Fault detection method and device of lithium ion battery and electronic equipment

Abstract

The invention relates to a lithium ion battery fault detection method and apparatus, and an electronic device. The method comprises the steps of obtaining first voltage data of single batteries in a battery cluster; based on the first voltage data, dividing the battery monomers into at least one battery module, and obtaining a correlation coefficient between any two battery monomers in the battery module; according to the correlation coefficient, determining an abnormal single battery in the battery module; obtaining a voltage mean value of an abnormal battery module where the abnormal battery monomer is located; and according to the first voltage data and the voltage mean value, determining a fault detection result of the abnormal single battery, so that the abnormal single battery can be screened out through the correlation coefficient, the fault detection range is reduced, the fault detection efficiency is improved, quantitative analysis is performed on the first voltage data and the voltage mean value, and the fault detection accuracy is improved. The short-circuit fault and the open-circuit fault can be identified more accurately, and the accuracy and the reliability of fault detection are remarkably improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of battery fault detection, and in particular to a method, device and electronic equipment for detecting a fault of a lithium-ion battery. Background Art

[0002] With the rapid development of renewable energy technology, lithium batteries are increasingly used as an efficient and environmentally friendly energy storage solution. However, lithium batteries may face a variety of faults during use, among which short circuit faults and open circuit faults are two common and dangerous types of faults, which not only affect the performance of the battery, but may also lead to safety accidents. Therefore, how to accurately and efficiently detect faults in lithium-ion batteries has become an urgent problem to be solved. Summary of the invention

[0003] The present disclosure provides a lithium ion battery fault detection method, device and electronic device to at least solve the problems of poor accuracy and low efficiency of lithium ion battery fault detection in the related art.

[0004] The technical solution of the present disclosure is as follows:

[0005] According to a first aspect of an embodiment of the present disclosure, a fault detection method for a lithium-ion battery is provided, the method comprising: obtaining first voltage data of a battery cell in a battery cluster; based on the first voltage data, dividing the battery cell into at least one battery module, and obtaining a correlation coefficient between any two battery cells in the battery module; determining an abnormal battery cell in the battery module according to the correlation coefficient; obtaining a voltage mean of the abnormal battery module where the abnormal battery cell is located; and determining a fault detection result of the abnormal battery cell according to the first voltage data and the voltage mean.

[0006] According to a second aspect of an embodiment of the present disclosure, a fault detection device for a lithium-ion battery is provided, comprising: a first acquisition module, used to acquire first voltage data of a battery cell in a battery cluster; a second acquisition module, used to divide the battery cell into at least one battery module based on the first voltage data, and acquire a correlation coefficient between any two battery cells in the battery module; a determination module, used to determine an abnormal battery cell in the battery module according to the correlation coefficient; a third acquisition module, used to acquire a voltage mean of the abnormal battery module where the abnormal battery cell is located; and a fault detection module, used to determine a fault detection result of the abnormal battery cell according to the first voltage data and the voltage mean.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement the lithium-ion battery fault detection method as described in the first aspect of the embodiment of the present disclosure.

[0008] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the lithium-ion battery fault detection method as described in the first aspect of the embodiment of the present disclosure.

[0009] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0010] The embodiment of the present disclosure obtains first voltage data of battery cells in a battery cluster, divides the battery cells into at least one battery module based on the first voltage data, obtains a correlation coefficient between any two battery cells in the battery module, determines abnormal battery cells in the battery module according to the correlation coefficient, obtains a voltage mean of the abnormal battery module where the abnormal battery cells are located, and determines a fault detection result of the abnormal battery cells according to the first voltage data and the voltage mean. Thus, the present disclosure can screen out abnormal battery cells through the correlation coefficient, narrow the scope of fault detection, improve the efficiency of fault detection, and quantitatively analyze the first voltage data and the voltage mean, so as to more accurately identify short circuit faults and open circuit faults, and significantly improve the accuracy and reliability of fault detection.

[0011] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0013] Figure 1 The figure is a flow chart showing a method for detecting a fault of a lithium-ion battery according to an exemplary embodiment.

[0014] Figure 2 is a flow chart showing a method for detecting a fault of a lithium-ion battery according to another exemplary embodiment.

[0015] Figure 3 The figure is a block diagram of a lithium-ion battery fault detection device according to an exemplary embodiment.

