Battery life prediction method and related product

Abstract

The invention provides a battery life prediction method and a related product. The battery life prediction method comprises the following steps: acquiring a charge-discharge curve of the charge capacity of a battery along with time change; identifying closed cycles and non-closed cycles in the charge and discharge curve based on a rain flow analysis method, and pairing the identified non-closed cycles to form closed cycles; calculating the equivalent damage degree of each closed cycle, and accumulating the calculated equivalent damage degrees to obtain the total damage degree of the battery; and predicting the remaining life of the battery according to the total damage degree of the battery. The method has the advantages that accurate quantification of irregular charge-discharge cycles in actual working conditions can be realized, and the accuracy of battery life prediction is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to a battery, in particular to a battery life prediction method and related products. BACKGROUND

[0002] In the field of battery life prediction, traditional methods mostly rely on simple cycle number accumulation or empirical models, which are difficult to adapt to complex and variable actual application scenarios. Such methods usually regard the charging and discharging process of the battery as regular complete cycles, ignoring the impact of a large number of irregular cycles in actual working conditions on the life of the battery, resulting in limited prediction accuracy.

[0003] Specifically, in actual use, the battery is often in a partial charging and discharging state, such as "30%→80%→50%→100%" non-complete charging and discharging process, which cannot be directly equivalent to a standard complete cycle in terms of damage to the battery. The traditional method has obvious defects in quantifying the damage of such cycles. At the same time, the damage degree of different discharge depth cycles to the battery is significantly different, and the aging effect of shallow discharge and deep discharge does not have a simple linear relationship, while the traditional model often uses a unified damage coefficient for calculation, which cannot reflect this difference, further reducing the accuracy of life prediction. SUMMARY

[0004] An object of the present application is to provide a battery life prediction method and related products that at least partially solve the above problems.

[0005] A further object of the present application is to accurately quantify irregular charging and discharging cycles in actual working conditions and improve the accuracy of battery life prediction.

[0006] Another further object of the present application is to continuously improve the accuracy of cycle recognition and damage quantification through iterative optimization, providing a more reliable data basis for life prediction.

[0007] In particular, according to a first aspect of the present application, the present application provides a battery life prediction method, comprising:

[0008] obtaining a charging and discharging curve of the state of charge of the battery over time;

[0009] identifying closed cycles and non-closed cycles in the charging and discharging curve based on the rain flow analysis method, and pairing the identified non-closed cycles to form closed cycles;

[0010] calculating the equivalent damage degree of each closed cycle, and accumulating the calculated equivalent damage degrees to obtain the total damage degree of the battery;

[0011] predicting the remaining life of the battery according to the total damage degree of the battery.

[0012] Optionally, the step of obtaining the charge-discharge curve of the battery includes:

[0013] Real-time collecting the charge data of the battery;

[0014] Filtering the collected charge data;

[0015] Generating the charge-discharge curve of the battery based on the filtered charge data.

[0016] Optionally, the step of identifying the closed cycles and the non-closed cycles in the charge-discharge curve based on the rain-flow analysis method includes:

[0017] Extracting all the peak points and valley points from the charge-discharge curve, and arranging them in time sequence to form a peak-valley point sequence;

[0018] Selecting three continuous peak-valley points from the start position of the peak-valley point sequence to form a charge-discharge cycle;

[0019] If the terminal amplitude of the charge-discharge cycle is greater than or equal to the starting amplitude, the charge-discharge cycle is a closed cycle, otherwise it is a non-closed cycle.

[0020] Optionally, the step of pairing the non-closed cycles to form closed cycles includes:

[0021] Storing the identified non-closed cycles;

[0022] According to the peak-valley complement principle, screening the non-closed cycles that can complement each other to form a complete charge-discharge process within a first preset deviation range;

[0023] Pairing the screened non-closed cycles to form closed cycles.

[0024] Optionally, after the step of pairing the non-closed cycles that can complement each other to form a complete charge-discharge process to form closed cycles, the method further includes:

[0025] Every preset time interval or every preset number of non-closed cycles are stored, all stored non-closed cycles are re-paired; and

[0026] The step of re-pairing all stored non-closed cycles includes:

[0027] Screening the non-closed cycles that can complement each other to form a complete charge-discharge process within a second preset deviation range, wherein the second preset deviation range is smaller than the first preset deviation range;

[0028] Pairing the screened non-closed cycles to form closed cycles.

