A method and system for predicting the remaining service life of a lithium battery

Abstract

The present invention discloses a method and system for predicting the remaining service life of a lithium battery, aligning at least one historical discharge voltage data subsequence, and sliding on at least one historical discharge voltage data subsequence based on a preset sliding window, calculating the change amount of the historical discharge voltage data in the sliding window each time sliding, and using the average value of each historical discharge voltage data change amount as a voltage correction coefficient, and using a preset correction rule to correct the first discharge voltage data according to the voltage correction coefficient, to obtain the first target discharge voltage data under the current cycle period, and determining the current remaining number of cycles corresponding to the first target discharge voltage data according to the preset initial discharge voltage data and the initial number of cycles corresponding to the initial discharge voltage data. The interference caused by the heating of the lithium battery is eliminated as much as possible, and the remaining number of cycles of the current lithium battery can be determined more accurately based on the change of the discharge voltage.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium battery detection, and in particular relates to a method and system for predicting the remaining service life of a lithium battery. Background Art

[0002] Lithium batteries have the characteristics of high energy density and low self-discharge rate. They are the main power source for electric vehicles, mobile phones, and aviation systems. The failure of lithium batteries will cause equipment performance degradation, increase maintenance losses, and even cause sudden disasters. At the same time, replacing lithium batteries too early will cause great economic losses. The remaining life of lithium batteries can represent the real-time status of the machine. Therefore, accurate prediction of the remaining life of lithium batteries is of key significance to the reliability and safety of electric equipment.

[0003] At present, since the heat of lithium batteries will also affect the value of the discharge voltage, if the remaining service life of the lithium battery is determined directly based on the difference between the current discharge voltage and the initial discharge voltage, it will be impossible to accurately analyze whether the change in the current discharge voltage is caused by the aging of the lithium battery itself, which will affect the prediction of the actual service life of the lithium battery. Summary of the invention

[0004] The present invention provides a method and system for predicting the remaining service life of a lithium battery, which is used to solve the technical problem that it is impossible to accurately analyze whether the change in the current discharge voltage is caused by the aging of the lithium battery itself, thereby affecting the prediction of the actual service life of the lithium battery.

[0005] In a first aspect, the present invention provides a method for predicting the remaining service life of a lithium battery, comprising:

[0006] Acquire historical cycle aging data of the lithium battery under multiple historical cycle periods, wherein the historical cycle aging data includes historical discharge voltage data and historical temperature data corresponding to the historical discharge voltage data;

[0007] Sorting each historical discharge voltage data according to the time sequence to obtain a historical discharge voltage data sequence, wherein one historical discharge voltage data in the historical discharge voltage data sequence corresponds to one historical temperature data;

[0008] Acquire first cycle aging data of the lithium battery in a current cycle, wherein the first cycle aging data includes first discharge voltage data and first temperature data corresponding to the first discharge voltage data;

[0009] According to the first temperature data, at least one target historical discharge voltage data is extracted from the historical discharge voltage data sequence using a preset extraction rule, and at least one historical discharge voltage data subsequence is intercepted using the at least one target historical discharge voltage data as a starting node according to a preset interception rule;

[0010] Aligning the at least one historical discharge voltage data subsequence according to the historical temperature data, and sliding on the at least one historical discharge voltage data subsequence based on a preset sliding window, calculating the change amount of the historical discharge voltage data in the sliding window each time sliding, and using the average value of each change amount of the historical discharge voltage data as the voltage correction coefficient;

[0011] According to the voltage correction coefficient, the first discharge voltage data is corrected using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and according to the preset initial discharge voltage data and the initial cycle number corresponding to the initial discharge voltage data, the current remaining cycle number corresponding to the first target discharge voltage data is determined.

