected to a storage cycle test to obtain second measured data. In this embodiment, the battery to be predicted is subjected to a charge-discharge cycle test, and the test current of the charge-discharge cycle test is greater than the current of the battery operating condition, thereby avoiding a long test period and poor operability caused by a charge-discharge cycle test with a small current, and the test period is shortened. In this embodiment, the battery is subjected to a charge-discharge cycle test and a storage cycle test, which is more in line with the actual operating condition of the battery compared with a single charge-discharge test or a storage cycle test, so as to improve the accuracy of the cycle life prediction of the battery based on the first measured data and the second measured data.

[0038] It should be noted that the capacity retention rate (capacity attenuation rate) of the charge-discharge cycle process and the storage time cycle process can be the accumulation of the capacity retention rates (capacity attenuation rates) of the two or the weighted accumulation of the capacity retention rates (capacity attenuation rates) of the two, which is not limited here. As a preferred embodiment of the present embodiment, the accumulation of the capacity retention rates (capacity attenuation rates) of the two is selected to improve the calculation efficiency of subsequent data fitting.

[0039] In step 101, the battery to be predicted is subjected to a charge-discharge cycle test to obtain first measured data, including: setting the test temperature and at least two test currents of the battery, 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 according to the test current to obtain the first measured data.

[0040] The test temperature is the normal working temperature of the battery, such as 25℃. The test current is the set test charge-discharge current, and at least two test currents are required to ensure that the relationship curve between the charge-discharge current and the 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 of the battery operating condition, and the upper limit of the test current can be the charge-discharge MAP table of the battery, which can ensure that abnormal attenuation caused by lithium precipitation can be avoided during the charge-discharge cycle test.

[0041] Specifically, the battery can be placed in a thermostat with a temperature of the test temperature, and the battery can be subjected to repeated charge and discharge tests with different test currents. The number of times of the charge-discharge cycle of the battery is referred to as the cycle number. The capacity retention rate of the battery decreases after the charge-discharge cycle. The capacity retention rate at different cycle numbers under the test temperature and the test charge-discharge current is recorded to obtain the first measured data, so as to construct a capacity retention rate model comprehensively representing the cycle life and the storage life of the battery based on the first measured data.

[0042] In step 101, the storage cycle test is performed on the battery to obtain second measured data, including: setting a test temperature and a test state of charge of the battery; performing a storage test on the battery at the test temperature and according to the test state of charge to obtain the second measured data.

[0043] The test state of charge (SOC) is a set state of charge in the storage cycle test, and the average SOC in the cycle process is 50%, so the test SOC in this embodiment can be 50%. The test temperature in this embodiment is consistent with the test temperature in the charge-discharge cycle test.

[0044] Specifically, at the test temperature and the test SOC, storage cycle tests of different storage times are performed, and the capacity retention rate of the battery will decrease after the storage cycle. The capacity retention rate of different storage times at the test temperature and the test SOC is recorded to obtain the second measured data, so as to subsequently construct a capacity retention rate model representing the storage life of the battery based on the second measured data.

[0045] 102. Determine the capacity decay rate model of the battery based on the first measured data and the second measured data.

[0046] The capacity decay rate model is a model of the capacity decay rate related to the battery in the charge-discharge cycle test and / or the storage test, and is used to predict the cycle life of the battery.

[0047] Specifically, the capacity retention rate model can be obtained by curve fitting according to the first measured data and the second measured data, and the capacity decay rate model of the battery can be determined according to the relationship between the capacity retention rate and the capacity decay rate. It can be understood that, since the first measured data can reflect the information of the cycle decay of the battery in the charge-discharge cycle process and the storage time cycle process, and the second measured data can reflect the information of the cycle decay of the battery in the storage cycle process, therefore, the capacity decay rate model determined according to the first measured data and the second measured data comprehensively considers the cycle decay of the battery in the charge-discharge cycle process and the storage time cycle process. Compared with a single charge-discharge test or a storage cycle test, the cycle life prediction of the battery is fully considered by the cycle life decay rate and the storage life decay rate, which is beneficial to improve the accuracy of the cycle life prediction of the battery.