[0029] Optionally, the step of calculating the equivalent damage degree of each of the closed cycles comprises:

[0030] For each of the closed cycles, the depth of discharge and the average state of charge thereof are obtained;

[0031] The equivalent damage degree of each cycle is calculated according to the depth of discharge and the average state of charge.

[0032] Optionally, the step of predicting the remaining life of the battery according to the total damage degree of the battery comprises:

[0033] A mapping relationship model between the total damage degree of the battery and the remaining life is established;

[0034] The total damage degree is input into the mapping relationship model to obtain the remaining life of the battery;

[0035] The remaining life is presented in the form of the remaining number of cycles or the percentage of the remaining available capacity.

[0036] Optionally, after the step of predicting the remaining life of the battery according to the total damage degree of the battery, the method further comprises:

[0037] The maximum depth of discharge of the battery is adjusted according to the predicted remaining life.

[0038] According to a second aspect of the present application, the present application provides a computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of the battery life prediction method according to any one of the preceding aspects.

[0039] According to a second aspect of the present application, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the battery life prediction method according to any one of the preceding aspects.

[0040] The battery life prediction method of the present application realizes accurate quantification of irregular charging and discharging cycles in actual working conditions by obtaining the charging and discharging curve of the state of charge changing with time, identifying closed cycles and non-closed cycles in combination with the rain flow analysis method, pairing non-closed cycles as closed cycles, and predicting the remaining life based on the equivalent damage degree of the closed cycles. Compared with the traditional method which only relies on the number of cycles or the empirical model, this method can effectively capture the damage of part of the charging and discharging and other incomplete cycles, avoiding the error of simply equivalent irregular cycles to complete cycles. At the same time, by distinguishing the damage degrees of different closed cycles, the differential effects of different depths of discharge on battery aging are fully reflected, which significantly improves the accuracy and adaptability of life prediction.

[0041] Further, the battery life prediction method of the present application can realize dynamic optimization of pairing accuracy by re-pairing the non-closed cycles every preset time interval or after accumulating a preset number, and limiting the second preset deviation range to be smaller than the first preset deviation range. The initial pairing allows a larger deviation to quickly form a closed cycle, meeting the real-time requirement; and the subsequent re-pairing with a smaller deviation range can reduce the matching error of non-closed cycles, further reduce the cumulative deviation of damage degree calculation, and continuously improve the accuracy of cycle recognition and damage quantification through iterative optimization, thereby providing a more reliable data basis for life prediction.

[0042] Further, the battery life prediction method of the present application can realize active aging control of the battery use by dynamically adjusting the maximum discharge depth after predicting the remaining life. When the remaining life is low, the degradation rate can be slowed down by reducing the maximum discharge depth, thereby prolonging the actual service time of the battery under the premise of meeting the basic use requirements, balancing the relationship between battery performance and life, and improving the economy and safety of battery use.

[0043] The above and other objects, advantages and features of the present application will become more apparent from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a better understanding of the preferred embodiments, and are not to be considered as restrictive of the application. Moreover, in the drawings, like reference numerals refer to similar components throughout the several views. In the drawings:

[0045] Figure 1 is a schematic flowchart of a battery life prediction method according to an embodiment of the present application;

[0046] Figure 2 is a schematic flowchart of identifying closed cycles and non-closed cycles based on the rain flow analysis method;

[0047] Figure 3 is a schematic diagram of closed cycles and non-closed cycles according to an embodiment of the present application;

[0048] Figure 4 is a schematic diagram of pairing non-closed cycles according to an embodiment of the present application;

[0049] Figure 5 is a schematic diagram of a computer program product according to an embodiment of the present application;

[0050] Figure 6 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present application;

[0051] Figure 7 is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] It should be understood by those skilled in the art that the embodiments described below are only a part of the embodiments of the present application, not all the embodiments of the present application, and are intended to explain the technical principles of the present application, not to limit the protection scope of the present application. Based on the embodiments provided by the present application, all other embodiments obtained by those skilled in the art without creative labor shall fall within the protection scope of the present application.

[0053] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can fetch the instructions from an instruction execution system, apparatus or device and execute the instructions, or in conjunction with these instructions execution systems, apparatus or devices.

