A battery life prediction method based on rain flow counting method and application

Abstract

The application discloses a battery life prediction method based on a rainflow counting method, comprising the following steps: obtaining the corresponding relationship between the remaining capacity and the working time of a measured battery under different temperatures, and obtaining a temperature acceleration coefficient of the measured battery according to the corresponding relationship; obtaining the expected life of the measured battery under different temperatures according to the temperature acceleration coefficient based on the theoretical life of the measured battery under a preset temperature; adopting the rainflow counting method to count the temperature history of the measured battery, and obtaining the cumulative damage degree of the measured battery after the temperature history according to the linear damage accumulation principle; and obtaining the actual life of the measured battery after the temperature history according to the cumulative damage degree and the expected life of the measured battery under different temperatures. The method can solve the problems that the existing lithium battery life prediction method is complicated in calculation, the influence of the temperature history on the battery life is not considered, the prediction precision is not high, and the engineering use needs cannot be met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power batteries, and more particularly to a battery life prediction method based on rain flow counting, a battery life prediction device based on rain flow counting, an electronic device, and a computer-readable storage medium. BACKGROUND

[0002] Lithium-ion batteries, as a new generation of green energy, are widely used in electric vehicles, energy storage systems, aerospace and civilian fields due to their advantages in cycle life, volume and cost. With the continuous application of lithium-ion batteries in human life, the problem of life is increasingly prominent. During use, a series of physical and chemical changes occur inside the battery, which will cause its performance and capacity to show a declining trend, and changes in the external environment will also affect its health status, ultimately leading to the end of the battery's life. As an important part of the system, the damage of the battery will cause the entire system to malfunction, causing property loss and even casualties, which will hinder its commercial application. Therefore, timely prediction of the battery life and taking appropriate measures can avoid unnecessary trouble and ensure the safe and reliable operation of the battery and the system. Therefore, during the use of the battery, timely mastery of the battery life can effectively guide the operation and maintenance of the battery, replace the faulty battery, and ensure the normal operation of the battery pack.

[0003] At present, the models used to predict the life of lithium batteries are generally divided into two categories: one is a physical model obtained by abstracting and mathematically analyzing the object being studied, which reflects the actual operation mechanism of the system; the other is a regression data model obtained by analyzing and studying the relevant parameters of the system being studied. Due to the special structure of lithium batteries and the very complex internal electrochemical reaction, it is difficult to directly measure the relevant parameters, so it is difficult to establish an accurate physical model or mathematical model for lithium batteries, so the model-based prediction method is not suitable for the field of lithium battery remaining life prediction, and the data-driven prediction method is widely used at present, including: artificial neural network (ANN), support vector machine (SVM), correlation vector machine, particle filter, etc. However, the above-mentioned lithium battery life prediction method is complex to calculate and does not consider the influence of temperature history on battery life, so the prediction accuracy is not high and cannot meet the needs of engineering use. SUMMARY

[0004] In view of at least one defect or improvement demand of the prior art, the present application provides a battery life prediction method and application based on rain flow counting, aiming to solve the problem that the existing lithium battery life prediction method is complex to calculate and does not consider the influence of temperature history on battery life, thus the prediction accuracy is not high and cannot meet the needs of engineering use.

[0005] To achieve the above object, according to a first aspect of the present application, a battery life prediction method based on rainflow counting method is provided, comprising: obtaining a corresponding relationship between battery residual capacity and battery working time of a measured battery at different temperatures, and obtaining a temperature acceleration coefficient of the measured battery according to the corresponding relationship; obtaining expected life of the measured battery at different temperatures according to the temperature acceleration coefficient based on theoretical life of the measured battery at a preset temperature; obtaining cumulative damage degree of the measured battery after a temperature history by using rainflow counting method and according to linear damage accumulation principle; and obtaining actual life of the measured battery after the temperature history according to the cumulative damage degree and the expected life of the measured battery at different temperatures.

[0006] In an embodiment of the present application, the corresponding relationship between the battery residual capacity and the battery working time is represented as: ln[Q(t)]=ln(a)+z×ln(t); wherein Q is the battery residual capacity, a is a battery material coefficient, z is a temperature attenuation coefficient, and t is the battery working time.

[0007] In an embodiment of the present application, the temperature acceleration coefficient satisfies the equation: K=Ae -Ea / RT ; wherein K is the temperature acceleration coefficient, R is a molar gas constant, T is a thermodynamic temperature, Ea is an apparent activation energy, and A is a frequency factor.