[0012] In a second aspect, the present invention provides a lithium battery remaining service life prediction system, comprising:

[0013] A first acquisition module is configured to acquire historical cycle aging data of the lithium battery under multiple historical cycle periods, wherein the historical cycle aging data includes historical discharge voltage data and historical temperature data corresponding to the historical discharge voltage data;

[0014] A sorting module is configured to sort each historical discharge voltage data according to a time sequence to obtain a historical discharge voltage data sequence, wherein one historical discharge voltage data in the historical discharge voltage data sequence corresponds to one historical temperature data;

[0015] A second acquisition module is configured to acquire first cycle aging data of the lithium battery in a current cycle, wherein the first cycle aging data includes first discharge voltage data and first temperature data corresponding to the first discharge voltage data;

[0016] an extracting module, configured to extract at least one target historical discharge voltage data from the historical discharge voltage data sequence according to the first temperature data and using a preset extracting rule, and to obtain at least one historical discharge voltage data subsequence by taking the at least one target historical discharge voltage data as a starting node according to the preset extracting rule;

[0017] a calculation module, configured to align the at least one historical discharge voltage data subsequence according to the historical temperature data, and slide on the at least one historical discharge voltage data subsequence based on a preset sliding window, calculate the change amount of the historical discharge voltage data in the sliding window each time sliding, and use the average value of each change amount of the historical discharge voltage data as the voltage correction coefficient;

[0018] The correction module is configured to correct the first discharge voltage data according to the voltage correction coefficient using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and determine the current remaining number of cycles corresponding to the first target discharge voltage data according to the preset initial discharge voltage data and the initial number of cycles corresponding to the initial discharge voltage data.

[0019] In a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for predicting the remaining useful life of a lithium battery of any embodiment of the present invention.

[0020] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the method for predicting the remaining useful life of a lithium battery of any embodiment of the present invention.

[0021] The lithium battery remaining service life prediction method and system of the present application align at least one historical discharge voltage data subsequence according to historical temperature data, and slide on at least one historical discharge voltage data subsequence based on a preset sliding window, calculate the change amount of the historical discharge voltage data in the sliding window each time sliding, and use the average value of each historical discharge voltage data change amount as the voltage correction coefficient, and use the voltage correction coefficient to correct the first discharge voltage data using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and determine the current remaining number of cycles corresponding to the first target discharge voltage data based on the preset initial discharge voltage data and the initial number of cycles corresponding to the initial discharge voltage data, so as to eliminate the interference caused by the heating of the lithium battery as much as possible, and can more accurately determine the remaining number of cycles of the current lithium battery based on the change of the discharge voltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a method for predicting the remaining service life of a lithium battery provided by one embodiment of the present invention;

[0024] Figure 2 A structural block diagram of a lithium battery remaining service life prediction system provided by an embodiment of the present invention;

[0025] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 , which shows a flow chart of a method for predicting the remaining service life of a lithium battery of the present application.

[0028] like Figure 1 As shown, the method for predicting the remaining service life of a lithium battery specifically includes the following steps:

[0029] Step S101, obtaining historical cycle aging data of the lithium battery under multiple historical cycle periods, wherein the historical cycle aging data includes historical discharge voltage data and historical temperature data corresponding to the historical discharge voltage data.

[0030] Under ideal conditions, the temperature of a lithium battery remains unchanged after each cycle of charge and discharge. Therefore, the number of cycles of the current lithium battery can be determined directly by comparing the degree of change in the discharge voltage, thereby determining the number of remaining cycles of the lithium battery. However, in actual situations, the temperature of a lithium battery changes after each cycle of charge and discharge. For example, when the lithium battery is not in use, the temperature reaches a first temperature after the first cycle, and reaches a second temperature after the second cycle. The first temperature and the second temperature are generally different. Therefore, obtaining the historical discharge voltage data and historical temperature data of the lithium battery under multiple historical cycle periods facilitates the accurate prediction of the remaining service life of the subsequent lithium battery.

[0031] Step S102 , sorting each historical discharge voltage data according to the time sequence to obtain a historical discharge voltage data sequence, wherein one historical discharge voltage data in the historical discharge voltage data sequence corresponds to one historical temperature data.