[0048] In step 102, 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; determining the capacity decay rate model of the battery based on the capacity retention rate model.

[0049] The capacity retention rate model is a model of the capacity retention rate related to the battery during the charge-discharge cycle test and / or storage test, and is used to predict the capacity retention rate of the battery.

[0050] Specifically, curve fitting can be performed according to the first measured data and the second measured data respectively to obtain the capacity retention rate model corresponding to the first measured data and the second measured data respectively, and the capacity decay rate model is calculated by 1-capacity retention rate model according to the relationship between the capacity retention rate model and the capacity decay rate model.

[0051] The capacity retention rate model includes a first capacity retention rate model and a second capacity retention rate model, wherein the first capacity retention rate model is a capacity retention rate model that comprehensively characterizes the storage life and cycle life of the battery, and the second capacity retention rate model is a capacity retention rate model that characterizes the cycle life of the battery; based on the first measured data and the second measured data, the capacity retention rate model of the battery is obtained, including: based on the first measured data, curve fitting is performed to obtain the first capacity retention rate model of the battery; based on the second measured data, curve fitting is performed to obtain the second capacity retention rate model of the battery.

[0052] The first capacity retention rate model is a capacity retention rate model that comprehensively characterizes the storage life and cycle life of the battery, and is used to predict the capacity retention rate of the battery during the charge-discharge cycle test, i.e. the cycle life in the conventional sense, which comprehensively considers the cycle life of the cycle charge-discharge and the storage life of the storage time. The first capacity retention rate model can be a function expression representing the relationship between the capacity retention rate and the cycle number, the test temperature, and the test current.

[0053] The second capacity retention rate model is a capacity retention rate model that characterizes the storage life of the battery, and is used to predict the capacity retention rate of the battery during the storage cycle test. The second capacity retention rate model can be a function expression representing the relationship between the capacity retention rate and the storage time, the test temperature, and the test SOC.

[0054] Specifically, since the capacity decay rate of the battery conforms to the Gaussian degradation equation, the first measured data and the second measured data can be fitted by using the Gaussian degradation equation according to the relationship between the capacity retention rate and the capacity decay rate, i.e. capacity retention rate = 1-capacity decay rate, to obtain the first capacity retention rate model and the second capacity retention rate model. As shown in FIG. 2, it is a schematic diagram of the first capacity retention rate model, wherein the first capacity retention rate model is a curve at 25°C and test currents of 0.5C and 1C, as shown in FIG. 3, it is a schematic diagram of the second capacity retention rate model, wherein the second capacity retention rate model is a curve at 25°C and a state of charge of 50%.

[0055] The first capacity retention rate model of the battery is obtained by curve fitting based on the first measured data, including: solving the preset first Gaussian degradation equation by using the first measured data to obtain the first capacity retention rate model.

[0056] The expression of the preset first Gaussian degradation equation is as follows:

[0057] (1)

[0058] In formula (1), Q1 is the first capacity retention rate at the test temperature, C is the test charge-discharge current in the first measured data, n is the cycle number in the first measured data, and f(C) and h(C) are functions related to C, which can be a linear function or a multiple function related to C.

[0059] Specifically, the first measured data is substituted into formula (1), and the parameters in f(C) and h(C) are solved to obtain the first capacity retention rate model.

[0060] The second capacity retention rate model of the battery is obtained by curve fitting based on the second measured data, including: solving the preset second Gaussian degradation equation by using the second measured data to obtain the second capacity retention rate model of the battery.

[0061] The expression of the preset second Gaussian degradation equation is as follows:

[0062] (2)

[0063] In formula (2), Q2 is the second capacity retention rate at the test temperature and the test SOC, t is the storage time in the second measured data, and a and b are parameters to be solved.