[0008] In an embodiment of the present application, the expected life of the measured battery at different temperatures is represented as: Li=L1×K1 / Ki; wherein Li is the expected life at different temperatures Ti, K1 is the temperature acceleration coefficient at the preset temperature, and Ki is the temperature acceleration coefficient at different temperatures Ti.

[0009] In an embodiment of the present application, the cumulative damage degree of the measured battery after the temperature history is represented as: D=∑ti / Li; and the actual life of the measured battery after the temperature history is represented as: L=(1-D)Li; wherein ti is the battery working time at different temperatures Ti.

[0010] In another aspect, the application provides a battery life prediction device based on rainflow counting method, comprising: a temperature acceleration coefficient obtaining module, configured to obtain a correspondence between battery residual capacity and battery working time of a measured battery at different temperatures, and obtain a temperature acceleration coefficient of the measured battery according to the correspondence; an expected life obtaining module, configured to obtain expected lives of the measured battery at different temperatures based on a theoretical life of the measured battery at a preset temperature and according to the temperature acceleration coefficient; a cumulative damage degree obtaining module, configured to count a temperature history of the measured battery by using rainflow counting method, and obtain a cumulative damage degree of the measured battery after the temperature history according to a linear damage accumulation principle; and an actual life obtaining module, configured to obtain an actual life of the measured battery after the temperature history according to the cumulative damage degree and the expected lives of the measured battery at different temperatures.

[0011] In one embodiment of the application, the expected life obtaining module is specifically configured to express the expected lives of the measured battery at different temperatures as Li=L1×K1 / Ki, where Li is the expected life at different temperature Ti, K1 is the temperature acceleration coefficient at the preset temperature, and Ki is the temperature acceleration coefficient at different temperature Ti.

[0012] In one embodiment of the application, the cumulative damage degree obtaining module is specifically configured to express the cumulative damage degree of the measured battery after the temperature history as D=∑ti / Li, and the actual life obtaining module is specifically configured to express the actual life of the measured battery after the temperature history as L=(1-D)Li, where ti is the battery working time at different temperature Ti.

[0013] According to a third aspect of the application, there is also provided an electronic device comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program which, when executed by the processing unit, causes the processing unit to perform the steps of the method according to any one of the above embodiments.

[0014] According to a fourth aspect of the application, there is also provided a computer readable storage medium storing a computer program executable by an access authentication device, which, when executed on the access authentication device, causes the access authentication device to perform the steps of the method according to any one of the above embodiments.

[0015] In general, the above technical solutions conceived by the application can at least achieve the following beneficial effects compared with the prior art:

[0016] The battery life prediction method based on the rain flow counting method provided by the intellectual achievement is different from the existing lithium battery life prediction method, and the problems of complex calculation and low precision are solved, the technical scheme is based on the theoretical life data of the battery at a fixed preset temperature and the temperature history of the battery in use, and the rain flow counting method is combined with the linear damage theory to predict the life of the battery, the calculation is simple, the real-time performance is good, the influence of the temperature history of the battery on the life is fully considered, the prediction precision is high, and the method has good engineering applicability. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 The flowchart of the battery life prediction method based on the rain flow counting method provided by the embodiments of the present application is provided.

[0019] Figure 2 The corresponding relationship diagram of the battery residual capacity and the battery working time at different temperatures provided by the embodiments of the present application is provided.

[0020] Figure 3 The temperature acceleration coefficient fitting curve diagram at different temperatures provided by the embodiments of the present application is provided.

[0021] Figure 4 The schematic diagram of the temperature history of the battery in use period is provided by the embodiments of the present application.

[0022] Figure 5 The schematic diagram of the load stress of the battery changing with time is provided by the embodiments of the present application.

[0023] Figure 6 The structural schematic diagram of the battery life prediction device based on the rain flow counting method provided by the embodiments of the present application is provided.

[0024] Figure 7 The structural schematic diagram of the electronic device provided by the embodiments of the present application is provided.

[0025] Figure 8 The structural schematic diagram of the computer readable storage medium provided by the embodiments of the present application is provided. DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0027] The terms "first", "second", "third" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish different objects and are not used to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed or can optionally include other steps or units inherent to the process, method, product or device.

[0028] As shown in Figure 1 The first embodiment of the present application proposes a battery life prediction method based on rainflow counting method, for example, comprising: step S1, obtaining the corresponding relationship between the remaining capacity of the measured battery at different temperatures and the battery working time, and obtaining the temperature acceleration coefficient of the measured battery according to the corresponding relationship; step S2, based on the theoretical life of the measured battery at a preset temperature, obtaining the expected life of the measured battery at different temperatures according to the temperature acceleration coefficient; step S3, using rainflow counting method to count the temperature history of the measured battery, and obtaining the cumulative damage degree of the measured battery after the temperature history according to the linear damage accumulation principle; step S4, obtaining the actual life of the measured battery after the temperature history according to the cumulative damage degree and the expected life of the measured battery at different temperatures.