[0016] Figure 4It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0017] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0019] The following embodiments describe in detail the lithium-ion battery fault detection method, device and electronic device proposed in the present disclosure.

[0020] Figure 1 A schematic flow chart of a lithium-ion battery fault detection method provided in an embodiment of the present disclosure.

[0021] like Figure 1 As shown, the lithium-ion battery fault detection method proposed in this embodiment includes the following steps:

[0022] S101, obtaining first voltage data of battery cells in a battery cluster.

[0023] In an embodiment of the present disclosure, before obtaining the first voltage data of the battery cells in the battery cluster, the operation data of the battery cells in the battery cluster during a historical period can be collected, wherein the operation data carries a timestamp, and the operation data is resampled according to a preset time sliding window to obtain target operation data, wherein the target operation data includes at least target current data, target voltage data and target state of charge (State of Charge, SOC for short) data.

[0024] For example, for a 280Ah lithium-ion battery energy storage system, the battery management system can be used to collect historical daily operating data of battery cells in the battery cluster under different environments and operating cycles. Different data in the operating data often have different corresponding timestamps. Therefore, the operating data can be resampled according to the preset time sliding window to obtain the target operating data.

[0025] It should be noted that the present disclosure does not limit the setting of the time sliding window. Optionally, the time sliding window can be set to 5S.

[0026] Optionally, in the process of resampling the running data according to the preset time sliding window, if a null value occurs, it is filled with the previous non-null value.

[0027] In the embodiment of the present disclosure, the state of the battery cell can be obtained based on the target current data and the preset current threshold, wherein the state includes at least a charging state, a static state and a discharging state. In response to the state of the battery cell being a charging state, the target voltage data within a preset time period before the charging termination voltage is used as the first voltage data.

[0028] It should be noted that the present disclosure does not limit the setting of the preset current threshold.

[0029] For example, for the target current data I, when the target current data I is less than -5A (current threshold), that is, when I<-5A, the state of the battery cell is the charging state; for the target current data I, when the absolute value of the target current data I is less than 5A (current threshold), that is, when |I|<5A, the state of the battery cell is the static state; for the target current data I, when the target current data I is greater than 5A, that is, when I>5A, the state of the battery cell is the discharging state.

[0030] In the embodiment of the present disclosure, in response to the battery cell being in a charging state, the target voltage data within a preset period before the charging termination voltage is used as the first voltage data.

[0031] It should be noted that the present disclosure does not limit the setting of the preset time period. Optionally, the preset time period can be set to 30 minutes.

[0032] S102: Based on the first voltage data, the battery cells are divided into at least one battery module, and a correlation coefficient between any two battery cells in the battery module is obtained.

[0033] Among them, the battery module is a modular unit composed of battery cells.

[0034] Optionally, the property information of the battery cell may be determined based on the first voltage data, and the battery cell may be divided into at least one battery module according to the property information of the battery cell, wherein the battery module includes at least two battery cells.

[0035] It should be noted that the present disclosure does not limit the type of correlation coefficient. For example, the correlation coefficient may be a Pearson correlation coefficient.

[0036] For example, if the correlation coefficient is the Pearson correlation coefficient, the correlation between any two battery cells in the battery module can be calculated based on the calculation formula of the Pearson correlation coefficient to obtain the correlation coefficient between any two battery cells in the battery module.

[0037] S103. Determine the abnormal battery cell in the battery module according to the correlation coefficient.

[0038] In the embodiment of the present disclosure, for any battery cell in the battery module, sum the correlation coefficients associated with any battery cell, and obtain the sum value of the correlation coefficients. In response to the sum value of the correlation coefficients being less than the correlation coefficient threshold, determine any battery cell in the battery module as an abnormal battery cell.

[0039] Among them, the correlation coefficient threshold R is obtained through multiple tests.

[0040] For example, for battery module 1, if battery module 1 includes battery cell 1, battery cell 2, battery cell 3, battery cell 4, and battery cell 5, for battery cell 1 in battery module 1, the correlation coefficient between battery cell 1 and battery cell 2 is r 12 , the correlation coefficient between battery cell 1 and battery cell 3 is r 13 , the correlation coefficient between battery cell 1 and battery cell 4 is r i4 , the correlation coefficient between battery cell 1 and battery cell 5 is r 15 , the correlation coefficients associated with battery cell 1 are r 12 , r 13 , r 14 and r 15 , if r 12 + r 13 + r 14 + r 15 < R, then it is determined that battery cell 1 in battery module 1 is abnormal.