[0064] Specifically, the second measured data is substituted into formula (2), and a and b are solved to obtain the second capacity retention rate model.

[0065] 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 representing the cycle life of the battery, and the second capacity decay rate model is a capacity decay rate model representing the storage life of the battery; the capacity decay rate model of the battery is determined based on the capacity retention rate model, including: determining the second capacity decay rate model according to the second capacity retention rate model; and decomposing the capacity decay rate of the battery in the charge-discharge cycle test process according to the first capacity retention rate model and the second capacity retention rate model to obtain the first capacity decay rate model.

[0066] The first capacity fade rate model is a capacity fade rate model representing the cycle life of the battery, and is used to predict the capacity fade rate of the battery in the process of charge-discharge cycling. The cycle fade is a cycle fade in a non-conventional sense, i.e., the capacity fade rate in the process of storage cycling is removed (not included), i.e., the capacity fade rate of a full charge-discharge cycle. The first capacity fade rate model can be a function expression representing the relationship between the capacity fade rate and the cycle number, the test temperature, and the test current.

[0067] The second capacity fade rate model is a capacity fade rate model representing the storage life of the battery, and is used to predict the capacity fade rate of the battery in the process of storage cycling. The second capacity fade rate model can be a function expression representing the relationship between the capacity fade rate and the storage time, the test temperature, and the test SOC.

[0068] Specifically, since the capacity retention rate and the capacity fade rate satisfy the following relationship: capacity fade rate + capacity retention rate = 1, the first capacity retention rate model and the second capacity retention rate model can be converted into the respective corresponding capacity fade rate models according to the relationship. Since the second capacity retention rate model represents the capacity fade rate of the storage life of the battery, the capacity fade rate model corresponding to the second capacity retention rate model is the second capacity fade rate model. Similarly, since the first capacity retention rate model comprehensively represents the capacity retention rate of the storage life and the cycle life of the battery in the small current condition, the capacity fade rate model corresponding to the first capacity retention rate model comprehensively represents the capacity fade rate of the storage life and the cycle life of the battery. Therefore, only the capacity fade rate model corresponding to the first capacity retention rate model needs to be decomposed, such as removing the second capacity fade rate model, to obtain the first capacity fade rate model.

[0069] Specifically, the second capacity fade rate model can be calculated by 1-the second capacity retention rate model, the comprehensive capacity fade rate model can be calculated by 1-the first capacity retention rate model, and then the first capacity fade rate model can be calculated by decomposing the comprehensive capacity fade rate model according to the second capacity fade rate model, such as subtracting the second capacity fade rate model from the comprehensive capacity fade rate model. As shown in FIG. 4, which is a schematic diagram of the comprehensive capacity fade rate model, the first capacity fade rate model, and the second capacity fade rate model of the battery under the test current of 0.5C at 25°C, the three curves from top to bottom are the curves of the comprehensive capacity fade rate model, the first capacity fade rate model, and the second capacity fade rate model, respectively. As shown in FIG. 5, which is a schematic diagram of the comprehensive capacity fade rate model, the first capacity fade rate model, and the second capacity fade rate model of the battery under the test current of 1C at 25°C, the three curves from top to bottom are the curves of the comprehensive capacity fade rate model, the first capacity fade rate model, and the second capacity fade rate model, respectively.

[0070] It can be understood that in the embodiment, the capacity fade rate model representing the capacity fade rate of the cycle life of the battery is determined, that is, the prediction of the capacity fade of the full charge and discharge cycle (not including the storage cycle) is realized, so as to realize the prediction of the cycle life of the battery based on the first capacity fade rate model subsequently.

[0071] 103. Determine the cycle life of the battery based on the capacity fade rate model.

[0072] The cycle life of the battery can be represented by the capacity retention rate of the battery, that is, the result calculated by 1-capacity fade rate, such as state of health (SOH).

