Fault analysis method, device and equipment and computer readable storage medium

Abstract

The application discloses a fault analysis method, device and equipment and a computer readable storage medium. The fault analysis method comprises the following steps: performing data analysis based on a cloud database to determine a fault battery pack in which a single-cell voltage inconsistency phenomenon has occurred; comparing a state-of-charge extreme single cell and a health degree extreme single cell in the fault battery pack, and determining a cause of the single-cell voltage inconsistency phenomenon and an inconsistent single cell according to a comparison result; and when it is determined that the cause is an abnormally large self-discharge, determining a root cause of the abnormally large self-discharge according to a state-of-charge change trend of each single cell in the fault battery pack. The application can accurately locate a failure cause leading to single-cell voltage inconsistency, and simply and conveniently identify a root cause of abnormally large self-discharge of a single cell, so that closed-loop analysis of a fault cause can be quickly realized, product and process improvement is facilitated, and a series of problems possibly caused by root cause analysis through disassembly of a single cell are avoided.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a fault analysis method, apparatus, device, and computer-readable storage medium. Background Art

[0002] The most common cause of failure in lithium-ion battery packs used in electric vehicles is inconsistent cell voltage, that is, the voltage difference between different cells in the battery pack is large. Inconsistent cell voltage has a very large impact on the performance and reliability of the battery pack, which will directly lead to a decrease in the vehicle's cruising range. When the inconsistency reaches a certain level, the vehicle may even stop working, which in turn causes safety problems.

[0003] Currently, lithium-ion battery packs in electric vehicles can detect inconsistent cell voltages during use through on-board and cloud-based fault monitoring and alarm systems. However, they are unable to distinguish between the primary causes of cell voltage inconsistencies: abnormally high self-discharge and abnormal capacity decay. Excessive self-discharge is more likely to occur than abnormal capacity decay. However, existing techniques for root-cause analysis of inconsistencies caused by excessive cell self-discharge rely entirely on disassembly of the cells. From problem discovery to locating the root cause, the entire process requires a complex process of "booking a vehicle - changing the pack - unpacking - testing - disassembly - and analysis," which is time-consuming, costly, polluting, and poses safety risks. Summary of the Invention

[0004] The main purpose of this application is to provide a fault analysis method, device, equipment and computer-readable storage medium, which aims to accurately locate the failure cause that leads to inconsistent cell voltage, and to perform root cause analysis of the inconsistency caused by abnormally large cell self-discharge in a simple and convenient way.

[0005] To achieve the above objectives, the present application provides a fault analysis method, which includes the following steps:

[0006] Analyze data based on the cloud database to identify faulty battery packs that have experienced inconsistent cell voltages.

[0007] Comparing the state of charge extreme value cells and health extreme value cells in the faulty battery pack, and determining the cause of the cell voltage inconsistency phenomenon and the inconsistent cells based on the comparison results;

[0008] When it is determined that the cause is abnormally large self-discharge, the root cause of the abnormally large self-discharge is determined based on the charge state change trend of each cell in the faulty battery pack.

[0009] Optionally, the step of performing data analysis based on a cloud database to determine a faulty battery pack having a single cell voltage inconsistency phenomenon includes:

[0010] Obtaining the health of the battery pack and the health of each cell in the battery pack based on a cloud database;

[0011] Data analysis is performed based on the health of the battery pack and the health of each cell to determine a faulty battery pack in which cell voltage inconsistency has occurred.

[0012] Optionally, the step of obtaining the health of the battery pack and the health of each cell in the battery pack based on the cloud database includes:

[0013] Obtaining, based on a cloud database, the rated capacity of a battery pack, the state of charge of each cell in the battery pack, the charged capacity of each cell in the battery pack, and the difference in state of charge of each cell in the battery pack before and after charging;

[0014] Calculating the health of the battery pack based on the rated capacity, the state of charge of each cell, the charged capacity of each cell, and the difference in state of charge of each cell before and after charging;

[0015] The health of each cell is calculated according to the rated capacity, the charging capacity of each cell, and the difference in state of charge of each cell before and after charging.

[0016] Optionally, the step of performing data analysis based on the health of the battery pack and the health of each cell to determine a faulty battery pack having inconsistent cell voltages includes:

[0017] subtracting the maximum health value among the health values ​​of the cells from the health value of the battery pack to obtain a health value difference;

[0018] When the health difference is greater than a preset threshold, the battery pack is identified as a faulty battery pack with inconsistent cell voltages.