[0032] Step S103, obtaining first cycle aging data of the lithium battery in the current cycle period, wherein the first cycle aging data includes first discharge voltage data and first temperature data corresponding to the first discharge voltage data.

[0033] Step S104: extract at least one target historical discharge voltage data from the historical discharge voltage data sequence according to the first temperature data using a preset extraction rule, and obtain at least one historical discharge voltage data subsequence by taking the at least one target historical discharge voltage data as a starting node according to a preset interception rule.

[0034] In this step, it is checked whether there is at least one historical temperature data with the same value as the first temperature data; if not, at least one target historical temperature data with the smallest difference from the first temperature data is obtained, and historical discharge voltage data corresponding to the at least one target historical temperature data is extracted from the historical discharge voltage data sequence as the target historical discharge voltage data; if it exists, the target historical discharge voltage data corresponding to the at least one historical temperature data with the same value as the first temperature data is directly extracted from the historical discharge voltage data sequence.

[0035] For example, the current value of the first temperature data is 38°C, and the historical temperature data with the smallest difference from the first temperature data (38°C) is the historical temperature data A with a value of 37°C. Therefore, the historical discharge voltage data a corresponding to the historical temperature data A is used as the target historical discharge voltage data.

[0036] It should be noted that, taking a certain target historical discharge voltage data as the starting node, it is judged in turn whether the historical temperature data corresponding to each historical discharge voltage data after the starting node is greater than the first temperature data; until the historical temperature data corresponding to a certain historical discharge voltage data is not greater than the first temperature data, a historical discharge voltage data adjacent to a certain historical discharge voltage data is taken as the ending node, wherein the time order of a historical discharge voltage data is before a certain historical discharge voltage data; all historical discharge voltage data between the starting node and the ending node are intercepted, and arranged based on the time order to obtain a certain historical discharge voltage data subsequence.

[0037] For example, the temperature value corresponding to the historical discharge voltage data a is 37°C. Taking the historical discharge voltage data a as the starting node, after the historical discharge voltage data a, in chronological order: the temperature value corresponding to the historical discharge voltage data b is 38°C, the temperature value corresponding to the historical discharge voltage data c is 40°C, the temperature value corresponding to the historical discharge voltage data d is 43°C, the temperature value corresponding to the historical discharge voltage data e is 50°C, and the temperature value corresponding to the historical discharge voltage data f is 37°C.

[0038] Therefore, the intercepted historical discharge voltage data subsequence sequentially includes historical discharge voltage data b, historical discharge voltage data c, historical discharge voltage data d, and historical discharge voltage data e.

[0039] Step S105, aligning the at least one historical discharge voltage data subsequence according to the historical temperature data, and sliding on the at least one historical discharge voltage data subsequence based on a preset sliding window, calculating the change amount of the historical discharge voltage data in the sliding window each time sliding, and using the average value of each historical discharge voltage data change amount as the voltage correction coefficient.

[0040] In this step, the first historical discharge voltage data and the second historical discharge voltage data within the same historical temperature range are aligned, wherein the first historical discharge voltage data is the historical discharge voltage data in the first historical discharge voltage data subsequence, and the second historical discharge voltage data is the historical discharge voltage data in the second historical discharge voltage data subsequence; the historical discharge voltage data that is not aligned in the first historical discharge voltage data subsequence or the second historical discharge voltage data subsequence is removed.

[0041] Furthermore, a discharge voltage difference between the first historical discharge voltage data and the second historical discharge voltage data in the same sliding window, and a time interval between the first historical discharge voltage data and the second historical discharge voltage data are obtained, wherein the second historical discharge voltage data subsequence in which the second historical discharge voltage data is located is a historical discharge voltage data subsequence adjacent to the first discharge voltage data; and the change amount of the historical discharge voltage data is determined according to the ratio of the discharge voltage difference to the time interval.