[0073] Specifically, the health state of the battery can be determined according to the capacity fade rate model, that is, the cycle life of the battery can be determined. It can be understood that, compared with a single charge and discharge test or a storage cycle test, the capacity fade rate model comprehensively considers the cycle fade of the battery in the charge and discharge cycle process and the storage time cycle process, fully considers the influence of the cycle life fade rate and the storage life fade rate on the prediction of the cycle life of the battery, has high accuracy, and according to the capacity fade rate model, the prediction accuracy of the cycle life of the battery is improved compared with the prediction according to a single capacity fade rate model.

[0074] The first expression corresponding to the first capacity fade rate model and the expression corresponding to the second capacity fade rate model are summed to obtain a third expression corresponding to the decay rate cumulative sum model of the battery; and the cycle life of the battery is determined according to the working charge and discharge current of the battery and the third expression corresponding to the decay rate cumulative sum model.

[0075] Specifically, the cycle life of the battery can be determined according to the first capacity fade rate model and the second capacity fade rate model, such as by the following formula: Q SOH = , wherein Q SOH represents the cycle life of the battery, represents the first capacity fade rate model, represents the second capacity fade rate model, so as to realize the prediction of the cycle life of the battery. It can be understood that in the embodiment, the first capacity fade rate model and the second capacity fade rate model have high accuracy, and according to the first capacity fade rate model and the second capacity fade rate model, the prediction accuracy of the cycle life of the battery is improved compared with the prediction according to a single capacity fade rate model.

[0076] In one specific embodiment, the expression of the first capacity fade rate model is as follows:

[0077] (3)

[0078] Where C is the operating charging and discharging current. and These are C-related functions. For example, and It can be a linear function, the first capacity decay rate model:

[0079] (4)

[0080] The expression for the second capacity decay rate model is as follows:

[0081] (5)

[0082] Where t is the storage time, a and b are constants, and the second capacity decay rate model is as follows:

[0083] (6) (7)

[0084] For example, when the battery's operating charge / discharge current is 0.1C, the number of cycles n=1, the charge / discharge time t=20h, and the temperature is 25℃, its cycle life model is shown in Figure 6. The curve in Figure 6 is a schematic diagram of the battery's cycle life when the operating charge / discharge current is 0.1C.

[0085] More specifically, the first expression corresponding to the first capacity decay rate model and the expression corresponding to the second capacity decay rate model are summed, i.e. , This represents the cumulative decay rate model, then based on 100% capacity retention and Q. L The formula is as follows: Q SOH =1-Q L Substitute the operating charge / discharge current into Q SOH The cycle life of the battery can be calculated from this.

[0086] The cycle life prediction method of the battery, by performing the charge-discharge cycle test on the battery to be predicted, obtains the first measured data, and performing the storage cycle test on the battery to obtain the second measured data, by performing the charge-discharge cycle test on the battery to be predicted, and the test current of the charge-discharge cycle test is greater than the current of the battery operating condition, avoids the long test period and poor operability caused by the charge-discharge cycle test of the small current, and compared with the single charge-discharge test or the storage cycle test, is more in line with the actual operating condition of the battery, based on the first measured data and the second measured data, the capacity attenuation rate model of the battery is determined, the prediction of the cycle life attenuation rate of the battery and the prediction of the storage life attenuation rate are realized, the influence of the cycle life attenuation rate and the storage life attenuation rate on the cycle life prediction of the battery is fully considered, the capacity attenuation rate model has high accuracy, and according to the capacity attenuation rate model, compared with the prediction according to the capacity attenuation rate model obtained by single test, the accuracy of the cycle life prediction of the battery is improved.

[0087] As shown in FIG. 7, the embodiment of the application further provides a battery cycle life prediction device 200, the battery cycle life prediction device comprises:

[0088] The test module is configured to perform a charge-discharge cycle test on the battery to be predicted to obtain first measured data, and perform a storage cycle test on the battery to obtain second measured data, and the test current of the charge-discharge cycle test is greater than or equal to the current of the battery operating condition;

[0089] The first determination module is configured to determine a capacity attenuation rate model of the battery based on the first measured data and the second measured data;

[0090] The second determination module is configured to determine the cycle life of the battery based on the capacity attenuation rate model.