[0041] S104. Obtain the voltage average value of the abnormal battery module where the abnormal battery cell is located.

[0042] In the implementation of the present disclosure, after obtaining the abnormal battery cell, the battery module where the abnormal battery cell is located is the abnormal battery module. The voltage average value of the abnormal battery module where the abnormal battery cell is located can be obtained based on the first voltage data corresponding to each battery cell in the abnormal battery module.

[0043] For example, if battery module 1 is an abnormal battery module, the first voltage data U1 of battery cell 1, the first voltage data U2 of battery cell 2, the first voltage data U3 of battery cell 3, the first voltage data U4 of battery cell 4, and the first voltage data U5 of battery cell 5, the voltage average value of abnormal battery module 1 is

[0044] S105 , determining a fault detection result of an abnormal battery cell according to the first voltage data and the voltage average.

[0045] It should be noted that in the related art, (1) a battery system short circuit fault diagnosis method based on correlation coefficient includes the following steps: S1, collecting the voltage of the series-connected batteries in the battery system; S2, adding the designed periodic signal to the collected voltage; S3, calculating the moving correlation coefficient of the adjacent numbered batteries, including the first and last batteries; S4, comparing the obtained moving correlation coefficient and comparing it with the preset threshold value. If it is lower than the threshold, an alarm is sent; S5, by analyzing the number of the abnormal correlation coefficient, the location of the faulty battery is determined, and a fault alarm is issued. However, the above method mainly focuses on the early voltage fluctuation identification of short circuit faults. For other types of battery faults, such as open circuit, aging or performance degradation, it may not be effectively detected, which limits its scope of application. Secondly, although this method claims that it does not require a battery model, in actual applications, it is still necessary to add a periodic signal to ensure that no false alarm is issued when the battery is stationary, which increases the complexity and implementation difficulty of the system. Finally, this method relies on a preset threshold to determine the fault, and a fixed threshold may be difficult to adapt to changes in different battery states and operating conditions, affecting the accuracy and flexibility of diagnosis.

[0046] It should be noted that in the related art, (2) a method for detecting an internal short circuit in a power battery comprises: periodically updating the battery health SOH of the power battery, the SOH being updated according to at least one set of target historical charging data of the power battery, determining a first state of charge value after the power battery has finished operating according to the SOH, determining a first open circuit voltage value of the power battery according to the first state of charge value, and determining whether an internal short circuit occurs in the power battery according to a voltage difference between an actual open circuit voltage value of the power battery and the first open circuit voltage value. However, the above method mainly relies on the estimation of the SOH and the comparison of historical data, which may be affected by the accuracy of data collection, the integrity of historical data and changes in battery usage conditions, resulting in inaccurate or unstable detection results. Secondly, the method focuses on the detection of internal short circuits, and may not be able to effectively identify other types of faults, such as battery aging, external short circuits or performance degradation, thereby limiting its comprehensive diagnostic capability. Furthermore, frequent updates of the SOH and complex data analysis may require high computing resources and time costs, which are not conducive to real-time monitoring and rapid response.

[0047] It should be noted that in the related art, (3) a method for diagnosing micro-short circuit faults of lithium-ion battery packs that takes inconsistency into account. It includes: obtaining the median terminal voltage of the lithium battery in the constant current charging stage; obtaining the median IC curve based on the median terminal voltage, and detecting abnormal cells based on the median IC curve; obtaining the fault judgment result according to the IC curve spectrum of the abnormal cell and the median IC curve spectrum, and the fault judgment result includes: micro-short circuit fault and parameter inconsistency; if the fault judgment result is a micro-short circuit fault, the short-circuit resistance of the faulty cell is calculated. However, the above method mainly relies on the analysis of the median terminal voltage and IC curve in the constant current charging stage, and may not be able to effectively detect faults under other working conditions such as variable current or pulse charging, which limits its scope of application. Secondly, although this method This method reduces the computational burden, but its fault judgment results are limited to micro-short circuit faults and parameter inconsistencies. It may not be able to accurately identify other types of faults such as battery aging, external short circuits or performance degradation, affecting the comprehensive diagnostic capability. Furthermore, this method requires the pre-acquisition and analysis of the median IC curve, which increases the complexity of data processing and analysis and may lead to insufficient real-time performance. In addition, this method assumes that the difference between the IC curve spectrum of the abnormal monomer and the median IC curve spectrum is mainly caused by micro-short circuits, but in actual applications, other factors (such as temperature changes, contact resistance, etc.) may also affect the IC curve, thereby interfering with the accuracy of fault judgment.