[0042] Step S106, according to the voltage correction coefficient, the first discharge voltage data is corrected using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and according to the preset initial discharge voltage data and the initial cycle number corresponding to the initial discharge voltage data, the current remaining cycle number corresponding to the first target discharge voltage data is determined.

[0043] In this step, the second target historical discharge voltage data corresponding to the first discharge voltage data is obtained in the second historical discharge voltage data subsequence, wherein the historical temperature data corresponding to the second target historical discharge voltage data is the same as the first temperature data; the target time interval between the first discharge voltage data and the second target historical discharge voltage data is obtained, and the predicted discharge voltage data is determined according to the target time interval and the voltage correction coefficient; it is determined whether the difference between the predicted discharge voltage data and the first discharge voltage data is greater than the preset voltage difference; if it is greater than the preset voltage difference, the average value of the predicted discharge voltage data and the first discharge voltage data is taken as the corrected first discharge voltage data; if it is not greater than the preset voltage difference, the first discharge voltage data is not corrected. Afterwards, according to the preset initial discharge voltage data and the initial number of cycles corresponding to the initial discharge voltage data, the current remaining number of cycles corresponding to the first target discharge voltage data is determined.

[0044] It should be noted that the initial discharge voltage data and the initial cycle number can be calculated based on the factory parameters of the lithium battery.

[0045] In summary, the method of the present application aligns at least one historical discharge voltage data subsequence according to historical temperature data, and slides on at least one historical discharge voltage data subsequence based on a preset sliding window, calculates the change in the historical discharge voltage data in the sliding window each time it slides, and uses the average value of each historical discharge voltage data change as a voltage correction coefficient, and uses a preset correction rule to correct the first discharge voltage data according to the voltage correction coefficient to obtain the first target discharge voltage data under the current cycle period, and determines the current remaining number of cycles corresponding to the first target discharge voltage data according to the preset initial discharge voltage data and the initial number of cycles corresponding to the initial discharge voltage data, which can eliminate the interference caused by the heating of the lithium battery as much as possible, and can more accurately determine the remaining number of cycles of the current lithium battery based on the change in discharge voltage.

[0046] See also Figure 2 , which shows a structural block diagram of a lithium battery remaining service life prediction system of the present application.

[0047] like Figure 2 As shown, the lithium battery remaining service life prediction system 200 includes a first acquisition module 210 , a sorting module 220 , a second acquisition module 230 , a capture module 240 , a calculation module 250 and a correction module 260 .

[0048] Among them, the first acquisition module 210 is configured to obtain the historical cycle aging data of the lithium battery under multiple historical cycle periods, wherein the historical cycle aging data includes historical discharge voltage data and historical temperature data corresponding to the historical discharge voltage data; the sorting module 220 is configured to sort each historical discharge voltage data according to the time sequence to obtain a historical discharge voltage data sequence, wherein one historical discharge voltage data in the historical discharge voltage data sequence corresponds to one historical temperature data; the second acquisition module 230 is configured to obtain the first cycle aging data of the lithium battery under the current cycle period, wherein the first cycle aging data includes first discharge voltage data and first temperature data corresponding to the first discharge voltage data; the interception module 240 is configured to extract at least one target historical discharge voltage data in the historical discharge voltage data sequence according to the first temperature data using a preset extraction rule , and according to the preset interception rule, taking the at least one target historical discharge voltage data as the starting node, intercepting at least one historical discharge voltage data subsequence; the calculation module 250 is configured to align the at least one historical discharge voltage data subsequence according to the historical temperature data, and slide on the at least one historical discharge voltage data subsequence based on a preset sliding window, calculate the change amount of the historical discharge voltage data in the sliding window each time sliding, and use the average value of each change amount of the historical discharge voltage data as the voltage correction coefficient; the correction module 260 is configured to correct the first discharge voltage data according to the voltage correction coefficient using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and determine the current remaining number of cycles corresponding to the first target discharge voltage data according to the preset initial discharge voltage data and the initial number of cycles corresponding to the initial discharge voltage data.