[0029] In step S1, the remaining capacity of the battery is generally determined by the material system of the battery and the use time, i.e. the following formula:

[0030] Q(t)=a×t z

[0031] Taking the logarithm, the corresponding relationship between the remaining capacity of the battery and the battery working time is obtained:

[0032] ln[W(t)]=ln(a)+z×ln(t)

[0033] Wherein, Q is the remaining capacity of the battery, a is the battery material coefficient, which is affected by the battery chemical system, z is the temperature attenuation coefficient, which is determined by the design system, and t is the battery working time. As shown in Figure 2 The relationship between ln[Q(t)] and ln(t) will follow a linear equation, and the slope is z.

[0034] In step S2, as shown in Figure 3 , for example, the remaining capacity of different models of lithium batteries is tested at different temperatures, and the battery reaches the end of life when the remaining capacity is reduced to 80% of the rated capacity. The fitting curve of the temperature acceleration coefficient (the ratio of the battery life at different temperatures to the life base value) at different temperatures can be obtained.

[0035] The life base value mentioned is the theoretical life of the battery at a predetermined temperature. For example, based on the life of the battery at 25 degrees Celsius, the lithium battery life decay conforms to the Arrhenius equation according to the temperature acceleration coefficient fitting curve, that is, the life is reduced by half when the temperature is increased by a certain value. As shown in Figure 3 , the battery life is reduced by half when the temperature is increased by about 9 degrees.

[0036] The temperature acceleration coefficient K satisfies the Arrhenius equation:

[0037] K=Ae -Ea / RT

[0038] Where K is the temperature acceleration coefficient, R is the molar gas constant, T is the thermodynamic temperature, Ea is the apparent activation energy, and A is the frequency factor.

[0039] Further, assuming that the life of the battery provided by the battery manufacturer at a predetermined temperature is L1, the expected life of the measured battery at different temperatures is represented as: Li=L1×K1 / Ki; Where LI is the expected life at different temperatures Ti, K1 is the temperature acceleration coefficient at the predetermined temperature, and Ki is the temperature acceleration coefficient at different temperatures Ti. The predetermined temperature can be room temperature T1=25 degrees Celsius, or other temperatures, which are not limited by the present application.

[0040] In step S3, since the life of the battery at different temperatures is different, the temperature of the battery is changing in the actual application environment, therefore, in order to accurately calculate the life of the battery after experiencing different temperature processes, for example, the temperature process of the battery life is counted by rain flow counting, and the temperature change curve with time is shown in Figure 4 .

[0041] Due to the irregular distribution of temperature curve, for example, the temperature is counted by statistical counting method, the time under different temperature is obtained. In actual working condition, the load applied on the material structure is not a standard constant amplitude load. The process of converting the random load applied on the material structure into a plurality of constant amplitude loads with different amplitudes is called "compiling load spectrum". The process of converting the load-time curve in actual engineering into a plurality of complete cycles is called "statistical counting method". In order to measure the damage accumulation degree of the load under complex working condition on the material structure, the amplitude and the cycle number under different amplitudes of the random load or stress can be counted based on the statistical counting method, and the statistical results are used to form the load spectrum, so as to evaluate the damage accumulation degree or predict the service life.

[0042] As shown in Figure 5 Rainflow cycle counting method is also called "pagoda roof method" because after rotating the load-time curve by 90°, the time axis becomes the vertical axis, and the load stress is a series of roofs falling in the rain. The peak value of load stress is located on the right side, and the valley value is located on the left side. Before counting by using rainflow counting method, a series of arrangements need to be made for the temperature change curve. Rainflow counting method first needs to compress the data, remove the repeated values in the data, and extract the peak and valley values in the data; therefore, other points in the temperature change curve need to be discarded. In addition, the temperature with small amplitude also needs to be discarded because it has little effect on the service life. Thus, the time under different temperature Ti is ti.

[0043] Further, based on the time under different temperature, the influence of temperature history on the service life of the battery is calculated by using the miner linear damage accumulation principle, and the damage degree under different temperature is analyzed by using the preset lithium battery life model to calculate the service life value of the lithium battery.