[0019] Optionally, the causes of the cell voltage inconsistency phenomenon include: abnormally large self-discharge and abnormal capacity attenuation;

[0020] The step of comparing the cells with the extreme state of charge values ​​and the cells with the extreme health values ​​in the faulty battery pack, and determining the cause of the cell voltage inconsistency and the inconsistent cells based on the comparison results, includes:

[0021] When the cell with the smallest state of charge before charging, the cell with the smallest state of charge after charging, and the cell with the highest health are all the same cell, determining that the cause of the cell voltage inconsistency is abnormally large self-discharge, and the cell with the highest health is regarded as an inconsistent cell;

[0022] When the cell with the smallest state of charge before charging, the cell with the largest state of charge after charging, and the cell with the smallest health are all the same cell, it is determined that the cause of the cell voltage inconsistency phenomenon is abnormal capacity attenuation, and the cell with the smallest health is regarded as an inconsistent cell.

[0023] Optionally, the step of determining the root cause of the abnormally large self-discharge according to the charge state change trend of each cell in the faulty battery pack includes:

[0024] Calculating the state of charge deviation of each cell in the faulty battery pack;

[0025] With date as the horizontal axis and the charge state deviation of each monomer as the vertical axis, a trend graph of the charge state deviation of each monomer over time is drawn;

[0026] Obtaining a graphical morphology of inconsistent states of charge of monomers based on the trend graph;

[0027] The root cause of the abnormally large self-discharge is determined based on the graphic morphology.

[0028] Optionally, the root causes of the abnormally large self-discharge include: short circuit in the battery cell and copper deposition on the negative electrode;

[0029] The step of determining the root cause of abnormally large self-discharge according to the graphic morphology includes:

[0030] When the rate of change of the graphic morphology is stable and greater than a preset rate of change, it is determined that the root cause of the abnormally large self-discharge is a short circuit in the battery cell;

[0031] When the rate of change of the graphical morphology decreases monotonically, it is determined that the root cause of the abnormally large self-discharge is copper deposition on the negative electrode.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a fault analysis device, which includes:

[0033] A fault location module, which is used to perform data analysis based on a cloud database to identify faulty battery packs that have experienced cell voltage inconsistency;

[0034] An extreme value comparison module, configured to compare the state of charge extreme value cells and health extreme value cells in the faulty battery pack, and determine the cause of the cell voltage inconsistency phenomenon and the inconsistent cells based on the comparison results;

[0035] The root cause analysis module is used to determine the root cause of the abnormal self-discharge according to the charge state change trend of each cell in the faulty battery pack when it is determined that the cause is abnormal self-discharge.

[0036] In addition, to achieve the above-mentioned purpose, the present application also provides a fault analysis device, which includes: a memory, a processor, and a fault analysis program stored on the memory and executable on the processor. When the fault analysis program is executed by the processor, the steps of the fault analysis method described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, on which a fault analysis program is stored. When the fault analysis program is executed by a processor, the steps of the fault analysis method described above are implemented.

[0038] The present application proposes a fault analysis method, device, equipment and computer-readable storage medium, which overcome the technical defects of the existing technology that it is unable to distinguish the main causes of inconsistent single-cell voltages, and there are many problems in the root cause analysis of the inconsistent phenomenon caused by abnormally large single-cell self-discharge. In the fault analysis method, data analysis is first performed based on a cloud database to determine a faulty battery pack in which cell voltage inconsistency has occurred, thereby quickly locating the faulty battery pack and the faulty vehicle. The state of charge extreme value cells and health extreme value cells in the faulty battery pack are then compared, and the cause of the cell voltage inconsistency and the inconsistent cells are determined based on the comparison results. The SOC of all cells in the faulty battery pack before and after charging is calculated, the cell numbers of the extreme SOCs are identified, and the maximum and minimum SOH values ​​of the cells are associated. Combined with the correspondence between SOC and SOH, it is possible to quickly determine whether the failure mode causing the cell voltage inconsistency is abnormal self-discharge or abnormal capacity attenuation. Finally, when it is determined that the cause is abnormal self-discharge, the root cause of the abnormal self-discharge is determined based on the state of charge change trend of each cell in the faulty battery pack. A trend graph of the SOC deviation of each cell over time is constructed, and based on the main features of the graph of the inconsistent cell SOC, it is possible to determine whether the root cause of the abnormal self-discharge is a short circuit in the cell or copper deposition on the negative electrode.