[0048] In the implementation of the present disclosure, the first target voltage data of the abnormal battery cell can be determined from the first voltage data, the voltage difference between the first target voltage data and the voltage mean can be obtained, and a voltage difference time series composed of the voltage differences can be obtained, and a change curve of the voltage difference time series can be obtained. According to the change curve, the fault detection result of the abnormal battery cell can be determined, wherein the fault detection result includes a short circuit fault and an open circuit fault.

[0049] Among them, the short circuit failure is caused by the existence of short-circuit resistance inside the battery, which in turn causes the self-discharge of the battery cell, resulting in the voltage of the short-circuited battery cell being significantly lower than the voltage level of the normal battery cell.

[0050] Among them, open circuit fault refers to the existence of an open circuit between battery cells, which causes the battery cell voltage to be higher than the voltage level of a normal battery cell.

[0051] In the embodiment of the present disclosure, the fault detection result of the abnormal battery cell may be determined according to the change trend of the change curve.

[0052] For example, if the changing trend of the change curve is continuously negative or gradually becomes negative, it indicates that the voltage of the abnormal battery cell continues to abnormally decrease to be lower than the voltage average of the abnormal battery module, and the fault detection result of the abnormal battery cell is determined to be a short circuit fault. If the changing trend of the change curve is continuously positive or gradually becomes positive, it indicates that the voltage of the abnormal battery cell continues to abnormally increase to be higher than the voltage average of the abnormal battery module, and the fault detection result of the abnormal battery cell is determined to be an open circuit fault.

[0053] In summary, the fault detection method for lithium-ion batteries provided in the embodiments of the present disclosure obtains first voltage data of battery cells in a battery cluster, divides the battery cells into at least one battery module based on the first voltage data, and obtains the correlation coefficient of any two battery cells in the battery module, determines the abnormal battery cells in the battery module according to the correlation coefficient, obtains the voltage mean of the abnormal battery module where the abnormal battery cells are located, and determines the fault detection result of the abnormal battery cells according to the first voltage data and the voltage mean. Therefore, the present disclosure can screen out abnormal battery cells through the correlation coefficient, narrow the scope of fault detection, improve the efficiency of fault detection, and quantitatively analyze the first voltage data and the voltage mean, which can more accurately identify short circuit faults and open circuit faults, and significantly improve the accuracy and reliability of fault detection.

[0054] Figure 2 A schematic flow chart of a lithium-ion battery fault detection method provided in an embodiment of the present disclosure.

[0055] like Figure 2 As shown, the lithium-ion battery fault detection method proposed in this embodiment includes the following steps:

[0056] S201, acquiring the state of the battery cell according to the target current data and the preset current threshold, wherein the state at least includes a charging state, a static state and a discharging state.

[0057] In an embodiment of the present disclosure, operating data of battery cells in a battery cluster during a historical period can be collected, wherein the operating data carries a timestamp, and the operating data is resampled according to a preset time sliding window to obtain target operating data, wherein the target operating data includes at least target current data, target voltage data and target state of charge data.

[0058] It should be noted that the target current data and the preset current threshold value can be used to intelligently determine the state of the battery cell, that is, to determine whether the state of the battery cell is a charging state, a static state, or a discharging state, which lays a solid foundation for the subsequent determination of the voltage point data.

[0059] S202 , in response to the battery cell being in a charging state, taking the target voltage data in a preset period before the charging termination voltage as the first voltage data.

[0060] S203: Based on the first voltage data, the battery cells are divided into at least one battery module, and a correlation coefficient between any two battery cells in the battery module is obtained.

[0061] In the embodiment of the present disclosure, for the battery cells i and the battery cells j in the battery module, based on the first voltage data, the first voltage average of the battery cell i and the second voltage average of the battery cell j are obtained, and according to the first voltage data and the first voltage average of the battery cell i, and the first voltage data and the second voltage average of the battery cell j, the correlation coefficient between the battery cell i and the battery cell j is obtained.

[0062] It should be noted that the correlation coefficient may be the Pearson correlation coefficient, which can be used as an indicator to measure the strength of the correlation and can effectively reveal changes in the internal state of the battery cells. The correlation coefficient ranges from -1 to 1. When the correlation coefficient is 1, it indicates that there is a completely positive correlation between the two battery cells. When the correlation coefficient is 0, it indicates that there is no linear correlation between the two battery cells.

