Battery life prediction method, computing device and computer readable storage medium

Abstract

The invention provides a battery life prediction method, computing equipment and a computer readable storage medium, and the method comprises the steps: obtaining a target SOH corresponding to a target battery in different charging and discharging cycles, and constructing a relation between the target SOH and the charging and discharging cycles; a classic curve is obtained, the classic curve is processed to obtain a reference curve, and the error between the reference curve and the target SOH is minimum; and predicting the service life of the target battery according to the reference curve. Therefore, the service life of the battery can be accurately predicted, and the safety risk of the battery can be better managed.

Description

Technical Field

[0001] The present application belongs to the field of power batteries and computers, and specifically relates to a battery life prediction method, a computing device, and a computer-readable storage medium. Background Art

[0002] In recent years, the new energy industry has flourished. Power batteries (hereinafter referred to as batteries, also known as batteries, power batteries, etc.) as an important component of new energy equipment (such as automobiles, aircraft, etc.) have also received widespread attention, especially the life of power batteries, which has attracted widespread attention.

[0003] Battery life prediction has always been a difficult problem in the industry. Accurate life prediction algorithms are of great significance to battery value estimation and also provide a basis and reference for life regulation.

[0004] Therefore, there is an urgent need for a technical solution for battery life prediction that can accurately predict the battery life in order to better manage battery safety risks. Summary of the invention

[0005] The technical problem to be solved by this application is how to provide a technical solution for battery life prediction that can accurately predict the battery life to better manage the safety risks of the battery.

[0006] In order to solve or improve the above-mentioned technical problems to a certain extent, according to the first aspect of the present application, a method for predicting battery life is provided, the method comprising: obtaining a target SOH corresponding to a target battery in different charge and discharge cycles, and constructing a relationship between the target SOH and the charge and discharge cycle; obtaining a classic curve, and processing the classic curve to obtain a reference curve, wherein the error between the reference curve and the target SOH is minimized; and predicting the life of the target battery according to the reference curve.

[0007] In an optional embodiment, the processing the classic curve to obtain the reference curve includes: rotating and / or scaling the classic curve to obtain the reference curve.

[0008] In an optional embodiment, constructing the relationship between the target SOH and the charge and discharge cycle includes: drawing a target curve of the target SOH changing with the charge and discharge cycle; the error between the reference curve and the target SOH is minimized, including: the shape difference between part or all of the reference curve and the target curve is minimized.

[0009] In an optional embodiment, the classic curve is generated based on the following steps: performing a cycle aging experiment on a sample battery; determining a reference SOH of the sample battery every preset number of charge and discharge cycles; and plotting a curve of the reference SOH changing with the charge and discharge cycles to obtain the classic curve.

[0010] In an optional embodiment, obtaining the target SOH corresponding to the target battery includes: under preset operating conditions, calculating the target SOH corresponding to the target battery by an incremental capacity analysis algorithm and / or a differential voltage analysis algorithm.

[0011] In an optional embodiment, the preset operating condition satisfies at least one of the following conditions: the charging current value is less than a first preset value; the fluctuation of the charging current value is less than a second preset value; the charging starting SOC of the target battery is less than a third preset value; the temperature of the target battery satisfies a preset temperature range.

[0012] In an optional embodiment, predicting the life of the target battery based on the reference curve includes: obtaining a target number of charge and discharge cycles corresponding to the SOH of the target battery when it is retired; and using the target number as the life of the target battery.

[0013] In an optional embodiment, predicting the life of the target battery based on the reference curve further includes: determining the correspondence between the charge and discharge cycles and time based on the usage history of the target battery; and based on the correspondence, taking the time corresponding to the target quantity as the life of the target battery.

[0014] According to a second aspect of the present application, a computing device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, it is capable of implementing any step of the battery life prediction method.

[0015] According to a third aspect of the present application, a computer-readable storage medium is provided for storing a computer program, wherein the program, when executed by a computer or a processor, implements the steps of any one of the battery life prediction methods.

