Lithium ion battery fault grading diagnosis method based on self-attenuation interval anomaly

Abstract

The invention belongs to the technical field of lithium ion battery fault diagnosis. According to the lithium ion battery fault grading diagnosis method based on the self-attenuation interval abnormity degree, first-level diagnosis is carried out according to the sample entropy of a battery monomer and a correlation coefficient of a voltage sequence and a module mean value voltage sequence; secondary diagnosis is carried out according to the comparison result of the battery self-attenuation interval anomaly and the set anomaly threshold, the problem that the false alarm rate and the missing report rate in a traditional threshold diagnosis method cannot be considered at the same time is solved through a two-stage diagnosis strategy, the good fault diagnosis capability is achieved under different working conditions, and robustness is high.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery fault diagnosis, and in particular to a lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality, a lithium-ion battery fault classification diagnosis system based on self-attenuation interval abnormality, a computer device, a computer-readable storage medium and a computer program product. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Lithium-ion batteries have become efficient energy supply components for large-scale energy storage and clean energy promotion due to their low pollution, long cycle life, high energy density, and low self-discharge rate. However, lithium-ion batteries are affected by a variety of factors such as ambient temperature, aging, working conditions, and mechanical shock, which will inevitably induce a variety of faults, thereby affecting the safe and stable operation of the energy storage system. Therefore, it is crucial to diagnose early minor battery faults in a timely manner. However, the characteristics of early faults are highly concealed and easily masked by noise, making it extremely difficult to accurately diagnose them.

[0004] Common battery failures include internal short circuit, external short circuit, overvoltage, poor contact, etc. Among them, the battery internal short circuit failure will experience a chain reaction of heat release at the end, and finally cause thermal runaway. Poor contact failure will cause voltage mutation, damage the internal components of the battery, and increase the risk of thermal runaway. Therefore, timely detection of small short circuits and poor contact failures is the key to ensuring the safe operation of lithium-ion batteries.

[0005] Traditional threshold detection technology diagnoses battery faults by judging whether battery voltage, current, temperature and other parameters exceed the threshold. The threshold method is widely used in vehicle battery management systems due to its simple implementation and easy realization. However, it is difficult to set the optimal threshold and cannot detect minor faults, resulting in high false alarm and missed alarm rates.

[0006] Patent No. CN202010978479.1 proposes an online fault diagnosis technology based on battery electrical and thermal models to achieve real-time detection of internal short circuit and thermal abuse faults. However, this technology requires an accurate battery mechanism model, has high computational complexity, and is difficult to apply in practice; Patent No. CN201911024438.2 proposes a battery fault detection technology based on improved sample entropy, which diagnoses short circuit and open circuit faults by calculating the sample entropy of the battery cell voltage. However, this technology only uses sample entropy as a single fault diagnosis indicator. When faced with transient conditions of actual battery operation, this technology will have omissions and false alarms, and has poor robustness; Patent No. CN202210600865.6 detects lithium deposition faults online based on real-time measured battery thickness, temperature, charge and other data. However, this technology requires a complex lithium deposition model and precise sensor layout, and has poor practicality. In summary, the existing fault diagnosis methods cannot take into account both low false alarm rate and low omission rate, and have high computational complexity and poor robustness. Summary of the invention

[0007] In order to address the deficiencies in the prior art, the present invention provides a lithium-ion battery fault hierarchical diagnosis method based on self-attenuation interval abnormality. By calculating the sample entropy and correlation coefficient of the single cell voltage sequence, a first-level fault diagnosis is quickly performed on the battery cell, and a more accurate second-level fault diagnosis is performed based on the self-attenuation interval abnormality, thereby achieving a more accurate diagnosis of the battery fault.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality.