[0039] Compared with the existing technology, the present application can accurately locate the main fault cause of inconsistent cell voltage, and combined with the inconsistent SOC performance of the cell, it can accurately identify the two main causes of abnormally large cell self-discharge, namely short circuit in the battery cell and copper deposition on the negative electrode; the fault analysis method used in the present application to determine the failure cause of inconsistent cell voltage and locate the root cause of abnormally large self-discharge is simple and convenient, and can quickly realize closed-loop analysis of the fault cause, which is helpful to improve products and processes, and is of great significance to ensuring the performance of lithium-ion battery packs, improving reliability in operation, reducing the failure rate of electric vehicles and improving the safety of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 Schematic diagram of the application scenario of the fault analysis method provided in this application;

[0042] Figure 2 A flowchart of an embodiment of a fault analysis method provided by this application;

[0043] Figure 3 A battery pack SOC variation trend diagram related to an embodiment of the fault analysis method provided in this application;

[0044] Figure 4 Another battery pack SOC variation trend diagram involved in an embodiment of the fault analysis method provided in this application;

[0045] Figure 5 A flowchart of battery pack failure analysis in an application scenario corresponding to an embodiment of the failure analysis method provided in this application;

[0046] Figure 6 A battery pack SOC variation trend diagram for another application scenario corresponding to an embodiment of the fault analysis method provided in this application;

[0047] Figure 7 This is a verification result diagram of a single-unit disassembly analysis in another application scenario corresponding to the first embodiment of the fault analysis method provided in this application;

[0048] Figure 8 A schematic structural diagram of an embodiment of a fault analysis device provided by the present application;

[0049] Figure 9 This is a structural diagram of an embodiment of a fault analysis device provided by this application. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of methods and apparatus consistent with certain aspects of the present application, as detailed in the appended claims.

[0051] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the numbers used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0052] Existing technology can identify inconsistent cell voltages during use of lithium-ion battery packs through vehicle-side and cloud-based fault monitoring and alarm systems. However, it cannot distinguish between the primary causes of cell voltage inconsistencies: abnormally high self-discharge and abnormal capacity decay. Abnormally high self-discharge is more likely to occur than abnormal capacity decay. However, existing technology relies entirely on disassembling the cells to analyze the root cause of inconsistencies caused by abnormally high cell self-discharge. From problem discovery to locating the root cause, the entire process requires a complex process of "booking a car - changing the pack - unpacking - testing - disassembly - analysis," which is time-consuming, costly, polluting, and poses safety risks.

[0053] In response to the above-mentioned problems existing in the prior art, the present application provides a fault analysis method, device, equipment and computer-readable storage medium. The inventive concept of the fault analysis method provided in the present application is: calculating the SOH (Pack_SOH) of the battery pack and the SOH of all cells in the battery pack through the cloud, and screening out vehicles with inconsistent cells and their outlier cells by setting thresholds; calculating the SOC of all cells in the battery pack before and after charging, identifying the cell numbers of extreme SOCs, associating the maximum and minimum values ​​of the cell SOH, and locating the failure modes that cause cell inconsistency through the correspondence between SOC and SOH: abnormally large self-discharge and abnormal capacity attenuation; through the SOC calculation and inconsistency analysis of the cell over a longer time dimension, according to the graphical morphology of the inconsistent cell SOC results, locating the two major internal causes of abnormally large cell self-discharge: short circuit within the battery cell and copper deposition on the negative electrode.

[0054] The following describes exemplary application scenarios of the embodiments of the present application.

[0055] Figure 1 This is a schematic diagram of an application scenario of the fault analysis method provided in the embodiment of the present application, such as Figure 1As shown, the cloud 11 is a cloud platform based on the existence of the fault analysis device 12. When the vehicle 13 is charged by the vehicle charging device 14, the cloud 11 can collect the electrical data of the battery pack from the vehicle 13 and perform fault analysis on the battery pack of the vehicle 13 based on the acquired electrical data of the battery pack. It should be noted that although Figure 1 Only one vehicle is shown in the figure, but the cloud 11 can collect the electrical data of the battery packs of multiple vehicles at the same time and perform data analysis. When the cloud 11 performs data analysis on the electrical data of a battery pack and finds that the battery pack has inconsistent cell voltages, the battery pack is set as a faulty battery pack and the vehicle in which it is located is set as a faulty vehicle. Furthermore, the SOC (State of Charge, unit %) of all cells in the battery pack of the faulty vehicle before and after charging is obtained. It is the ratio of the remaining capacity of the battery after a period of use or long-term storage to its fully charged capacity, usually expressed as a percentage. Its value range is 0 to 1. When SOC=0, it means that the battery is fully discharged, and when SOC=1, it means that the battery is fully charged. The cell number of the extreme SOC is identified, and the associated cell SOH (State of Health, battery capacity, health, performance status, unit %, that is, the percentage of the fully charged capacity of the battery relative to the rated capacity, 100% for a new battery and 0% for a completely scrapped battery) maximum and minimum values. Through the correspondence between SOC and SOH, the failure mode causing the inconsistent cells is located: abnormally large self-discharge and abnormal capacity attenuation; when it is determined that the cause of the inconsistent cell voltage is abnormally large self-discharge, the cloud 11 calculates the SOC change trend of all cells in the faulty battery pack over time. By constructing a trend graph of the SOC deviation of each cell over time, based on the main features of the graph of inconsistent cell SOC, it can be known whether the root cause of the abnormally large self-discharge is a short circuit in the battery cell or copper deposition on the negative electrode.