[0016] Compared with the prior art, the present application has obvious advantages and beneficial effects. By means of the above technical solution, the battery life prediction method, computing device, and storage medium of the present application can achieve considerable technical advancement and practicality, and have wide industrial utilization value, and have at least the following advantages:

[0017] The present application provides a method for predicting battery life, a computing device, and a computer-readable storage medium. Without disassembling the target battery, it is only necessary to obtain the relationship between multiple target SOHs and the charge-discharge cycle, and compare it with the reference curve to accurately predict the life of the target battery. Among them, the classic curve is drawn by experimenting on batteries of the same type as the target battery, and the reference curve is obtained by processing the classic curve. This is because the target battery will experience more complex working conditions than the laboratory during normal use. If the classic curve is used directly, it may not be able to adapt to the operating conditions of the target battery. If the life of the target battery is to be accurately predicted, the classic curve needs to be processed.

[0018] Furthermore, by conducting cycle aging experiments on sample batteries of the same type as the target battery, the classic curve can be accurately drawn for subsequent battery life prediction.

[0019] Furthermore, the reference curve can be used to determine the charge and discharge cycles that the target battery can go through before it is retired, thereby estimating the life of the target battery. Furthermore, the target battery can be managed throughout its life cycle based on the predicted battery life.

[0020] Furthermore, the time corresponding to the life of the target battery can be estimated, and the target battery can be managed throughout its life cycle based on the predicted battery life.

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specifically cites the preferred embodiments and describes them in detail with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a method for predicting battery life according to an embodiment of the present application is shown;

[0023] Figure 2 A schematic diagram of a flow chart of a method for generating a classic curve according to an embodiment of the present application;

[0024] Figure 3 A schematic diagram of the structure of a battery life prediction device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] As mentioned in the background technology, the safety and life of power batteries are their core indicators. High-safety and long-life batteries are beneficial to extending the service life of vehicles, reducing the risk of battery thermal runaway, and reducing users' anxiety about vehicle costs and safety.

[0026] However, there is no effective battery life prediction strategy in the existing technology, which makes it impossible to accurately predict battery life and thus unable to effectively manage battery safety.

[0027] To solve this problem, the present application provides a battery life prediction method, a computing device, and a computer-readable storage medium.

[0028] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined application purpose, the specific implementation methods and effects of the battery life prediction method, computing device, and computer-readable storage medium proposed in the present application are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0029] It should be noted that the battery in the present application is a power battery (or battery pack, power battery pack), and the battery is an energy system that provides power for new energy equipment such as electric vehicles and aircraft.

[0030] In the first aspect of this application, see Figure 1 As shown, Figure 1 This is a flow chart of a first method for predicting battery life provided by the present application. The first method for predicting battery life may include the following steps S101 and S102, each of which is described in detail as follows.

[0031] Step S101 , obtaining a target SOH (State Of Health) corresponding to a target battery at different charge and discharge cycles, and constructing a relationship between the target SOH and the charge and discharge cycle.

[0032] Among them, for the target battery, it will experience multiple charge and discharge cycles during its use. The process from fully charging to fully discharging the target battery is called a discharge cycle (cycle), and the process from fully discharging to fully charging the target battery is called a charge cycle (cycle). A charge and discharge cycle refers to a charge cycle and / or a discharge cycle.

[0033] During the use of the target battery, the SOH of the target battery is calculated every several charge and discharge cycles. It should be noted that the SOH of the target battery can be calculated according to a variety of existing SOH calculation methods.

[0034] For example, under preset operating conditions, the target SOH corresponding to the target battery may be calculated by an incremental capacity analysis (ICA) algorithm and / or a differential voltage analysis (DVA) algorithm.

[0035] Among them, the preset working condition can be set according to requirements. In a specific example, in order to ensure the accuracy of SOH calculation, the preset working condition can be set to meet at least one of the following conditions: the charging current value is less than the first preset value, that is, the slow charging condition; the fluctuation of the charging current value is less than the second preset value, that is, the charging current is relatively warm; the charging starting SOC of the target battery is less than the third preset value, that is, the starting SOC is low; the temperature of the target battery meets the preset temperature range, that is, the temperature is relatively warm and will not be too low or too high.

[0036] Step S102: Acquire a classic curve, and process the classic curve to obtain a reference curve, wherein the error between the reference curve and the multiple target SOHs is minimal.