[0054] The embodiments of the present application first provide a battery life prediction method, Figure 1 is a schematic flowchart of a battery life prediction method according to an embodiment of the present application, as Figure 1 shown, the battery life prediction method at least includes the following steps S102 to S108.

[0055] Step S102, obtaining a charge-discharge curve of a charge amount of a battery changing with time.

[0056] The charge-discharge curve intuitively reflects the charge-discharge process of the battery in actual use, including the rising (charging) and falling (discharging) trend of the electric quantity, and converts the dynamic electric quantity change of the battery into quantifiable and graphical information for subsequent identification of the charge-discharge cycle and analysis of the damage degree, thereby providing original data support.

[0057] Step S204, identifying closed cycles and non-closed cycles in the charge-discharge curve based on the rain flow analysis method, and pairing the identified non-closed cycles to form closed cycles.

[0058] The core logic of the rain flow analysis method is similar to the flow of rain along a multi-layer roof. The cycle that can form a complete loop is a closed cycle, and the cycle that does not form a complete loop is a non-closed cycle. After identifying the two types of cycles, the non-closed cycles are paired to form closed cycles, thereby preparing for subsequent damage degree calculation.

[0059] Step S106, calculate the equivalent damage degree of each closed cycle, and accumulate the calculated equivalent damage degrees to obtain the total damage degree of the battery.

[0060] The total damage degree is the sum of the damage caused by all closed cycles to the battery during use. Each closed cycle has different damage degrees to the battery due to different parameters such as discharge depth and average state of charge. By calculating the equivalent damage degree of each cycle and accumulating these equivalent damage degrees, the total damage degree that can comprehensively reflect the aging degree of the battery is obtained.

[0061] Step S108, predict the remaining life of the battery according to the total damage degree of the battery.

[0062] By using the above method, the charging and discharging curve of the state of charge changing with time is obtained, the closed cycle and the non-closed cycle are identified by combining the rain flow analysis method, the non-closed cycle is paired as the closed cycle, and the total damage degree is accumulated based on the equivalent damage degree of the closed cycle to predict the remaining life, which realizes the accurate quantification of the irregular charging and discharging cycle in the actual working condition. Compared with the traditional method which only relies on the number of cycles or the empirical model, this method can effectively capture the damage of part of the charging and discharging and avoid the error of simply equivalent non-regular cycle to complete cycle; at the same time, by distinguishing the damage degree of different closed cycles, the different influences of different discharge depths on the battery aging are fully reflected, which significantly improves the accuracy and adaptability of life prediction.

[0063] In some embodiments, when obtaining the charging and discharging curve of the state of charge changing with time, the state of charge data of the battery can be collected in real time, then the collected state of charge data is filtered, and finally the charging and discharging curve of the state of charge changing with time is generated based on the filtered state of charge data.

[0064] Real-time collection of state of charge data can ensure the timeliness and continuity of data. By filtering out small fluctuations, it can effectively avoid short charging and short discharging interference, so that the generated charging and discharging curve can more smoothly reflect the real trend of battery power change.

[0065] In actual application, the charging and discharging timing data of the battery can be obtained in real time by the battery management system, which records the changes of various parameters such as voltage, current, temperature, state of charge, etc.

[0066] Figure 2 is a schematic flow chart for identifying closed cycles and non-closed cycles based on rain flow analysis method, as shown in Figure 2 Based on the rain flow analysis method, identifying the closed cycle and the non-closed cycle in the charging and discharging curve can include the following steps S202 to S206.

[0067] Step S202, all peak points and valley points are extracted from the charge-discharge curve, and arranged in time sequence to form a peak-valley point sequence.

[0068] This step eliminates irrelevant flat segment data by focusing on the key turning points of the charge-discharge curve, ensuring that the subsequent cycle analysis is only around the effective charge-discharge process, avoiding the interference of redundant information in continuous power changes, and laying a clear data foundation for cycle recognition.

[0069] Step S204, from the starting position of the peak-valley point sequence, three consecutive peak-valley points are selected to form a charge-discharge cycle.

[0070] This continuous point selection method based on time sequence can completely capture the rising-falling or falling-rising process of single charge-discharge, accurately restore the dynamic characteristics of actual charge-discharge, and make the cycle composition highly consistent with the real power change law of the battery.