[0063] In the embodiment of the present disclosure, the correlation coefficient between battery cell i and battery cell j can be obtained according to the following formula:

[0064]

[0065] Among them, r ij is the correlation coefficient between battery cell i and battery cell j, X i is the first voltage data of battery cell i, is the first voltage average value of battery cell i, Y i is the first voltage data of battery cell j, is the second voltage average value of battery cell j.

[0066] S204, determining abnormal battery cells in the battery module according to the correlation coefficient.

[0067] In an embodiment of the present disclosure, for battery cell i in a battery module, correlation coefficients associated with battery cell i are summed to obtain a sum of correlation coefficients. In response to the sum of correlation coefficients being less than a preset correlation coefficient threshold, battery cell i in the battery module is determined to be an abnormal battery cell.

[0068] It should be noted that, through the correlation coefficient sum value and the correlation coefficient threshold, abnormal battery cells in the battery module can be screened out, which effectively narrows the scope of subsequent analysis and improves the efficiency of the fault detection process.

[0069] S205 , determining first target voltage data of the abnormal battery cell from the first voltage data.

[0070] S206, obtaining a voltage difference between the first target voltage data and the voltage mean, and obtaining a voltage difference time series consisting of the voltage differences.

[0071] S207, obtaining a change curve of the voltage difference time series, and determining a fault detection result of the abnormal battery cell according to the change curve, wherein the fault detection result includes a short circuit fault and an open circuit fault.

[0072] It should be noted that, by performing deviation analysis on the voltage of the abnormal battery cell, that is, obtaining the change curve of the voltage difference time series, if the changing trend of the changing curve is continuously negative or gradually becomes negative, it indicates that the voltage of the abnormal battery cell continues to abnormally decrease to be lower than the voltage average of the abnormal battery module, and the fault detection result of the abnormal battery cell is determined to be a short circuit fault. If the changing trend of the changing curve is continuously positive or gradually becomes positive, it indicates that the voltage of the abnormal battery cell continues to abnormally increase to be higher than the voltage average of the abnormal battery module, and the fault detection result of the abnormal battery cell is determined to be an open circuit fault.

[0073] It should be noted that if the fault detection result of the abnormal battery cell is a short circuit fault, the natural voltage decay characteristics of the abnormal battery cell with a short circuit fault under a long-term static state are fully considered, and the voltage decay rate of the abnormal battery cell with a short circuit fault can be further analyzed. The fault detection result of the short circuit fault can be verified to ensure the accuracy and reliability of the fault detection result of the short circuit fault.

[0074] In the disclosed embodiment, second voltage data of battery cells in the battery cluster can be obtained, second target voltage data of the abnormal battery cells can be determined from the second voltage data, and first target state of charge data of the abnormal battery cells can be determined from the target state of charge data. Based on the second target voltage data and the first target state of charge data, the fault detection result of the short circuit fault is verified, and in response to the fault detection result of the short circuit fault passing the verification, the final fault detection result is determined to be a short circuit fault.

[0075] It should be noted that by verifying the fault detection results of short-circuit faults, not only the accuracy and reliability of micro-short circuit detection are improved, but also strong technical support can be provided for battery performance optimization and fault prevention.

[0076] It should be noted that the present disclosure does not limit the specific method of obtaining the second voltage data of the battery cells in the battery cluster.

[0077] Optionally, in response to the battery cell being in a stationary state and the duration of the stationary state being greater than a preset duration threshold, the target voltage data in the stationary state period is used as the second voltage data.

[0078] For example, in response to the battery cell being in a stationary state and the duration of the stationary state being greater than 3 hours (preset duration threshold), the target voltage data in the stationary state period is used as the second voltage data.

[0079] In the embodiment of the present disclosure, a pre-divided state of charge data interval can be obtained, and a slope threshold associated with the state of charge data interval can be obtained, a linear regression analysis can be performed on the second target voltage data to obtain a slope value, and a target state of charge data interval in which the first target state of charge data is located can be obtained, and a target slope threshold associated with the target state of charge data interval can be determined from the slope threshold, and the fault detection result of the short circuit fault can be verified based on the target slope threshold and the slope value.