[0091] In an embodiment, the first determination module 202 is specifically configured to:

[0092] obtain a capacity retention rate model of the battery based on the first measured data and the second measured data;

[0093] determine the capacity attenuation rate model of the battery based on the capacity retention rate model.

[0094] In an embodiment, the capacity retention rate model comprises a first capacity retention rate model and a second capacity retention rate model, wherein the first capacity retention rate model is a capacity retention rate model comprehensively representing the storage life and the cycle life of the battery, and the second capacity retention rate model is a capacity retention rate model representing the cycle life of the battery; the first determination module 202 is specifically further configured to:

[0095] perform curve fitting based on the first measured data to obtain the first capacity retention rate model of the battery;

[0096] Based on the second measured data, curve fitting is performed to obtain a second capacity retention rate model of the battery.

[0097] In an embodiment, the capacity fade rate model includes a first capacity fade rate model and a second capacity fade rate model, wherein the first capacity fade rate model is a capacity fade rate model representing the cycle life of the battery, and the second capacity fade rate model is a capacity fade rate model representing the storage life of the battery; and the second determination module 203 is specifically configured to:

[0098] determine the second capacity fade rate model according to the second capacity retention rate model;

[0099] According to the first capacity retention rate model and the second capacity retention rate model, the capacity fade rate of the battery in the charge-discharge cycle test process is decomposed to obtain the first capacity fade rate model.

[0100] In an embodiment, the second determination module 203 is specifically further configured to:

[0101] determine the second capacity fade rate model according to the second capacity retention rate model;

[0102] determine a comprehensive capacity fade rate model according to the first capacity retention rate model, the comprehensive capacity fade rate model being a capacity fade rate model comprehensively representing the storage life and the cycle life of the battery;

[0103] According to the second capacity fade rate model, the comprehensive capacity fade rate model is decomposed to obtain the first capacity fade rate model.

[0104] In an embodiment, the test module 201 is specifically configured to:

[0105] set a test temperature and at least two test currents of the battery, wherein the test current is greater than or equal to the current of the operating condition of the battery;

[0106] perform a charge-discharge cycle test on the battery according to the test charge-discharge current at the test temperature to obtain the first measured data.

[0107] In an embodiment, the test module 201 is specifically further configured to:

[0108] set a test temperature and a test state of charge of the battery;

[0109] perform a storage test on the battery according to the test state of charge at the test temperature to obtain the second measured data.

[0110] In an embodiment, the first determination module 202 is specifically further configured to:

[0111] solve the first preset Gaussian degradation equation by using the first measured data to obtain the first capacity retention rate model.

[0112] In an embodiment, the first determining module 202 is further specifically configured to:

[0113] solving the preset second Gaussian degradation equation by using the second measured data to obtain a second capacity retention rate model of the battery.

[0114] In an embodiment, the second determining module 203 is specifically configured to:

[0115] performing summation operation on the first expression corresponding to the first capacity attenuation rate model and the expression corresponding to the second capacity attenuation rate model to obtain a third expression corresponding to the attenuation rate cumulative sum model of the battery;

[0116] determining the cycle life of the battery according to the working charge-discharge current of the battery and the third expression corresponding to the attenuation rate cumulative sum model.

[0117] Embodiments of the present application also provide an electronic device integrating any of the battery cycle life prediction devices provided by embodiments of the present application. The electronic device comprises:

[0118] one or more processors;

[0119] a memory; and

[0120] one or more application programs, wherein the one or more application programs are stored in the memory and are configured to execute the battery cycle life prediction method in any of the battery cycle life prediction method embodiments by the processor.