[0056] The fault analysis device 12 can be configured as a device that can support the operation of the cloud platform, such as a server, a server cluster, a computer, etc. Figure 1 The fault analysis device 12 is illustrated by taking a server as an example, which is only an example description of the fault analysis device 12 and does not limit its function.

[0057] It should be noted that the above application scenarios are merely exemplary, and the fault analysis methods, apparatuses, devices, and computer-readable storage media provided in the embodiments of the present application include but are not limited to the above application scenarios.

[0058] The present application provides a fault analysis method, referring to Figure 2 , Figure 2 A flowchart of an embodiment of a fault analysis method provided by this application.

[0059] In this embodiment, the fault analysis method includes the following steps:

[0060] Step S10, performing data analysis based on the cloud database to determine the faulty battery pack that has experienced cell voltage inconsistency;

[0061] It should be noted that in this embodiment, the execution entity is the cloud, which relies on hardware carriers such as servers, server clusters, or computers. The cloud includes a vehicle monitoring and alarm system that can perform real-time analysis of data uploaded by the vehicle end to promptly detect whether the vehicle has any faults, helping the owner to preemptively eliminate potential hidden dangers during vehicle operation. The cloud database is used to store data uploaded by different vehicles at different times, such as the electrical data of the battery pack, rated capacity, electrical data of all single cells in the battery pack (a battery pack is composed of multiple single cells, and single cells in this embodiment refer to the single cells in the battery pack), and OCV (Open Circuit Voltage)-SOC comparison tables corresponding to the battery pack model. Based on the large amount of data stored in the database, the cloud can analyze whether the operating status of the battery packs of different vehicles is normal. When an anomaly is detected, the faulty battery pack with the abnormality can be screened out and a fault notification can be sent to the vehicle end or the owner's mobile terminal. In this embodiment, the main task is to identify whether the battery pack has inconsistent cell voltages and to find the faulty battery pack with inconsistent cell voltages and the corresponding faulty vehicle based on the cloud database.

[0062] Based on this, as a feasible embodiment, the above step S10 may include:

[0063] Step S11, obtaining the health of the battery pack and the health of each cell in the battery pack based on a cloud database;

[0064] Step S12 , performing data analysis based on the health of the battery pack and the health of each cell to determine a faulty battery pack with inconsistent cell voltages.

[0065] In this embodiment, the battery pack is judged whether the cell voltage inconsistency phenomenon occurs by obtaining the change of the battery pack electrical data within a charging period (for example, 1 hour or 2 hours, which is not limited in this embodiment). Specifically, the battery pack health Pack_SOH and the health SOHi of all cells in the battery pack (i is the number of the single cell) are calculated based on the change of the battery pack electrical data within the charging period. Then, data analysis is performed to determine whether the battery pack has the cell voltage inconsistency phenomenon.

[0066] Furthermore, as a feasible embodiment, the above step S11 may include:

[0067] Step S111, obtaining the rated capacity of the battery pack, the state of charge of each cell in the battery pack, the charged capacity of each cell in the battery pack, and the difference in state of charge of each cell in the battery pack before and after charging based on a cloud database;

[0068] Step S112, calculating the health of the battery pack based on the rated capacity, the state of charge of each cell, the charged capacity of each cell, and the difference in state of charge of each cell before and after charging;

[0069] Step S113 , calculating the health of each cell according to the rated capacity, the charged capacity of each cell, and the difference in state of charge of each cell before and after charging.