[0080] Optionally, in response to the slope value being less than the target slope threshold, the fault detection result of the short circuit fault is verified, and in response to the slope value being greater than or equal to the target slope threshold, the fault detection result of the short circuit fault is not verified.

[0081] For example, the pre-divided state of charge data intervals may include [0, 30%] and (30%, 100%], the slope threshold associated with the state of charge data interval [0, 30%] is k1, for example: k1 is -0.0028v / h, and the slope threshold associated with the state of charge data interval (30%, 100%] is k2, for example: k2 is -0.0008v / h.

[0082] In the disclosed embodiment, after the fault detection result of the abnormal battery cell is obtained, an early warning reminder can be generated in time to remind the maintenance personnel, so that the maintenance personnel can locate the problem more accurately and implement targeted repair or replacement strategies.

[0083] In summary, the fault detection method of the lithium-ion battery provided by the embodiment of the present disclosure obtains the state of the battery cell according to the target current data and the preset current threshold value, wherein the state includes at least the charging state, the static state and the discharging state. In response to the state of the battery cell being the charging state, the target voltage data within the preset time period before the charging termination voltage is used as the first voltage data. Based on the first voltage data, the battery cell is divided into at least one battery module, and the correlation coefficient between any two battery cells in the battery module is obtained. According to the correlation coefficient, the abnormal battery cell in the battery module is determined, the first target voltage data of the abnormal battery cell is determined from the first voltage data, the voltage difference between the first target voltage data and the voltage mean is obtained, and the voltage difference time series composed of the voltage difference is obtained, and the change curve of the voltage difference time series is obtained. According to the change curve, the fault detection result of the abnormal battery cell is determined, wherein the fault detection result includes a short circuit fault and an open circuit fault. If the fault detection result of the abnormal battery cell is a short circuit fault, the second voltage data of the battery cell in the battery cluster is obtained, the second target voltage data of the abnormal battery cell is determined from the second voltage data, and the target voltage is obtained from the target voltage. The first target state of charge data of the abnormal battery cell determined in the standard state of charge data verifies the fault detection result of the short circuit fault according to the second target voltage data and the first target state of charge data. In response to the fault detection result of the short circuit fault being verified, the final fault detection result is determined to be a short circuit fault. Therefore, the present disclosure significantly improves the accuracy and comprehensiveness of fault detection by deeply analyzing and fault detecting multi-dimensional data such as current data, voltage data and SOC, and based on the correlation coefficient, the mean-based voltage deviation analysis method and the voltage decay rate. It can not only more accurately identify short circuit faults and open circuit faults, but also consider the influence of battery aging and environmental factors, improve the reliability and practicality of lithium battery fault detection, reduce false alarms and missed alarms, and can issue accurate warnings in the early stage of lithium battery micro-short circuits, buy time for the implementation of preventive measures, effectively reduce safety risks, and provide strong technical support for the maintenance and management of lithium batteries, which is conducive to improving the safety and reliability of lithium battery energy storage systems, and providing more accurate technical means for battery health management. It can be applied to electric vehicles, energy storage systems, portable electronic devices and other fields.

[0084] Figure 3 FIG. 1 is a block diagram of a lithium-ion battery fault detection device according to an exemplary embodiment. Figure 3 As shown, the lithium-ion battery fault detection device 300 of the embodiment of the present disclosure may specifically include: a first acquisition module 301 , a second acquisition module 302 , a determination module 303 , a third acquisition module 304 and a fault detection module 305 .

[0085] A first acquisition module 301 is used to acquire first voltage data of a battery cell in a battery cluster;

[0086] A second acquisition module 302, configured to divide the battery cells into at least one battery module based on the first voltage data, and acquire a correlation coefficient between any two battery cells in the battery module;

[0087] A determination module 303, configured to determine an abnormal battery cell in the battery module according to the correlation coefficient;

[0088] A third acquisition module 304 is used to acquire a voltage average of the abnormal battery module where the abnormal battery cell is located;

[0089] The fault detection module 305 is used to determine a fault detection result of an abnormal battery cell according to the first voltage data and the voltage mean value.

[0090] In one embodiment of the present disclosure, the device 300 is further used to: collect operating data of battery cells in the battery cluster within a historical period, wherein the operating data carries a timestamp; resample the operating data according to a preset time sliding window to obtain target operating data, wherein the target operating data includes at least target current data, target voltage data and target state of charge data.