[0121] Embodiments of the present application also provide an electronic device integrating any of the battery cycle life prediction devices provided by embodiments of the present application. As shown in FIG. 8, it shows the structure schematic diagram of the electronic device related to embodiments of the present application, specifically:

[0122] The electronic device can include 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, etc. Those skilled in the art can understand that the electronic device structure shown in FIG. 8 does not constitute a limitation on the electronic device, and can include more or fewer components than the diagram, or combine certain components, or different component arrangements. Among them:

[0123] The processor 301 is the control center of the electronic device, connects all parts of the electronic device through various interfaces and lines, executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 302 and calling data stored in the memory 302, thereby overall monitoring the electronic device. Optionally, the processor 301 can include one or more processing cores; preferably, the processor 301 can integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 301.

[0124] The memory 302 can be used to store software programs and modules, and the processor 301 executes various functions and data processing by running the software programs and modules stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 302 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 302 can also include a memory controller to provide access for the processor 301 to the memory 302.

[0125] The electronic device also includes a power supply 303 for powering various components, and preferably the power supply 303 can be logically connected to the processor 301 through a power management system, so as to realize functions such as management of charging, discharging and power consumption management through the power management system. The power supply 303 can also include one or more than one direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power supply converter or inverter, a power supply state indicator, etc. Any component.

[0126] The electronic device can 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.

[0127] Although not shown, the electronic device can also include a display unit, etc., which will not be described here. Specifically, in the present embodiment, the processor 301 in the electronic device will load the executable file corresponding to the process of one or more than one application program into the memory 302 according to the following instructions, and run the application program stored in the memory 302 by the processor 301, thereby realizing various functions, as follows:

[0128] The first measured data is obtained by performing a charge-discharge cycle test on the battery to be predicted, and the second measured data is obtained by performing a storage cycle test on the battery, and a test current of the charge-discharge cycle test is greater than or equal to a current of an operating condition of the battery;

[0129] A capacity attenuation rate model of the battery is determined based on the first measured data and the second measured data.

[0130] A cycle life of the battery is determined based on the capacity attenuation rate model.

[0131] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by related hardware controlled by the instructions, which can be stored in a computer readable storage medium and loaded and executed by a processor.

[0132] To this end, the embodiments of the present application provide a computer readable storage medium, which can include a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, etc. A computer program is stored on the storage medium, and the computer program is loaded by a processor to execute the steps in any of the cycle life prediction methods of the battery provided by the embodiments of the present application. For example, the computer program loaded by the processor can execute the following steps:

[0133] The first measured data is obtained by performing a charge-discharge cycle test on the battery to be predicted, and the second measured data is obtained by performing a storage cycle test on the battery, and a test current of the charge-discharge cycle test is greater than or equal to a current of an operating condition of the battery;

[0134] A capacity attenuation rate model of the battery is determined based on the first measured data and the second measured data.

[0135] A cycle life of the battery is determined based on the capacity attenuation rate model.

[0136] The embodiments of the present application also provide a computer program product, which includes a computer program / instruction, and the computer program / instruction is used to execute the steps in any of the cycle life prediction methods of the battery provided by the embodiments of the present application when executed by a processor.

[0137] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the detailed description of other embodiments above, which will not be repeated here.

[0138] In specific implementation, the above units or structures can be implemented as independent entities, or can be combined as the same or several entities, and the specific implementation of the above units or structures can refer to the method embodiments in the foregoing, which will not be described here again.

Claims

1. A method for predicting cycle life of a battery, comprising: performing a charge-discharge cycle test on a battery to be predicted to obtain first measured data, and performing a storage cycle test on the battery to obtain second measured data, wherein a test current of the charge-discharge cycle test is greater than or equal to a current of an operating condition of the battery; determining a capacity fade rate model of the battery based on the first measured data and the second measured data; determining a cycle life of the battery based on the capacity fade rate model.