[0010] A lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality includes the following processes:

[0011] For any battery cell, collect the voltage sequence of the battery cell and the module mean voltage sequence, calculate the sample entropy of the battery cell, and the correlation coefficient between the voltage sequence and the module mean voltage sequence;

[0012] Calculating the battery self-attenuation interval abnormality of the battery cell according to the sample entropy and the correlation coefficient;

[0013] When the sample entropy is less than or equal to the sample entropy threshold, and the correlation coefficient is greater than or equal to the correlation coefficient threshold, it is determined that the battery cell is normal; when the sample entropy is greater than the sample entropy threshold, or the correlation coefficient is less than the correlation coefficient threshold, it is determined that the battery cell is faulty;

[0014] When determining that the battery cell is faulty, the battery self-attenuation interval abnormality is compared with a set abnormality threshold. When the battery self-attenuation interval abnormality is less than the set abnormality threshold, the battery cell is finally determined to be faulty; when the battery self-attenuation interval abnormality is equal to the set abnormality threshold, the battery cell is not faulty.

[0015] In a second aspect, the present invention provides a lithium-ion battery fault classification diagnosis system based on self-attenuation interval abnormality.

[0016] A lithium-ion battery fault classification diagnosis system based on self-attenuation interval abnormality, comprising:

[0017] The sample entropy and correlation coefficient calculation unit is configured to: for any battery cell, collect the voltage sequence of the battery cell and the module mean voltage sequence, and calculate the sample entropy of the battery cell and the correlation coefficient between the voltage sequence and the module mean voltage sequence;

[0018] A battery self-decay interval abnormality calculation unit is configured to: calculate the battery self-decay interval abnormality of the battery cell according to the sample entropy and the correlation coefficient;

[0019] The primary diagnosis unit is configured to: determine that the battery cell is normal when the sample entropy is less than or equal to the sample entropy threshold and the correlation coefficient is greater than or equal to the correlation coefficient threshold; determine that the battery cell is faulty when the sample entropy is greater than the sample entropy threshold or the correlation coefficient is less than the correlation coefficient threshold;

[0020] The secondary diagnostic unit is configured to: when determining that the battery cell is faulty, compare the abnormality degree of the battery self-attenuation interval with a set abnormality threshold; when the abnormality degree of the battery self-attenuation interval is less than the set abnormality threshold, finally determine that the battery cell is faulty; when the abnormality degree of the battery self-attenuation interval is equal to the set abnormality threshold, the battery cell is not faulty.

[0021] In a third aspect, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium;

[0022] a processor adapted to execute a computer program;

[0023] A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the lithium-ion battery fault classification diagnosis method based on the self-attenuation interval abnormality as described in the first aspect of the present invention is implemented.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality as described in the first aspect of the present invention.

[0025] In a fifth aspect, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality as described in the first aspect of the present invention.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention innovatively proposes a lithium-ion battery fault classification diagnosis strategy based on the self-attenuation interval abnormality, and proposes the battery self-attenuation interval abnormality, which can accurately extract the characteristics of minor faults; the present invention has good fault diagnosis capabilities under different working conditions and strong robustness;

[0028] The present invention performs a primary diagnosis based on the sample entropy of the battery cell and the correlation coefficient between the voltage sequence and the module mean voltage sequence, and performs a secondary diagnosis based on the comparison result of the abnormality of the battery self-attenuation interval and the set abnormality threshold. Through the two-level diagnosis strategy, the problem of the inability to take both the false alarm rate and the missed alarm rate into account in the traditional threshold diagnosis method is overcome.

[0029] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0031] Figure 1 A schematic flow chart of a lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality provided in Example 1 of the present invention;

[0032] Figure 2 The voltage curve of the battery pack under the UDDS working condition provided in Example 1 of the present invention;

[0033] Figure 3 The fault diagnosis result based on sample entropy in the primary diagnosis provided in Embodiment 1 of the present invention;

[0034] Figure 4 The fault diagnosis result based on the correlation coefficient in the primary diagnosis provided by Embodiment 1 of the present invention;

[0035] Figure 5 The minor fault diagnosis result based on the abnormality degree of the self-attenuation interval provided in Example 1 of the present invention;

[0036] Figure 6 The diagnostic results of the technology of the present invention under the CLTC working condition provided in Example 1 of the present invention;

[0037] Figure 7 The diagnostic results of the technology of the present invention under the HWYCOL working condition provided by Example 1 of the present invention;

[0038] Figure 8 A schematic diagram of a lithium-ion battery fault classification diagnosis system based on self-attenuation interval abnormality provided in Example 2 of the present invention;

[0039] Fig. 9 A schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0042] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0043] Embodiment 1:

[0044] This implementation proposes a lithium-ion battery fault classification diagnosis method based on the abnormality of the self-attenuation interval, such as Figure 1 As shown, the following process is included:

[0045] S1: First-level diagnosis based on sample entropy and correlation coefficient.