[0049] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0050] In some other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the method for predicting the remaining service life of a lithium battery in any of the above method embodiments;

[0051] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0052] Acquire historical cycle aging data of the lithium battery under multiple historical cycle periods, wherein the historical cycle aging data includes historical discharge voltage data and historical temperature data corresponding to the historical discharge voltage data;

[0053] Sorting each historical discharge voltage data according to the time sequence to obtain a historical discharge voltage data sequence, wherein one historical discharge voltage data in the historical discharge voltage data sequence corresponds to one historical temperature data;

[0054] Acquire first cycle aging data of the lithium battery in a current cycle, wherein the first cycle aging data includes first discharge voltage data and first temperature data corresponding to the first discharge voltage data;

[0055] According to the first temperature data, at least one target historical discharge voltage data is extracted from the historical discharge voltage data sequence using a preset extraction rule, and at least one historical discharge voltage data subsequence is intercepted using the at least one target historical discharge voltage data as a starting node according to a preset interception rule;

[0056] Aligning the at least one historical discharge voltage data subsequence according to the historical temperature data, and sliding on the at least one historical discharge voltage data subsequence based on a preset sliding window, calculating the change amount of the historical discharge voltage data in the sliding window each time sliding, and using the average value of each change amount of the historical discharge voltage data as the voltage correction coefficient;

[0057] According to the voltage correction coefficient, the first discharge voltage data is corrected using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and according to the preset initial discharge voltage data and the initial cycle number corresponding to the initial discharge voltage data, the current remaining cycle number corresponding to the first target discharge voltage data is determined.

[0058] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the lithium battery remaining service life prediction system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the lithium battery remaining service life prediction system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0059] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 The example of the connection via bus is taken. The memory 320 is the computer-readable storage medium mentioned above. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the method for predicting the remaining service life of a lithium battery in the above method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the lithium battery remaining service life prediction system. The output device 340 may include display devices such as display screens.

[0060] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0061] As an embodiment, the electronic device is applied to a lithium battery remaining service life prediction system and is used for a client, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0062] Acquire historical cycle aging data of the lithium battery under multiple historical cycle periods, wherein the historical cycle aging data includes historical discharge voltage data and historical temperature data corresponding to the historical discharge voltage data;

[0063] Sorting each historical discharge voltage data according to the time sequence to obtain a historical discharge voltage data sequence, wherein one historical discharge voltage data in the historical discharge voltage data sequence corresponds to one historical temperature data;

[0064] Acquire first cycle aging data of the lithium battery in a current cycle, wherein the first cycle aging data includes first discharge voltage data and first temperature data corresponding to the first discharge voltage data;

[0065] According to the first temperature data, at least one target historical discharge voltage data is extracted from the historical discharge voltage data sequence using a preset extraction rule, and at least one historical discharge voltage data subsequence is intercepted using the at least one target historical discharge voltage data as a starting node according to a preset interception rule;

[0066] Aligning the at least one historical discharge voltage data subsequence according to the historical temperature data, and sliding on the at least one historical discharge voltage data subsequence based on a preset sliding window, calculating the change amount of the historical discharge voltage data in the sliding window each time sliding, and using the average value of each change amount of the historical discharge voltage data as the voltage correction coefficient;

[0067] According to the voltage correction coefficient, the first discharge voltage data is corrected using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and according to the preset initial discharge voltage data and the initial cycle number corresponding to the initial discharge voltage data, the current remaining cycle number corresponding to the first target discharge voltage data is determined.