[0091] In one embodiment of the present disclosure, the first acquisition module 301 is further used to: acquire the state of the battery cell according to the target current data and a preset current threshold, wherein the state includes at least a charging state, a static state and a discharging state; in response to the state of the battery cell being a charging state, use the target voltage data within a preset time period before the charging termination voltage as the first voltage data.

[0092] In one embodiment of the present disclosure, the fault detection module 305 is further used to: determine the first target voltage data of the abnormal battery cell from the first voltage data; obtain the voltage difference between the first target voltage data and the voltage mean, and obtain a voltage difference time series composed of the voltage difference; obtain a change curve of the voltage difference time series, and determine the fault detection result of the abnormal battery cell according to the change curve, wherein the fault detection result includes a short circuit fault and an open circuit fault.

[0093] In one embodiment of the present disclosure, the device 300 is also used to: if the fault detection result of the abnormal battery cell is a short circuit fault, obtain second voltage data of the battery cell in the battery cluster; determine second target voltage data of the abnormal battery cell from the second voltage data, and determine first target state of charge data of the abnormal battery cell from the target state of charge data; verify the fault detection result of the short circuit fault based on the second target voltage data and the first target state of charge data; in response to the fault detection result of the short circuit fault passing the verification, determine that the final fault detection result is a short circuit fault.

[0094] In one embodiment of the present disclosure, the device 300 is also used to: obtain a pre-divided state of charge data interval, and obtain a slope threshold associated with the state of charge data interval; perform a linear regression analysis on the second target voltage data to obtain a slope value; obtain the target state of charge data interval in which the first target state of charge data is located, and determine the target slope value associated with the state of charge data interval from the slope value; and verify the fault detection result of the short circuit fault based on the slope threshold and the target slope value.

[0095] In one embodiment of the present disclosure, the device 300 is further used to: in response to the state of the battery cell being a static state and the duration of the static state being greater than a preset duration threshold, use the target voltage data within the static state period as the second voltage data.

[0096] In one embodiment of the present disclosure, the second acquisition module 302 is further used to: for the battery cell i and the battery cell j in the battery module, based on the first voltage data, obtain the first voltage average value of the battery cell i and the second voltage average value of the battery cell j; and obtain the correlation coefficient between the battery cell i and the battery cell j according to the first voltage data and the first voltage average value of the battery cell i, and the first voltage data and the second voltage average value of the battery cell j.

[0097] In one embodiment of the present disclosure, the determination module 303 is further used to: for battery cell i in the battery module, sum the correlation coefficients associated with the battery cell i to obtain the sum of the correlation coefficients; in response to the sum of the correlation coefficients being less than a preset correlation coefficient threshold, determine that the battery cell i in the battery module is an abnormal battery cell.

[0098] In the embodiments of the present disclosure, the specific manner in which each module in the lithium-ion battery fault detection device of the above embodiments performs operations has been described in detail in the embodiments of the lithium-ion battery fault detection method, and will not be repeated here.

[0099] In summary, the fault detection device for lithium-ion batteries provided by the embodiments of the present disclosure obtains first voltage data of battery cells in a battery cluster, divides the battery cells into at least one battery module based on the first voltage data, and obtains the correlation coefficient of any two battery cells in the battery module, determines the abnormal battery cells in the battery module according to the correlation coefficient, obtains the voltage mean of the abnormal battery module where the abnormal battery cells are located, and determines the fault detection result of the abnormal battery cells according to the first voltage data and the voltage mean. Therefore, the present disclosure can screen out abnormal battery cells through the correlation coefficient, narrow the scope of fault detection, improve the efficiency of fault detection, and quantitatively analyze the first voltage data and the voltage mean, so as to more accurately identify short circuit faults and open circuit faults, and significantly improve the accuracy and reliability of fault detection.

[0100] In order to implement the above embodiment, Figure 4 As shown, the present disclosure also proposes an electronic device 1000, the device includes a memory 110, a processor 120, and a computer program stored in the memory and executable on the processor 120, when the processor 120 executes the program instructions, the execution is realized Figure 1 to Figure 2 Embodiments of a lithium-ion battery fault detection method.

[0101] In order to implement the above embodiments, the present disclosure also proposes a computer-readable storage medium.