[0046] S1.1: Sample entropy.

[0047] First, collect the voltage sequence of monomer n And the module mean voltage series

[0048]

[0049] In the formula, u n (t) is the battery cell voltage of cell n at time t, is the mean voltage of the entire battery module at time t, N is the length of the voltage sequence, and m is the width of the moving time window.

[0050] Calculate voltage deviation series

[0051]

[0052] definition and Distance:

[0053]

[0054] According to the calculated monomer voltage deviation sequence distance, statistics The number of A i , where r is the similarity tolerance of sample entropy.

[0055]

[0056] Calculate the voltage deviation series of two monomers Probability of matching m points:

[0057]

[0058] Increase the dimension to m+1, statistics The distance is denoted as B i :

[0059]

[0060] Calculate the voltage deviation series of two monomers Probability of matching m+1 points:

[0061]

[0062] Finally, the sample entropy is calculated.

[0063]

[0064] (2) Correlation coefficient

[0065] calculate and Correlation coefficient:

[0066]

[0067] Where COV(t) is and The covariance of , σ1(t) and σ2(t) are and The standard deviation of , m is the width of the moving time window.

[0068] S2: Secondary diagnosis based on the abnormality of the self-attenuation interval.

[0069] Invention battery self-attenuation interval abnormality:

[0070]

[0071] In the formula, α is the abnormal scaling factor, CORR(t) and SE(t) are the correlation coefficient and sample entropy of the monomer at time t, respectively. t0 is the starting time, and β is the self-attenuation factor.

[0072] The overall framework of the algorithm is as follows Figure 1 First, the sample entropy and correlation coefficient are calculated based on the original voltage sequence of each battery cell to achieve rapid first-level fault diagnosis, namely:

[0073]

[0074] In the formula, θ SE and θ CORR are the diagnostic thresholds of the sample entropy method and the correlation coefficient method, respectively.

[0075] Afterwards, the self-attenuation interval abnormality fault feature extraction technology was invented to achieve accurate secondary fault diagnosis and reduce the false alarm rate and missed alarm rate, namely:

[0076]

[0077] In the formula, θ FC It is the upper limit threshold of the abnormality degree in the self-attenuation interval.

[0078] Figure 2 The figure shows the original voltage curve of the battery pack under UDDS working condition. The sampling frequency is 1HZ. Specifically, the No. 1 cell had external short circuit, internal short circuit and poor contact faults at 300-330s, 660-690s and 1180-1140s respectively. It can be seen that the poor contact fault caused the voltage fluctuation to be less than 0.1V. The internal / external short circuit fault caused the voltage fluctuation to be less than 0.05V.

[0079] First, the mean voltage sequence of the six cells in the battery pack is calculated; then, the sample entropy of the cell voltage deviation sequence, the correlation coefficient of the original voltage sequence and the mean voltage sequence are calculated. Among them, the sample entropy similarity tolerance r = 0.01, and the time window width of the sample entropy and the correlation coefficient m = 8.

[0080] Figure 3 The fault diagnosis results based on sample entropy in the first-level diagnosis are shown. It can be seen that the fault diagnosis method based on sample entropy can detect that the faulty monomer 1 has a poor contact fault at 1180s-1140s. However, it is unable to diagnose the internal short circuit fault and the external short circuit fault. In addition, the voltage sample entropy of monomer 3, monomer 4 and monomer 5 is higher than the threshold, resulting in a false alarm. The method achieves a recall rate of 85.2% and a false alarm rate of 4.4%.