[0044] Specifically, fatigue refers to the process that one or more points in the material structure are subjected to repeated stress, and cracks or fractures are generated in the material structure after the number of periodic disturbances reaches a certain degree, resulting in permanent changes. In the process from when the material structure is subjected to stress to when it is completely broken, the cycle number or the required time of the process is called the "service life" of the material structure. Miner linear damage accumulation principle considers that the fatigue damage caused by different stresses can be linearly superimposed, and when the superimposed damage value reaches the limit, the material structure is considered to be damaged. Variable amplitude load can be considered as being composed of constant loads with different amplitudes. If the cycle number of the material structure under a certain constant amplitude stress is ni, and the cycle number required for fatigue failure is Ni, then the cumulative damage degree under the load is defined as:

[0045] Di=ni / Ni

[0046] Suppose that in a certain fatigue damage process, the material structure is subjected to k stresses Si (k = 1, 2..k), and the cycle times are ni (k = 1, 2..k), respectively, and the total cumulative damage degree is obtained as:

[0047] D = ∑Di = ∑ni / Ni i = 1, 2..k

[0048] It can be obtained that the cumulative damage degree is independent of the order of stress action, when D = ∑Di = 1, the material structure is considered to be fatigue failure.

[0049] According to the rainflow counting method, the battery usage time at different temperatures Ti is ti, and the battery life at different temperatures is Li, and the cumulative damage degree of the battery after experiencing different temperature processes is:

[0050] D = ∑ti / Li

[0051] Therefore, the lithium battery life is obtained as:

[0052] L = (1-D)Li

[0053] When D is close to 1, the battery failure can be determined.

[0054] In summary, the first embodiment of the present application proposes a battery life prediction method based on the rainflow counting method, which is different from the existing lithium battery life prediction method, and the technical scheme solves the problems of complex calculation and low precision, and according to the theoretical life data of the battery at a fixed preset temperature and the temperature history of the battery in use, the battery life is predicted by combining the rainflow counting method and the linear damage theory, which is simple in calculation, good in real-time performance, fully considers the influence of the battery temperature history on the life, has high prediction precision, and has good engineering applicability.

[0055] In addition, as shown in Figure 6 The second embodiment of the application proposes a battery life prediction device 20 based on the rainflow counting method, for example, including: a temperature acceleration coefficient obtaining module 201, an expected life obtaining module 202, a cumulative damage degree obtaining module 203 and an actual life obtaining module 204.

[0056] The temperature acceleration coefficient acquisition module 201 is used to obtain the correspondence between the remaining capacity and the operating time of the tested battery at different temperatures, and to obtain the temperature acceleration coefficient of the tested battery based on the correspondence. The expected lifespan acquisition module 202 is used to obtain the expected lifespan of the tested battery at different temperatures based on the theoretical lifespan of the tested battery at a preset temperature, according to the temperature acceleration coefficient. The cumulative damage degree acquisition module 203 is used to statistically analyze the temperature history of the tested battery using the rainflow counting method, and to obtain the cumulative damage degree of the tested battery after the temperature history based on the principle of linear damage accumulation. The actual lifespan acquisition module 204 is used to obtain the actual lifespan of the tested battery after the temperature history based on the cumulative damage degree and the expected lifespan of the tested battery at different temperatures.

[0057] Furthermore, the expected lifespan module 202 is specifically used to: express the expected lifespan of the tested battery at different temperatures as: Li=L1×K1 / Ki; where Li is the expected lifespan at different temperatures Ti, K1 is the temperature acceleration coefficient at a preset temperature, and Ki is the temperature acceleration coefficient at different temperatures Ti.

[0058] Furthermore, the cumulative damage degree acquisition module 203 is specifically used to: express the cumulative damage degree of the tested battery after the temperature process as: D=∑ti / Li; the actual lifespan acquisition module 204 is specifically used to: express the actual lifespan of the tested battery after the temperature process as: L=(1-D)Li; where ti is the battery working time at different temperatures Ti.

[0059] It is worth mentioning that the battery life prediction method based on the rain flow counting method implemented by the battery life prediction device 20 based on the rain flow counting method disclosed in the second embodiment of the present invention is as described in the first embodiment above, so it will not be described in detail here.

[0060] Optionally, the modules and other operations or functions in the second embodiment are respectively for implementing the battery life prediction method based on rainflow counting method described in the first embodiment, and the beneficial effects of this embodiment are the same as those of the first embodiment. For the sake of brevity, they will not be repeated here.

[0061] like Figure 7 As shown, the third embodiment of the present invention also provides an electronic device 30, which includes, for example, at least one processing unit 31 and at least one storage unit 32, wherein the storage unit 32 stores a computer program, and when the computer program is executed by the processing unit, the processing unit performs the steps of the method described above, and the beneficial effects of the electronic device 30 provided in this embodiment are the same as the beneficial effects of the battery life prediction method based on rain flow counting provided in the first embodiment.