[0071] Step S206, if the terminal amplitude of the charge-discharge cycle is greater than or equal to the starting amplitude, the charge-discharge cycle is a closed cycle, otherwise it is a non-closed cycle.

[0072] This quantitative determination method avoids subjective experience misjudgment of cycle type, and ensures the consistency and objectivity of closed cycle and non-closed cycle division.

[0073] Figure 3 is a schematic diagram of closed cycle and non-closed cycle according to an embodiment of the present application, as shown in Figure 3 , wherein the three peak-valley points a-b-c and the three peak-valley points d-e-f form a charge-discharge cycle. In the a-b-c cycle, because the terminal amplitude c is greater than the starting amplitude a, it is classified as a closed cycle; in the d-e-f cycle, because the terminal amplitude f is less than the starting amplitude d, it is classified as a non-closed cycle.

[0074] In some embodiments, when pairing non-closed cycles to form closed cycles, the identified non-closed cycles can be stored, and then according to the peak-valley complement principle, non-closed cycles that can complement each other to form a complete charge-discharge process are selected within a first preset deviation range, and the selected non-closed cycles are paired to form closed cycles.

[0075] The identified non-closed cycles are stored to establish a cycle database for subsequent processing. These non-closed cycles are incomplete charge-discharge processes that exist in actual working conditions, and if not processed, they will affect the accurate assessment of battery damage, and the storage operation provides a basic material for subsequent pairing work.

[0076] The core logic of the pairing work is to screen the non-closed cycles that can be paired according to the principle of peak-valley complementation. The peak-valley complementation refers to that the peak value (end point of charging) of one non-closed cycle and the valley value (end point of discharging) of another non-closed cycle can match each other, or vice versa, and the combination of the two can form a complete charging and discharging process. The screening within the first preset deviation range is to consider that there is a certain fluctuation in the actual charging and discharging process, and a certain deviation is allowed to improve the flexibility of pairing and ensure that the complementary cycles can be found within a reasonable range.

[0077] The non-closed cycles screened are paired as closed cycles, the purpose of which is to convert the incomplete cycles into basic units that can be used for calculating equivalent damage degrees. Because only the closed cycles can accurately reflect the damage caused to the battery by one complete charging and discharging, through such pairing operation, the complex irregular charging and discharging process in practice can be converted into a standard closed cycle, providing a unified and reliable analysis object for subsequent calculation of the equivalent damage degree of each cycle and the total damage degree, thereby improving the accuracy of battery life prediction.

[0078] Figure 4 is a schematic diagram of pairing non-closed cycles according to one embodiment of the present application, as Figure 4 shown, the x-y-z cycle and the d-e-f cycle are two non-closed cycles that are successfully paired, x-y is a discharging process, and e-f is a discharging process, and the discharging depths can be calculated, respectively. When the discharging depths of the two non-closed cycles are added, if the result is 100%, it means that they are combined, equivalent to one complete charging and discharging cycle from full charge (or a specific initial state of charge) to substantially empty (or a corresponding end state of charge). For example, the battery starts from 100% state of charge, x-y discharges 50% depth, and e-f discharges 50% depth, which adds up to 100%, equivalent to one full discharge, which can be recorded as one complete cycle for subsequent battery life evaluation, damage degree calculation and other analysis.

[0079] In some embodiments, after pairing the non-closed cycles that can be complementary to form a complete charging and discharging process as closed cycles, the stored non-closed cycles can be re-paired every preset time interval or every preset number of non-closed cycles. Specifically, when re-pairing, non-closed cycles that can be complementary to form a complete charging and discharging process can be screened within a second preset deviation range, wherein the second preset deviation range is smaller than the first preset deviation range, and the screened non-closed cycles are paired as closed cycles.

[0080] By re-pairing the non-closed cycles every preset time interval or after accumulating a preset number, and limiting the second preset deviation range to be smaller than the first preset deviation range, the pairing accuracy can be dynamically optimized. The initial pairing allows a larger deviation to quickly form a closed cycle, meeting the real-time requirement; and the subsequent re-pairing with a smaller deviation range can reduce the matching error of non-closed cycles, further reduce the cumulative deviation of damage degree calculation, and continuously improve the accuracy of cycle recognition and damage quantification through iteration optimization, providing a more reliable data basis for life prediction.