[0037] Among them, the classic curve is one or more pre-constructed curves, and the classic curve is used to reflect the changes of SOH of batteries of the same type as the target battery with charge and discharge cycles.

[0038] In the first optional processing method, the processing of the classic curve includes: rotating the classic curve. For some scenes, rotating the classic curve can quickly find an accurate reference curve, but the rotation requires complex calculations on the mathematical expression corresponding to the classic curve, which is computationally intensive.

[0039] In a second optional processing method, the processing of the classic curve includes: scaling the classic curve. This is a preferred method of the present application, because scaling the curve only requires multiplying the mathematical expression corresponding to the curve by a coefficient, which can effectively reduce the amount of calculation. If it is necessary to predict the life of a large number of batteries, the prediction efficiency can be effectively improved.

[0040] In a third optional processing method, the processing of the classic curve includes: rotating and scaling the classic curve. This method can obtain a more accurate reference curve and can effectively improve the accuracy of life prediction, but the disadvantage is that the amount of calculation is large.

[0041] Step S103: predicting the life of the target battery according to the reference curve.

[0042] pass Figure 1The provided battery life prediction method only needs to obtain the relationship between multiple target SOHs and the charge-discharge cycles without disassembling the target battery, and compare it with the reference curve to accurately predict the life of the target battery. The classic curve is drawn by experimenting with batteries of the same type as the target battery, and the reference curve is obtained by processing the classic curve. This is because the target battery will experience more complex working conditions than the laboratory during normal use. If the classic curve is used directly, it may not be able to adapt to the operating conditions of the target battery. If the life of the target battery is to be accurately predicted, the classic curve needs to be processed.

[0043] In an alternative embodiment, see Figure 2 , Figure 2 A schematic flow chart of a method for generating a classic curve is provided, wherein the classic curve is generated based on the following steps:

[0044] Step S201, performing a cycle aging test on a sample battery.

[0045] Among them, the aging experiment can also be called a "degradation experiment", which is to conduct repeated charge-discharge-charge... experiments on sample batteries in the laboratory to observe the degradation of the sample batteries.

[0046] The sample battery is one or more batteries of the same type as the target battery. In order to ensure the accuracy of the experimental results, several more sample batteries are generally prepared. The same type means that if the target battery is a lithium iron phosphate battery, the sample battery should also be a lithium iron phosphate battery. Or, if the target battery is a cylindrical battery of a ternary system, the sample battery should also be a cylindrical battery of a ternary system. This ensures that the classic curve obtained by the cycle aging experiment on the sample battery can reflect the characteristics of the target battery.

[0047] Step S202 : determining a reference SOH of the sample battery every preset number of charge and discharge cycles.

[0048] The preset number can be set as needed, such as determining a reference SOH every 50 or 100 cycles. It should be noted that the preset number can be adjusted when performing a cycle aging experiment on the same sample battery.

[0049] Optionally, the reference SOH of the sample battery may be determined by constant capacity of the sample battery without disassembling the sample battery.

[0050] Optionally, the reference SOH of the sample battery may be determined by disassembling the sample battery and detecting the conditions of the active ingredients in the sample battery.

[0051] It should be noted that the methods for determining the SOH of the sample battery include but are not limited to the above examples, and other commonly used SOH determination methods are within the protection scope of this application.

[0052] Step S203 , plotting a curve of the reference SOH changing with the charge-discharge cycle to obtain the classic curve.

[0053] That is, after obtaining the reference SOH corresponding to a plurality of charge and discharge cycles, a classic curve is drawn to reflect the relationship between the reference SOH and the charge and discharge cycles.

[0054] In this embodiment, by performing a cycle aging experiment on sample batteries of the same type as the target battery, a classic curve can be accurately drawn for subsequent battery life prediction.

[0055] In an optional embodiment, when constructing the relationship between the target SOH and the charge and discharge cycle for step S101, for different charge and discharge cycles, the target SOH corresponding to each charge and discharge cycle can be obtained and saved as a two-dimensional dot matrix, and each point in the dot matrix can be expressed as (cycle, SOH), where cycle represents the charge and discharge cycle, and SOH represents the target SOH.