[0062] likeFigure 8 As shown, the fourth embodiment of the present application also provides a computer readable storage medium 40, which stores a computer program, and the computer program is executed by a processor to implement the steps of the method described above. The computer readable storage medium 40 provided by the embodiment has the same beneficial effects as the battery life prediction method based on the rain flow counting method provided by the first embodiment.

[0063] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0064] 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 related description of other embodiments.

[0065] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some services interfaces, devices or units, and can be electrical or other forms.

[0066] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the embodiment.

[0067] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware, or in the form of software functional unit.

[0068] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned memory includes: a U disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0069] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be instructed by a program to be completed by relevant hardware, and the program can be stored in a computer readable memory, which can include a flash disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0070] The above is only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of embodiments of the present disclosure after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not described in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

[0071] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present disclosure.

[0072] Those skilled in the art readily understand that the above only describes preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A battery life prediction method based on rainflow counting method, characterized by, The method comprises the following steps: obtaining the corresponding relationship between the remaining capacity and the working time of the battery at different temperatures, and obtaining the temperature acceleration coefficient of the battery according to the corresponding relationship; the temperature acceleration coefficient is the ratio of the battery life at different temperatures to the basic life value; obtaining the expected life of the battery at different temperatures according to the temperature acceleration coefficient based on the theoretical life of the battery at a preset temperature; obtaining the cumulative damage degree of the battery after the temperature history according to the linear damage accumulation principle by using the rainflow counting method to count the temperature history of the battery; obtaining the actual life of the battery after the temperature history according to the cumulative damage degree and the expected life of the battery at different temperatures.

2. The rainflow counting method-based battery life prediction method of claim 1, wherein, The corresponding relationship between the battery residual capacity and the battery working time is expressed as: ; wherein, is the battery residual capacity, is the battery material coefficient, z is the temperature attenuation coefficient, and t is the battery working time. 3.The rainflow counting-based battery life prediction method of claim 1, wherein, The temperature acceleration factor satisfies the equation: ; wherein, is the temperature acceleration factor, is the molar gas constant, is the thermodynamic temperature, is the apparent activation energy, is the frequency factor.

4. The rainflow counting method-based battery life prediction method of claim 1, wherein, The expected life of the measured battery at different temperatures is represented as: ; wherein, L1 is the life of the measured battery at a preset temperature, is a temperature acceleration coefficient at the preset temperature, is a temperature acceleration coefficient at different temperatures. is a temperature acceleration coefficient at different temperatures. ​ 5. The rainflow counting method-based battery life prediction method of claim 4, wherein, The cumulative damage degree of the measured battery after the temperature history is represented as: The actual life of the measured battery after the temperature history is represented as: wherein, is the battery operating time at different temperatures .

6. A rainflow counting method-based battery life prediction device characterized by comprising: The method comprises the following steps: a temperature acceleration coefficient obtaining module is configured to obtain the corresponding relationship between the remaining capacity and the working time of the battery at different temperatures, and obtain the temperature acceleration coefficient of the battery according to the corresponding relationship; the temperature acceleration coefficient is the ratio of the battery life at different temperatures to the basic life value; an expected life obtaining module is configured to obtain the expected life of the battery at different temperatures according to the temperature acceleration coefficient based on the theoretical life of the battery at a preset temperature; a cumulative damage degree obtaining module is configured to obtain the cumulative damage degree of the battery after the temperature history according to the linear damage accumulation principle by using the rainflow counting method to count the temperature history of the battery; an actual life obtaining module is configured to obtain the actual life of the battery after the temperature history according to the cumulative damage degree and the expected life of the battery at different temperatures.

7. The rainflow counting method-based battery life prediction device according to claim 6, characterized by, The expected life span obtaining module is specifically configured to represent the expected life span of the measured battery at different temperatures as: ; wherein, L1 is the life span of the measured battery at a preset temperature, is the expected life span at different temperatures, is a temperature acceleration coefficient at the preset temperature, is a temperature acceleration coefficient at different temperatures, .

8. The rainflow counting method-based battery life prediction device according to claim 6, characterized by, The cumulative damage degree obtaining module is specifically configured to represent the cumulative damage degree of the measured battery after the temperature history as: The actual life obtaining module is specifically configured to represent the actual life of the measured battery after the temperature history as: ; wherein, is the battery working time at different temperatures .

9. An electronic device, comprising: The computer program is stored in the storage unit and is executed by the processing unit, so that the processing unit executes the steps of the method according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer program is stored in the storage unit and is executed by the processing unit, so that the processing unit executes the steps of the method according to any one of claims 1-5.

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