[0081] It should be noted that the first preset deviation range and the second preset deviation range both refer to the difference between the terminal amplitude of the first non-closed cycle and the starting amplitude of the second non-closed cycle in the two successfully paired non-closed cycles. The first preset deviation range and the second preset deviation range can be set in a targeted manner in combination with the actual situation of the battery.

[0082] In some embodiments, when calculating the equivalent damage degree of each closed cycle, the discharge depth and average state of charge of each closed cycle can be obtained, and then the equivalent damage degree of each cycle can be calculated based on the discharge depth and average state of charge.

[0083] The discharge depth directly reflects the change in the amount of electricity during a single charge and discharge process of the battery, and different discharge depths have obvious differences in the degree of damage to the internal structure of the battery. The average state of charge is related to the electrochemical state of the battery during the cycle process, and different average states of charge have different intensities of side reactions, resulting in different damage to the battery. Targeted acquisition of these two parameters can make the calculation of the equivalent damage degree of each closed cycle more in line with the actual damage to the battery, avoiding the roughness of using a uniform standard to calculate the damage of all cycles.

[0084] In some embodiments, when predicting the remaining life of the battery based on the total damage degree of the battery, a mapping relationship model between the total damage degree of the battery and the remaining life can be established, then the total damage degree is input into the mapping relationship model to obtain the remaining life of the battery, and finally the remaining life is presented in the form of remaining cycle number or remaining available capacity percentage.

[0085] The mapping relationship model is constructed based on a large amount of experimental data and actual aging rules, and establishes a quantitative correlation between the abstract total damage degree and the specific remaining life, providing a clear theoretical basis and data support for the prediction process, avoiding the deviation of subjective experience judgment, making the prediction from qualitative to quantitative, and improving the scientificity and stability of the prediction.

[0086] The remaining life is presented in the form of remaining cycle number or remaining available capacity percentage, which is intuitive and easy to understand. The remaining cycle number can clearly indicate to the user how many charging and discharging cycles the battery can still complete, and the remaining available capacity percentage can reflect the ratio of the current capacity to the initial capacity, both of which provide specific reference indicators for battery use planning, replacement decision, etc., and enhance the practical application value of the prediction results.

[0087] Further, after predicting the remaining life of the battery according to the total damage degree of the battery, the maximum discharge depth of the battery can be adjusted according to the predicted remaining life.

[0088] After predicting the remaining life, the maximum discharge depth is dynamically adjusted, which can realize active aging control of the battery. When the remaining life is low, the degradation rate is slowed down by reducing the maximum discharge depth (for example, from allowing discharge to 20% to discharge to 40%), which can extend the actual service time of the battery under the premise of guaranteeing basic use requirements, balances the relationship between battery performance and life, and improves the economy and safety of battery use.

[0089] The flowchart provided by the embodiment is not intended to indicate that the operations of the method are to be performed in any particular order, or that all of the operations of the method are included in every case. In addition, the method can include additional operations. Additional changes can be made to the above-described method within the scope of the technical idea provided by the embodiment method.

[0090] It should be understood that in some embodiments, parts can be realized by hardware, software, firmware or a combination thereof. In the above-described implementation, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0091] The embodiment also provides a computer program product 10, a computer readable storage medium 20, and a computer device 30. Figure 5 is a schematic diagram of the computer program product 10 according to an embodiment of the application, Figure 6 is a schematic diagram of the computer readable storage medium 20 according to an embodiment of the application, Figure 7 is a schematic diagram of the computer device 30 according to an embodiment of the application. The computer program product 10 includes a computer program 11, which, when executed by the processor 32, implements the steps of any of the battery life prediction methods described above. The computer readable storage medium 20 has the above-mentioned computer program 11 stored thereon, and the computer program 11, when executed by the processor 32, implements the steps of the battery life prediction method of any of the embodiments described above. The computer device 30 can include a memory 31, a processor 32, and a computer program 11 stored on the memory 31 and running on the processor 32.

[0092] The computer program 11 for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program 11 may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform various aspects of the present invention, an electronic circuit including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit.

[0093] In the description of this embodiment, the computer program product 10 is a related product including the computer program 11 .

[0094] For the purposes of the description of this embodiment, the computer-readable storage medium 20 is a tangible device capable of retaining and storing the computer program 11, and can be any device that can contain, store, communicate, propagate, or transmit the program 11 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium 20 include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the foregoing.