[0102] When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device can perform the above-mentioned lithium-ion battery fault detection method. Optionally, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0103] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0104] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A lithium-ion battery fault detection method, characterized in that: The method comprises: Acquiring first voltage data of a battery cell in a battery cluster; Based on the first voltage data, the battery cells are divided into at least one battery module, and a correlation coefficient between any two battery cells in the battery module is obtained; Determining abnormal battery cells in the battery module according to the correlation coefficient; Obtaining a voltage average value of the abnormal battery module where the abnormal battery cell is located; A fault detection result of an abnormal battery cell is determined according to the first voltage data and the voltage average.

2. The method according to claim 1, characterized in that Before obtaining the first voltage data of the battery cells in the battery cluster, the method further includes: Collecting operation data of battery cells in the battery cluster in a historical period, wherein the operation data carries a timestamp; The operation data is resampled according to a preset time sliding window to obtain target operation data, wherein the target operation data at least includes target current data, target voltage data and target state of charge data.

3. The method according to claim 2, characterized in that The step of obtaining first voltage data of a battery cell in the battery cluster includes: According to the target current data and a preset current threshold, acquiring the state of the battery cell, wherein the state at least includes a charging state, a static state, and a discharging state; In response to the state of the battery cell being a charging state, target voltage data within a preset period before a charging termination voltage is used as first voltage data.

4. The method according to claim 1, characterized in that: The step of determining the fault detection result of the abnormal battery cell according to the first voltage data and the voltage mean value includes: determining first target voltage data of the abnormal battery cell from the first voltage data; Acquire a voltage difference between the first target voltage data and the voltage mean, and acquire a voltage difference time series composed of the voltage difference; A change curve of the voltage difference time series is obtained, and a fault detection result of the abnormal battery cell is determined according to the change curve, wherein the fault detection result includes a short circuit fault and an open circuit fault.

5. The method according to claim 4, characterized in that The method further comprises: If the fault detection result of the abnormal battery cell is a short circuit fault, obtaining second voltage data of the battery cell in the battery cluster; determining second target voltage data of the abnormal battery cell from the second voltage data, and determining first target state of charge data of the abnormal battery cell from the target state of charge data; verifying a fault detection result of a short circuit fault according to the second target voltage data and the first target state of charge data; In response to the fault detection result of the short circuit fault being verified, a final fault detection result is determined to be a short circuit fault.

6. The method according to claim 5, characterized in that The verifying the fault detection result of the short circuit fault according to the second target voltage data and the first target state of charge data includes: Acquire a pre-divided state of charge data interval, and acquire a slope threshold value associated with the state of charge data interval; Performing a linear regression analysis on the second target voltage data to obtain a slope value; Acquire a target state of charge data interval in which the first target state of charge data is located, and determine a target slope threshold value associated with the target state of charge data interval from the slope threshold values; The fault detection result of the short circuit fault is verified according to the target slope threshold and the target slope value.

7. The method according to claim 3, characterized in that The step of obtaining the second voltage data of the battery cells in the battery cluster further includes: In response to the state of the battery cell being a static state, and the duration of the static state being greater than a preset duration threshold, the target voltage data in the static state period is used as the second voltage data.

8. The method according to claim 1, characterized in that The obtaining of the correlation coefficient between any two battery cells in the battery module includes: For a battery cell i and a battery cell j in the battery module, based on the first voltage data, obtaining a first voltage average value of the battery cell i and a second voltage average value of the battery cell j; The correlation coefficient between the battery cell i and the battery cell j is obtained according to the first voltage data and the first voltage average of the battery cell i, and the first voltage data and the second voltage average of the battery cell j.

9. The method according to claim 8, characterized in that The step of determining an abnormal battery cell in the battery module according to the correlation coefficient includes: For a battery cell i in a battery module, summing the correlation coefficients associated with the battery cell i to obtain a sum of the correlation coefficients; In response to the correlation coefficient sum value being less than a correlation coefficient threshold, it is determined that the battery cell i in the battery module is an abnormal battery cell.

10. A lithium-ion battery fault detection device, characterized in that: The device comprises: A first acquisition module, used to acquire first voltage data of a battery cell in the battery cluster; a second acquisition module, configured to divide the battery cells into at least one battery module based on the first voltage data, and acquire a correlation coefficient between any two battery cells in the battery module; A determination module, configured to determine an abnormal battery cell in the battery module according to the correlation coefficient; A third acquisition module is used to acquire a voltage average of the abnormal battery module where the abnormal battery cell is located; A fault detection module is used to determine a fault detection result of an abnormal battery cell according to the first voltage data and the voltage mean.

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