[0081] Figure 4 The fault diagnosis results based on correlation coefficient in the first-level diagnosis are shown. It can be seen that this method can detect that the faulty cell 1 has external short circuit, internal short circuit and poor contact faults at 300s-330s, 660s-690s, and 1180s-1140s, respectively, and achieves full detection of three types of faults. However, the correlation coefficient of cell 2 at 660s-690s and 1180s-1140s is also lower than the threshold, resulting in false alarms. This method achieves a complete search rate of 88.9% and a false alarm rate of 29.6%. In summary, the first-level diagnostic technology can roughly identify minor battery faults. However, false alarms and missed alarms occur frequently.

[0082] Figure 5 The results of minor fault diagnosis based on the self-attenuation interval abnormality are demonstrated. It can be seen that the technology accurately diagnosed the internal short circuit, external short circuit, and poor contact faults of the faulty battery 1 at 300s-330s, 660s-690s, and 1080s-1140s, and no false alarms or missed alarms occurred. After multiple large-scale battery data tests and verifications, the technology achieved a completeness rate of ≥96.3% and a false alarm rate of ≤2.3%. This is because the self-attenuation interval abnormality of the present technology accurately extracts the early characteristics of minor faults and filters out environmental noise and normal voltage fluctuations.

[0083] Figure 6 , Figure 7 The secondary diagnosis results of the technology of the present invention under CLTC working conditions and HYWCOL working conditions are shown. It can be seen that the technology accurately diagnosed the internal short circuit, external short circuit and poor contact faults occurring in different time periods under the above two working conditions, and no false alarms occurred. This verifies the robustness of the technology of the present invention.

[0084] Embodiment 2:

[0085] like Figure 8 As shown, this implementation provides a lithium-ion battery fault classification diagnosis system based on self-attenuation interval abnormality, including:

[0086] The sample entropy and correlation coefficient calculation unit is configured to: for any battery cell, collect the voltage sequence of the battery cell and the module mean voltage sequence, and calculate the sample entropy of the battery cell and the correlation coefficient between the voltage sequence and the module mean voltage sequence;

[0087] A battery self-decay interval abnormality calculation unit is configured to: calculate the battery self-decay interval abnormality of the battery cell according to the sample entropy and the correlation coefficient;

[0088] The primary diagnosis unit is configured to: determine that the battery cell is normal when the sample entropy is less than or equal to the sample entropy threshold and the correlation coefficient is greater than or equal to the correlation coefficient threshold; determine that the battery cell is faulty when the sample entropy is greater than the sample entropy threshold or the correlation coefficient is less than the correlation coefficient threshold;

[0089] The secondary diagnostic unit is configured to: when determining that the battery cell is faulty, compare the abnormality degree of the battery self-attenuation interval with a set abnormality threshold; when the abnormality degree of the battery self-attenuation interval is less than the set abnormality threshold, finally determine that the battery cell is faulty; when the abnormality degree of the battery self-attenuation interval is equal to the set abnormality threshold, the battery cell is not faulty.

[0090] The specific working method of the system is described in Example 1 and will not be repeated here.

[0091] It is understandable that the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (some) of the units can be further divided into multiple functionally smaller units to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the fault classification diagnosis system can also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0092] According to another embodiment of the present application, the system described in this embodiment can be constructed and the fault classification diagnosis method of the embodiment of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general computing device such as a computer including processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0093] Embodiment 3:

[0094] like Fig. 9As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0095] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0096] Processor 1001 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0097] The processor 1001 is configured to execute the following process:

[0098] For any battery cell, collect the voltage sequence of the battery cell and the module mean voltage sequence, calculate the sample entropy of the battery cell, and the correlation coefficient between the voltage sequence and the module mean voltage sequence;

[0099] Calculating the battery self-attenuation interval abnormality of the battery cell according to the sample entropy and the correlation coefficient;

[0100] When the sample entropy is less than or equal to the sample entropy threshold, and the correlation coefficient is greater than or equal to the correlation coefficient threshold, it is determined that the battery cell is normal; when the sample entropy is greater than the sample entropy threshold, or the correlation coefficient is less than the correlation coefficient threshold, it is determined that the battery cell is faulty;

[0101] When determining that the battery cell is faulty, the battery self-attenuation interval abnormality is compared with a set abnormality threshold. When the battery self-attenuation interval abnormality is less than the set abnormality threshold, the battery cell is finally determined to be faulty; when the battery self-attenuation interval abnormality is equal to the set abnormality threshold, the battery cell is not faulty.