[0095] The computer device 30 can be, for example, a server, a desktop computer, a notebook computer, a tablet computer, or a smart phone. In some examples, the computer device 30 can be a cloud computing node. The computer device 30 can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. The computer device 30 can be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules can be located in both local and remote computer system storage media including memory storage devices.

[0096] The computer device 30 can include a processor 32 adapted to execute stored instructions, a memory 31 that provides temporary storage for operations of the instructions during operation. The processor 32 can be a single core processor, a multi-core processor, a computing cluster, or any number of other configurations. The memory 31 can include random access memory (RAM), read only memory, flash memory, or any other suitable memory system.

[0097] The computer device 30 can also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows for input and output of data with external devices that can be connected to the computer device. The network adapter / interface can provide for communication between the computer device and a network, typically illustrated as a communication network.

[0098] To this end, it will be understood that, although the present application has been described herein in terms of several exemplary embodiments, modifications which fall within the scope of the present application can be made without departing from the spirit thereof, and it is intended to claim each individual embodiment separately as well as every distinct combination that can be made from the elements of the foregoing embodiments. Therefore, the scope of the present application should be understood to include all such modifications.

Claims

1. A battery life prediction method, comprising: Obtain the charge and discharge curve of the battery as the charge changes over time; identifying closed cycles and non-closed cycles in the charge-discharge curve based on a rain flow analysis method, and pairing the identified non-closed cycles to form closed cycles; Calculating the equivalent damage degree of each closed cycle, and accumulating the calculated equivalent damage degrees to obtain a total damage degree of the battery; The remaining life of the battery is predicted based on the total damage degree of the battery.

2. The battery life prediction method according to claim 1, wherein: The steps of obtaining a charge and discharge curve of a battery showing a change in charge over time include: Real-time collection of battery charge data; performing filtering processing on the collected charge data; A charge-discharge curve showing the change of charge over time is generated based on the filtered charge data.

3. The battery life prediction method according to claim 1, wherein: The steps of identifying closed cycles and non-closed cycles in the charge-discharge curve based on the rainflow analysis method include: Extracting all peak points and valley points from the charge-discharge curve and arranging them in chronological order to form a peak-valley point sequence; From the starting position of the peak-valley point sequence, three consecutive peak-valley points are selected in sequence to form a charge-discharge cycle; If the end point amplitude of the charge-discharge cycle is greater than or equal to the starting point amplitude, the charge-discharge cycle is a closed cycle, otherwise it is a non-closed cycle.

4. The battery life prediction method according to claim 3, wherein: The step of pairing the non-closed loops to form a closed loop comprises: Storing identified non-closed loops; According to the peak-valley complementary principle, non-closed cycles that can complement each other to form a complete charging and discharging process are screened within the first preset deviation range; Pair the filtered non-closed loops into closed loops.

5. The battery life prediction method according to claim 4, wherein: After the step of pairing the non-closed cycles that can complement each other to form a complete charge and discharge process into a closed cycle, the method further includes: Re-pairing all the stored non-closed cycles every preset time period or every time a preset number of the non-closed cycles are accumulated and stored; and The step of re-pairing all stored non-closed loops includes: Screening non-closed cycles that can complement each other to form a complete charge and discharge process within a second preset deviation range, wherein the second preset deviation range is smaller than the first preset deviation range; Pair the filtered non-closed loops into closed loops.

6. The battery life prediction method according to claim 1, wherein: The step of calculating the equivalent damage degree of each closed cycle includes: For each closed cycle, obtaining the discharge depth and average charge; The equivalent damage degree of each cycle is calculated according to the discharge depth and the average charge.

7. The battery life prediction method according to claim 1, wherein: The step of predicting the remaining life of the battery according to the total damage degree of the battery includes: Establish a mapping relationship model between the total damage degree of the battery and the remaining life; Inputting the total damage degree into the mapping relationship model to obtain the remaining life of the battery; The remaining life is presented as the number of remaining cycles or the percentage of remaining available capacity.

8. The battery life prediction method according to claim 1, wherein: After the step of predicting the remaining life of the battery according to the total damage degree of the battery, the method further includes: Adjusts the maximum depth of discharge of the battery based on the predicted remaining life.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the battery life prediction method according to any one of claims 1 to 8 are implemented.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of the battery life prediction method according to any one of claims 1 to 8.

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