[0056] Correspondingly, the error between the reference curve and the multiple target SOHs is the smallest, and it can be considered that there are multiple points in the reference curve, and the errors between the multiple points and the multiple (cycle, SOH) points are the smallest. For example, the sum of the differences (or the absolute values ​​of the differences) between the multiple points and the multiple (cycle, SOH) points is the smallest; or the variance or standard deviation of the differences between the multiple points and the multiple (cycle, SOH) points is the smallest; or the mean of the sum of the differences (or the absolute values ​​of the differences) between the multiple points and the multiple (cycle, SOH) points is the smallest.

[0057] In this embodiment, a method for determining the minimum error between the curve and the dot matrix is ​​provided.

[0058] In another optional embodiment, when constructing the relationship between the target SOH and the charge-discharge cycle in step S101, a target curve of the target SOH changing with the charge-discharge cycle can be drawn. That is, after obtaining the target SOH corresponding to each charge-discharge cycle, a relationship curve between multiple target SOHs of the battery and the corresponding charge-discharge cycles is drawn with each point in the dot matrix as the target curve.

[0059] In one case, the minimum error between the reference curve and the multiple target SOHs in step S102 may include: the shape difference between a part or all of the reference curve and the target curve is minimum.

[0060] Specifically, the reference curve may refer to a curve reflecting the full life cycle of a sample battery corresponding to a target battery. The target battery often only experiences a portion of its life cycle, and a partial area of ​​the reference curve may be used for comparison.

[0061] In this embodiment, constructing a target curve can more comprehensively reflect the relationship between the target battery SOH and the charge and discharge cycle changes. When comparing the two curves, the difference in their shapes can be minimized, thereby being able to reflect the subsequent changes in the target battery SOH through a reference curve that is closest in shape to the target curve.

[0062] In another case, the minimum error between the reference curve and the multiple target SOHs in step S102 may include: the minimum error between a part or all of the reference curve and the target curve.

[0063] Specifically, the method for determining the minimum error between a partial or entire area of ​​the reference curve and the target curve can be specifically referred to the method for determining the minimum error between the above-mentioned curve and the dot matrix, which will not be repeated here.

[0064] In another case, the minimum error between the reference curve and the multiple target SOHs in step S102 may include: the shape difference between a part or all of the reference curve and the target curve is minimum, and the error between a part or all of the reference curve and the target curve is minimum. The specific determination method is referred to the above embodiment and will not be repeated here.

[0065] In an alternative embodiment, see again Figure 1 The step S103 of predicting the life of the target battery according to the reference curve may include: obtaining a target number of charge and discharge cycles corresponding to the SOH of the target battery when it is retired; and using the target number as the life of the target battery.

[0066] After determining the reference curve, the charge and discharge cycle corresponding to when the target battery meets the retirement conditions (such as SOH is 70%) can be determined based on the subsequent development of the reference curve, and then it can be determined how many charge and discharge cycles the target battery needs to go through to meet the retirement conditions.

[0067] In this embodiment, the charge and discharge cycles that the target battery can experience before retirement can be determined based on the reference curve, thereby estimating the life of the target battery. Furthermore, the target battery can also be managed throughout its life cycle based on the predicted battery life.

[0068] In an alternative embodiment, see again Figure 1The step S103 of predicting the life of the target battery according to the reference curve may also include: determining the correspondence between the charge and discharge cycles and time according to the usage history of the target battery; and according to the correspondence, taking the time corresponding to the target number as the life of the target battery.

[0069] Specifically, the usage history of the target battery may include the charge and discharge cycles experienced by the target battery in the past period of time. When devices such as electric vehicles are powered by batteries, the usage history of the battery usually varies with the user's habits of using the electric vehicle.

[0070] Furthermore, a user profile of each user can be built based on the usage history to reflect the user's usage habits. After obtaining the target number of charge and discharge cycles corresponding to the SOH of the target battery at retirement, the time required to complete the target number of charge and discharge cycles can be predicted based on the user profile, thereby being able to estimate the time corresponding to the life of the target battery.

[0071] In this embodiment, the time corresponding to the life of the target battery can be estimated. Furthermore, the target battery can be managed throughout its life cycle based on the predicted battery life.