[0068] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the remaining service life of a lithium battery, characterized in that: include: Acquire historical cycle aging data of the lithium battery under multiple historical cycle periods, wherein the historical cycle aging data includes historical discharge voltage data and historical temperature data corresponding to the historical discharge voltage data; Sorting each historical discharge voltage data according to the time sequence to obtain a historical discharge voltage data sequence, wherein one historical discharge voltage data in the historical discharge voltage data sequence corresponds to one historical temperature data; Acquire first cycle aging data of the lithium battery in a current cycle, wherein the first cycle aging data includes first discharge voltage data and first temperature data corresponding to the first discharge voltage data; According to the first temperature data, at least one target historical discharge voltage data is extracted from the historical discharge voltage data sequence by using a preset extraction rule, and at least one historical discharge voltage data subsequence is obtained by taking the at least one target historical discharge voltage data as a starting node according to a preset extraction rule, wherein according to the first temperature data, extracting at least one target historical discharge voltage data from the historical discharge voltage data sequence by using a preset extraction rule comprises: Find out whether there is at least one historical temperature data having the same value as the first temperature data; If not, obtaining at least one target historical temperature data having the smallest difference with the first temperature data, and extracting historical discharge voltage data corresponding to the at least one target historical temperature data from the historical discharge voltage data sequence as target historical discharge voltage data; If so, directly extracting target historical discharge voltage data corresponding to at least one historical temperature data identical to the first temperature data from the historical discharge voltage data sequence; The method of taking the at least one target historical discharge voltage data as a starting node according to a preset interception rule and intercepting at least one historical discharge voltage data subsequence comprises: Taking a certain target historical discharge voltage data as a starting node, sequentially determining whether the historical temperature data corresponding to each historical discharge voltage data after the starting node is greater than the first temperature data; When the historical temperature data corresponding to a certain historical discharge voltage data is not greater than the first temperature data, a historical discharge voltage data adjacent to the certain historical discharge voltage data is used as a termination node, wherein the time order of the historical discharge voltage data is before the certain historical discharge voltage data; intercepting all historical discharge voltage data between the start node and the end node, and arranging them in time order to obtain a certain historical discharge voltage data subsequence; Aligning the at least one historical discharge voltage data subsequence according to the historical temperature data, and sliding on the at least one historical discharge voltage data subsequence based on a preset sliding window, calculating the change amount of the historical discharge voltage data in the sliding window each time sliding, and using the average value of each change amount of the historical discharge voltage data as the voltage correction coefficient; According to the voltage correction coefficient, the first discharge voltage data is corrected using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and according to the preset initial discharge voltage data and the initial number of cycles corresponding to the initial discharge voltage data, the current remaining number of cycles corresponding to the first target discharge voltage data is determined, and the initial discharge voltage data and the initial number of cycles are calculated according to the factory parameters of the lithium battery.

2. A method for predicting the remaining service life of a lithium battery according to claim 1, characterized in that: The aligning the at least one historical discharge voltage data subsequence according to the historical temperature data comprises: Aligning first historical discharge voltage data and second historical discharge voltage data within the same historical temperature range, wherein the first historical discharge voltage data is historical discharge voltage data in a first historical discharge voltage data subsequence, and the second historical discharge voltage data is historical discharge voltage data in a second historical discharge voltage data subsequence; The unaligned historical discharge voltage data in the first historical discharge voltage data subsequence or the second historical discharge voltage data subsequence is removed.

3. A method for predicting the remaining service life of a lithium battery according to claim 2, characterized in that: The sliding window based on a preset sliding window slides on the at least one historical discharge voltage data subsequence, and calculating the change amount of the historical discharge voltage data in the sliding window each time the sliding window slides includes: Acquire a discharge voltage difference between the first historical discharge voltage data and the second historical discharge voltage data in the same sliding window, and a time interval between the first historical discharge voltage data and the second historical discharge voltage data, wherein the second historical discharge voltage data subsequence in which the second historical discharge voltage data is located is a historical discharge voltage data subsequence adjacent to the first discharge voltage data; The change amount of the historical discharge voltage data is determined according to the ratio of the discharge voltage difference to the time interval.