2. The cycle life prediction method of a battery according to claim 1, wherein The determining of the capacity fade rate model of the battery based on the first measured data and the second measured data comprises: obtaining a capacity retention rate model of the battery based on the first measured data and the second measured data; determining the capacity fade rate model of the battery based on the capacity retention rate model.

3. The cycle life prediction method of a battery according to claim 2, wherein, The capacity retention rate model comprises a first capacity retention rate model and a second capacity retention rate model, wherein the first capacity retention rate model is a capacity retention rate model representing both storage life and cycle life of the battery, and the second capacity retention rate model is a capacity retention rate model representing cycle life of the battery. The obtaining of the capacity retention rate model of the battery based on the first measured data and the second measured data comprises: performing curve fitting based on the first measured data to obtain the first capacity retention rate model of the battery; performing curve fitting based on the second measured data to obtain the second capacity retention rate model of the battery.

4. The cycle life prediction method of a battery according to claim 3, wherein The capacity fade rate model comprises a first capacity fade rate model and a second capacity fade rate model, wherein the first capacity fade rate model is a capacity fade rate model representing cycle life of the battery, and the second capacity fade rate model is a capacity fade rate model representing storage life of the battery. The determining of the capacity fade rate model of the battery based on the capacity retention rate model comprises: determining the second capacity fade rate model according to the second capacity retention rate model; decomposing capacity fade rate of the battery during the charge-discharge cycle test according to the first capacity retention rate model and the second capacity retention rate model to obtain the first capacity fade rate model.

5. The cycle life prediction method of a battery according to claim 4, wherein The decomposing of the capacity fade rate of the battery during the charge-discharge cycle test according to the first capacity retention rate model and the second capacity retention rate model to obtain the first capacity fade rate model comprises: determining the second capacity fade rate model according to the second capacity retention rate model; determining a comprehensive capacity fade rate model according to the first capacity retention rate model, wherein the comprehensive capacity fade rate model is a capacity fade rate model representing both storage life and cycle life of the battery; decomposing the comprehensive capacity fade rate model according to the second capacity fade rate model to obtain the first capacity fade rate model.

6. The cycle life prediction method of a battery according to claim 1, wherein The performing of the charge-discharge cycle test on the battery to be predicted to obtain the first measured data comprises: setting a test temperature and at least two test currents of the battery, wherein the test current is greater than or equal to the current of the operating condition of the battery; The battery is subjected to a charge-discharge cycle test at the test temperature and according to the test current, to obtain the first measured data.

7. The cycle life prediction method of a battery according to claim 1, wherein The storage cycle test on the battery to obtain the second measured data comprises: setting a test temperature and a test state of charge of the battery; The storage cycle test on the battery to obtain the second measured data comprises:

8. The cycle life prediction method of a battery according to claim 3, wherein, The curve fitting based on the first measured data to obtain the first capacity retention rate model of the battery comprises: The first capacity retention rate model is obtained by solving a preset first Gaussian degradation equation using the first measured data.

9. The cycle life prediction method of a battery according to claim 3, wherein, The curve fitting based on the second measured data to obtain the second capacity retention rate model of the battery comprises: The second capacity retention rate model of the battery is obtained by solving a preset second Gaussian degradation equation using the second measured data.

10. The cycle life prediction method of a battery according to claim 4, wherein The cycle life of the battery is determined based on the capacity decay rate model, which comprises: The third expression corresponding to the decay rate cumulative sum model of the battery is obtained by performing a summation operation on the first expression corresponding to the first capacity decay rate model and the expression corresponding to the second capacity decay rate model; The cycle life of the battery is determined according to the working charge-discharge current of the battery and the third expression corresponding to the decay rate cumulative sum model.

11. An electronic device, comprising: one or more processors; a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the cycle life prediction method of the battery of any one of claims 1 to 10.

12. A computer readable storage medium having stored thereon a computer program, the computer program being loaded by a processor to execute the cycle life prediction method of the battery of any one of claims 1 to 10.

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