[0072] In an optional embodiment, the battery life prediction method provided in the present application can be executed by the target battery, specifically by the battery management controller (Battery Management System, referred to as BMS) at the target battery end, which can also be called a battery management unit (Battery Management Unit, referred to as BMU). Furthermore, the classic curve can be stored in the memory of the BMS, and when executing the method described in the present application, the classic curve is obtained for comparison.

[0073] In another optional embodiment, the battery life prediction method provided in the present application can be executed by a server, wherein the server can refer to a cloud server. The server can obtain the SOH of multiple target batteries under different charge and discharge cycles, thereby predicting the life of each target battery, thereby determining whether each target battery is about to be retired. For batteries that are about to be retired, the server can communicate with the battery or the device of the battery to prompt the user that the battery is about to be retired, and please send it to the recycling site or the original manufacturer in time for battery replacement. This is to avoid safety problems caused by the continued use of retired batteries. Therefore, the cloud can uniformly manage a large number of batteries based on the battery life prediction method, and based on communication with the battery end or the device end, prompt battery replacement to reduce battery safety risks.

[0074] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0075] In the second aspect of this application, based on the same application concept, see Figure 3 The embodiment of the present application further provides a battery life prediction device 30, which may include:

[0076] A target SOH acquisition module 301 is used to acquire a target SOH corresponding to a target battery in different charge and discharge cycles, and to construct a relationship between the target SOH and the charge and discharge cycle;

[0077] A reference curve acquisition module 302 is used to acquire a classic curve and process the classic curve to obtain a reference curve, wherein the error between the reference curve and the target SOH is minimal;

[0078] The life prediction module 303 is used to predict the life of the target battery according to the reference curve.

[0079] In an optional embodiment, the reference curve acquisition module 302 is further configured to rotate and / or scale the classic curve to obtain the reference curve.

[0080] In an optional embodiment, the target SOH acquisition module 301 is also used to draw a target curve in which the target SOH changes with the charge and discharge cycle, and the reference curve acquisition module 302 is also used to minimize the shape difference between part or all of the reference curve and the target curve; and / or, to minimize the error between part or all of the reference curve and the target curve.

[0081] In an optional embodiment, the battery life prediction device 30 may further include a classic curve generation module, which specifically includes:

[0082] An experimental unit, used to perform cycle aging experiments on sample batteries;

[0083] A reference SOH acquisition unit, used to determine the reference SOH of the sample battery every preset number of charge and discharge cycles;

[0084] The classic curve acquisition unit is used to draw a curve showing the reference SOH changing with the charge-discharge cycle to obtain the classic curve.

[0085] In an optional embodiment, the target SOH acquisition module 301 may also be used to: under a preset operating condition, calculate the target SOH corresponding to the target battery by using an incremental capacity analysis algorithm and / or a differential voltage analysis algorithm.

[0086] Optionally, the preset operating conditions satisfy at least one of the following conditions: the charging current value is less than a first preset value; the fluctuation of the charging current value is less than a second preset value; the charging starting SOC of the target battery is less than a third preset value; the temperature of the target battery satisfies a preset temperature range.

[0087] In an optional embodiment, the life prediction module 303 can be used to: obtain a target number of charge and discharge cycles corresponding to the SOH of the target battery when it is retired; and use the target number as the life of the target battery.

[0088] In another optional embodiment, the life prediction module 303 can also be used to: determine the correspondence between the charge and discharge cycles and time based on the usage history of the target battery; and based on the correspondence, use the time corresponding to the target quantity as the life of the target battery.

[0089] The specific content of the first battery life prediction device 60 can be found in the related description of the first battery life prediction method, which will not be repeated here.

[0090] According to another embodiment of the present application, a computing device is also provided, which includes a memory and a processor, wherein the memory stores a computer program, and the program, when executed by the processor, can implement the steps of the battery life prediction method in any of the above-mentioned embodiments. The computing device may refer to a new energy device such as a car or an aircraft with a battery, or may refer to a BMS or other controller, or may refer to a server (such as a cloud server) or a charging and swapping device (such as a charging pile, a battery swapping station, etc.).