4. A method for predicting the remaining service life of a lithium battery according to claim 3, characterized in that: The first discharge voltage data is corrected according to the voltage correction coefficient by using a preset correction rule to obtain the first target discharge voltage data in the current cycle period, including: acquiring second target historical discharge voltage data corresponding to the first discharge voltage data in the second historical discharge voltage data subsequence, wherein the historical temperature data corresponding to the second target historical discharge voltage data is the same as the first temperature data; Acquire a target time interval between the first discharge voltage data and the second target historical discharge voltage data, and determine predicted discharge voltage data according to the target time interval and the voltage correction coefficient; Determining whether a difference between the predicted discharge voltage data and the first discharge voltage data is greater than a preset voltage difference; If it is greater than the preset voltage difference, taking the average value of the predicted discharge voltage data and the first discharge voltage data as the corrected first discharge voltage data; If it is not greater than the preset voltage difference, the first discharge voltage data is not corrected.

5. A lithium battery remaining service life prediction system, characterized in that: include: A first acquisition module is configured to acquire historical cycle aging data of the lithium battery under multiple historical cycle periods, wherein the historical cycle aging data includes historical discharge voltage data and historical temperature data corresponding to the historical discharge voltage data; A sorting module is configured to sort each historical discharge voltage data according to a time sequence to obtain a historical discharge voltage data sequence, wherein one historical discharge voltage data in the historical discharge voltage data sequence corresponds to one historical temperature data; A second acquisition module is configured to acquire first cycle aging data of the lithium battery in a current cycle, wherein the first cycle aging data includes first discharge voltage data and first temperature data corresponding to the first discharge voltage data; The interception module is configured to extract at least one target historical discharge voltage data from the historical discharge voltage data sequence according to the first temperature data using a preset extraction rule, and intercept at least one historical discharge voltage data subsequence with the at least one target historical discharge voltage data as a starting node according to the preset interception rule, wherein the extracting at least one target historical discharge voltage data from the historical discharge voltage data sequence according to the first temperature data using a preset extraction rule includes: Find out whether there is at least one historical temperature data having the same value as the first temperature data; If not, obtaining at least one target historical temperature data having the smallest difference with the first temperature data, and extracting historical discharge voltage data corresponding to the at least one target historical temperature data from the historical discharge voltage data sequence as target historical discharge voltage data; If so, directly extracting target historical discharge voltage data corresponding to at least one historical temperature data identical to the first temperature data from the historical discharge voltage data sequence; The method of taking the at least one target historical discharge voltage data as a starting node according to a preset interception rule and intercepting at least one historical discharge voltage data subsequence comprises: Taking a certain target historical discharge voltage data as a starting node, sequentially determining whether the historical temperature data corresponding to each historical discharge voltage data after the starting node is greater than the first temperature data; When the historical temperature data corresponding to a certain historical discharge voltage data is not greater than the first temperature data, a historical discharge voltage data adjacent to the certain historical discharge voltage data is used as a termination node, wherein the time order of the historical discharge voltage data is before the certain historical discharge voltage data; intercepting all historical discharge voltage data between the start node and the end node, and arranging them in time order to obtain a certain historical discharge voltage data subsequence; a calculation module, configured to align the at least one historical discharge voltage data subsequence according to the historical temperature data, and slide on the at least one historical discharge voltage data subsequence based on a preset sliding window, calculate the change amount of the historical discharge voltage data in the sliding window each time sliding, and use the average value of each change amount of the historical discharge voltage data as the voltage correction coefficient; The correction module is configured to correct the first discharge voltage data according to the voltage correction coefficient using a preset correction rule to obtain the first target discharge voltage data under the current cycle period, and determine the current remaining number of cycles corresponding to the first target discharge voltage data according to the preset initial discharge voltage data and the initial number of cycles corresponding to the initial discharge voltage data, and the initial discharge voltage data and the initial number of cycles are calculated according to the factory parameters of the lithium battery.

6. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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