[0102] The specific working method of the system is described in Example 1 and will not be repeated here.

[0103] Embodiment 4:

[0104] This implementation provides a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides a storage space that stores the processing system of the electronic device.

[0105] In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage; optionally, it may also be at least one computer-readable storage medium located away from the aforementioned processor.

[0106] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process:

[0107] For any battery cell, collect the voltage sequence of the battery cell and the module mean voltage sequence, calculate the sample entropy of the battery cell, and the correlation coefficient between the voltage sequence and the module mean voltage sequence;

[0108] Calculating the battery self-attenuation interval abnormality of the battery cell according to the sample entropy and the correlation coefficient;

[0109] When the sample entropy is less than or equal to the sample entropy threshold, and the correlation coefficient is greater than or equal to the correlation coefficient threshold, it is determined that the battery cell is normal; when the sample entropy is greater than the sample entropy threshold, or the correlation coefficient is less than the correlation coefficient threshold, it is determined that the battery cell is faulty;

[0110] When determining that the battery cell is faulty, the battery self-attenuation interval abnormality is compared with a set abnormality threshold. When the battery self-attenuation interval abnormality is less than the set abnormality threshold, the battery cell is finally determined to be faulty; when the battery self-attenuation interval abnormality is equal to the set abnormality threshold, the battery cell is not faulty.

[0111] The specific working method of the system is described in Example 1 and will not be repeated here.

[0112] Embodiment 5:

[0113] The present implementation provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the following process:

[0114] For any battery cell, collect the voltage sequence of the battery cell and the module mean voltage sequence, calculate the sample entropy of the battery cell, and the correlation coefficient between the voltage sequence and the module mean voltage sequence;

[0115] Calculating the battery self-attenuation interval abnormality of the battery cell according to the sample entropy and the correlation coefficient;

[0116] When the sample entropy is less than or equal to the sample entropy threshold, and the correlation coefficient is greater than or equal to the correlation coefficient threshold, it is determined that the battery cell is normal; when the sample entropy is greater than the sample entropy threshold, or the correlation coefficient is less than the correlation coefficient threshold, it is determined that the battery cell is faulty;

[0117] When determining that the battery cell is faulty, the battery self-attenuation interval abnormality is compared with a set abnormality threshold. When the battery self-attenuation interval abnormality is less than the set abnormality threshold, the battery cell is finally determined to be faulty; when the battery self-attenuation interval abnormality is equal to the set abnormality threshold, the battery cell is not faulty.

[0118] The specific working method of the system is described in Example 1 and will not be repeated here.

[0119] A person skilled in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0120] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instruction can be stored in a computer-readable storage medium or transmitted by a computer-readable storage medium. The computer instruction can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (for example, coaxial cable, optical fiber, digital line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server, a data center, etc. that contains one or more available media integration. Available media can be magnetic media (for example, floppy disk, hard disk, tape), optical media (for example, DVD), or semiconductor media (for example, solid-state drive (Solid State Disk, SSD)) and the like.

[0121] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality, characterized in that: The process includes: For any battery cell, collect the voltage sequence of the battery cell and the module mean voltage sequence, calculate the sample entropy of the battery cell, and the correlation coefficient between the voltage sequence and the module mean voltage sequence; Calculating the battery self-attenuation interval abnormality of the battery cell according to the sample entropy and the correlation coefficient; When the sample entropy is less than or equal to the sample entropy threshold, and the correlation coefficient is greater than or equal to the correlation coefficient threshold, it is determined that the battery cell is normal; When the sample entropy is greater than a sample entropy threshold, or the correlation coefficient is less than a correlation coefficient threshold, determining that the battery cell is faulty; When determining that the battery cell is faulty, the battery self-attenuation interval abnormality is compared with a set abnormality threshold. When the battery self-attenuation interval abnormality is less than the set abnormality threshold, the battery cell is finally determined to be faulty; when the battery self-attenuation interval abnormality is equal to the set abnormality threshold, the battery cell is not faulty.