[0091] The computing device may include a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computing device is used to provide computing and control capabilities. The memory of the computing device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computing device is used to exchange information between the processor and an external device. The communication interface of the computing device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a thermal management control method for a power battery is implemented. The display unit of the computing device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computing device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computing device shell, or an external keyboard, touchpad or mouse.

[0092] According to another embodiment of the present application, a computer-readable storage medium is provided for storing a computer program, wherein the program, when executed by a computer or a processor, implements the steps of the method for predicting battery life in any of the above-mentioned embodiments.

[0093] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0094] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described herein may be implemented as electronic hardware, computer software, or a combination of both.

[0095] In order to show the interchangeability between hardware and software, various schematic components, blocks, modules, circuits and steps are generally described above according to their functionality. Such functionality is implemented in hardware form or software form depending on the specific application and the design restrictions imposed on the overall system. Those skilled in the art can implement the described functionality in a variable manner for specific specific applications, but such implementation decisions should not be understood as causing deviations from the scope of the present application.

[0096] Although only some of the specific embodiments of the present application are described, it should be understood by those skilled in the art that the present application can be implemented in many other forms without departing from its subject matter and scope. Therefore, the examples and embodiments shown are considered to be illustrative rather than restrictive, and the present application may include various modifications and substitutions without departing from the spirit and scope of the present application as defined in the appended claims.

[0097] The above is only a preferred embodiment of the present application and does not constitute any form of limitation to the present application. Although the present application has been disclosed as a preferred embodiment as above, it is not intended to limit the present application. Any technician familiar with the profession can make some changes or modifications to the equivalent embodiments of the above disclosed technical contents without departing from the scope of the technical solution of the present application. However, any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application are still within the scope of the technical solution of the present application.

Claims

1. A method for predicting battery life, characterized in that: The method comprises: At different charge and discharge cycles, obtaining a target SOH corresponding to a target battery, and constructing a relationship between the target SOH and the charge and discharge cycle; Acquire a classic curve, and process the classic curve to obtain a reference curve, wherein the error between the reference curve and the target SOH is minimal; The life of the target battery is predicted according to the reference curve.

2. The method according to claim 1, characterized in that The step of processing the classical curve to obtain a reference curve includes: The reference curve is obtained by rotating and / or scaling the classical curve.

3. The method according to claim 1, characterized in that The constructing the relationship between the target SOH and the charge-discharge cycle includes: Draw a target curve of the target SOH changing with the charge and discharge cycle; The error between the reference curve and the target SOH is minimized, including: The shape difference between a part or all of the reference curve and the target curve is minimal; and / or The error between a partial or entire area of ​​the reference curve and the target curve is minimal.

4. The method according to any one of claims 1 to 3, characterized in that: The classic curve is generated based on the following steps: Conduct cycle aging experiments on sample batteries; Determining a reference SOH of the sample battery every preset number of charge and discharge cycles; The reference SOH is plotted as a function of the charge-discharge cycle to obtain the classic curve.

5. The method according to any one of claims 1 to 3, characterized in that: The obtaining the target SOH corresponding to the target battery includes: Under preset operating conditions, the target SOH corresponding to the target battery is calculated by an incremental capacity analysis algorithm and / or a differential voltage analysis algorithm.

6. The method according to claim 5, characterized in that The preset operating condition satisfies at least one of the following conditions: the charging current value is less than a first preset value; the fluctuation of the charging current value is less than a second preset value; the charging starting SOC of the target battery is less than a third preset value; the temperature of the target battery satisfies a preset temperature range.

7. The method according to claim 1, characterized in that The predicting the life of the target battery according to the reference curve includes: Obtain a target number of charge and discharge cycles corresponding to the SOH of the target battery when it is retired; The target number is used as the target battery life.

8. The method according to claim 7, characterized in that The predicting the life of the target battery according to the reference curve further includes: Determining the correspondence between the charge and discharge cycle and time according to the usage history of the target battery; According to the corresponding relationship, the time corresponding to the target quantity is used as the target battery life.

9. A computing device comprising a memory and a processor, characterized in that: The memory stores a computer program, which, when executed by the processor, can implement the steps of the battery life prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium for storing a computer program, characterized in that: When the program is executed by a computer or a processor, the program implements the steps of the battery life prediction method according to any one of claims 1 to 8.

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