2. The lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality as claimed in claim 1, characterized in that: Collect the voltage sequence of battery cell n And the module mean voltage series u n (t) is the battery cell voltage of cell n at time t, is the mean voltage of the entire battery module at time t, N is the voltage sequence length, and m is the moving time window width; Calculate voltage deviation series definition and Distance: According to the calculated voltage deviation sequence distance of battery cell n, statistics The number of A i , where r is the similarity tolerance of sample entropy; Calculate the voltage deviation series of two monomers Probability of matching m points: Increase the dimension to m+1, statistics The distance is denoted as B i , Calculate the voltage deviation series of two monomers Probability of matching m+1 points: Calculate sample entropy, 3. The lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality as claimed in claim 1, characterized in that: calculate and Correlation coefficient: Where COV(t) is and The covariance of , σ1(t) and σ2(t) are and The standard deviation of , m is the width of the moving time window.

4. The lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality as claimed in claim 1, characterized in that: Abnormality of battery self-attenuation interval: Where FV(t) = {exp[SE(t)]-1}[1-CORR(t)] α , α is the abnormal scaling factor, CORR(t) and SE(t) are the correlation coefficient and sample entropy of the monomer at time t, respectively. t0 is the starting time, β is the self-attenuation factor, and i is a time between t0 and t.

5. A lithium-ion battery fault classification diagnosis system based on self-attenuation interval abnormality, characterized in that: include: The sample entropy and correlation coefficient calculation unit is configured to: for any battery cell, collect the voltage sequence of the battery cell and the module mean voltage sequence, and calculate the sample entropy of the battery cell and the correlation coefficient between the voltage sequence and the module mean voltage sequence; A battery self-decay interval abnormality calculation unit is configured to: calculate the battery self-decay interval abnormality of the battery cell according to the sample entropy and the correlation coefficient; The primary diagnosis unit is configured to: determine that the battery cell is normal when the sample entropy is less than or equal to a sample entropy threshold and the correlation coefficient is greater than or equal to a correlation coefficient threshold; When the sample entropy is greater than a sample entropy threshold, or the correlation coefficient is less than a correlation coefficient threshold, determining that the battery cell is faulty; The secondary diagnostic unit is configured to: when determining that the battery cell is faulty, compare the abnormality degree of the battery self-attenuation interval with a set abnormality threshold; when the abnormality degree of the battery self-attenuation interval is less than the set abnormality threshold, finally determine that the battery cell is faulty; when the abnormality degree of the battery self-attenuation interval is equal to the set abnormality threshold, the battery cell is not faulty.

6. The lithium-ion battery fault classification diagnosis system based on self-attenuation interval abnormality as claimed in claim 5, characterized in that: In the sample entropy and correlation coefficient calculation unit, the voltage sequence of battery cell n is collected And the module mean voltage series u n (t) is the battery cell voltage of cell n at time t, is the mean voltage of the entire battery module at time t, N is the voltage sequence length, and m is the moving time window width; Calculate voltage deviation series definition and Distance: According to the calculated voltage deviation sequence distance of battery cell n, statistics The number of A i , where r is the similarity tolerance of sample entropy; Calculate the voltage deviation series of two monomers Probability of matching m points: Increase the dimension to m+1, statistics The distance is denoted as B i , Calculate the voltage deviation series of two monomers Probability of matching m+1 points: Calculate sample entropy, 7. The lithium-ion battery fault classification diagnosis system based on self-attenuation interval abnormality as claimed in claim 1, characterized in that: In the battery self-attenuation interval abnormality calculation unit, the battery self-attenuation interval abnormality: Where FV(t) = {exp[SE(t)]-1}[1-CORR(t)] α , α is the abnormal scaling factor, CORR(t) and SE(t) are the correlation coefficient and sample entropy of the monomer at time t, respectively. t0 is the starting time, β is the self-attenuation factor, and i is a time between t0 and t.

8. A computer device, characterized in that: include: A processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the lithium-ion battery fault classification diagnosis method based on the self-attenuation interval abnormality as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the lithium-ion battery fault classification diagnosis method based on self-attenuation interval abnormality as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the method for hierarchical diagnosis of lithium-ion battery faults based on self-attenuation interval abnormality as claimed in any one of claims 1 to 6 is implemented.

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