[0070] In this embodiment, in order to obtain the health status Pack_SOH of the battery pack and the health status SOHi of all cells in the battery pack, it is necessary to first obtain some parameters for calculating the health status, among which the rated capacity Q of the battery pack is the basic information of the battery pack and can be directly obtained; the state of charge SOCi of each single cell can be obtained based on the battery pack electrical data uploaded by the vehicle end and the OCV-SOC comparison table corresponding to the battery pack model, among which the state of charge SOCi of each single cell includes the state of charge SOCib before charging (i is the cell number, before is abbreviated as b for charging Before charging) and after charging, the state of charge SOCia (after is abbreviated as a for after charging); the charging capacity ΔAH of each monomer can be calculated using the ampere-hour integration method ∫IΔt / 3600. In the ampere-hour integration method, I is the real-time current in A, and Δt is the time interval between the two frames of current transmission. 3600 is 3600s, corresponding to 1h. By accumulating the ampere-hours charged in each frame, the charging capacity ΔAH of each monomer in the charging time can be obtained; the state of charge difference ΔSOCi before and after charging of each monomer can be obtained by SOCia-SOCib. After completing the acquisition of the above parameters, the health of the battery pack Pack_SOH and the health of each monomer in the battery pack SOHi can be calculated according to the following formula (here the battery pack is regarded as consisting of multiple monomers in series, so the rated capacity Q of the battery pack is the same as the rated capacity of each monomer):

[0071] Pack_SOH={min(SOCib*ΔAH / ΔSOCi)+ΔAH+min[(1-SOCia)*ΔAH / ΔSOCi]} / Q;

[0072] SOHi=ΔAH / ΔSOCi / Q.

[0073] It should be noted that the health of the battery pack is the ratio of the current full charge capacity of the battery pack divided by the rated capacity of the battery pack. The current full charge capacity of the battery pack is composed of three parts, namely the minimum capacity of each cell before charging, the cell charge capacity during the charging period, and the minimum capacity that each cell can receive after charging; the health of each cell in the battery pack is the ratio of the current full charge capacity of each cell divided by the rated capacity of the battery pack. The current full charge capacity of each cell is the ratio of the cell charge capacity during the charging period divided by the difference in charge state of each cell before and after charging.

[0074] Furthermore, as a feasible embodiment, the above step S12 may include:

[0075] Step S121, subtracting the maximum health value among the health values ​​of the cells from the health value of the battery pack to obtain a health value difference;

[0076] Step S122: When the health difference is greater than a preset threshold, the battery pack is identified as a faulty battery pack with inconsistent cell voltages.

[0077] In this embodiment, the purpose of obtaining the health of each cell is to screen out the cell with the highest health, whose maximum health SOHmax=max(ΔAH / ΔSOCi / Q), and the health difference ΔSOH=SOHmax-Pack_SOH.

[0078] As an example, this embodiment sets two thresholds k1 and k2, and provides a standard for judging the consistency of cells: if ΔSOH≤k1, the cell consistency is considered to be good; if ΔSOH is between (k1, k2), the cell consistency is considered to be in the early stage of degradation; if ΔSOH≥k2, the cell consistency is considered to be poor (corresponding to the phenomenon of inconsistent cell voltage); among them, k2 is greater than k1, and the thresholds k1 and k2 can be reasonably set according to different models of battery packs using the 3σ principle, and this embodiment does not impose any restrictions on this.

[0079] Step S20, comparing cells with extreme state of charge values ​​and cells with extreme health values ​​in the faulty battery pack, and determining the cause of the cell voltage inconsistency and the inconsistent cells based on the comparison results;

[0080] In this embodiment, a technical solution is provided for distinguishing the cause of the fault that causes the cell voltage inconsistency phenomenon. The cause of the cell voltage inconsistency phenomenon is determined by comparing the numbers of the cell with the largest SOC and the cell with the smallest SOH in the faulty battery pack, and based on the numbers of the inconsistent cells, it is known which cell in the faulty battery pack is the inconsistent cell.

[0081] Furthermore, as a feasible embodiment, the causes of the cell voltage inconsistency phenomenon include: abnormally large self-discharge and abnormal capacity attenuation. The above step S20 may include:

[0082] Step S21: When the cell with the smallest state of charge before charging, the cell with the smallest state of charge after charging, and the cell with the highest health are all the same cell, determining that the cell voltage inconsistency is caused by abnormally large self-discharge, and treating the cell with the highest health as an inconsistent cell;

[0083] In step S22, when the cell with the smallest state of charge before charging, the cell with the largest state of charge after charging, and the cell with the smallest health are all the same cell, it is determined that the cause of the cell voltage inconsistency phenomenon is abnormal capacity attenuation, and the cell with the smallest health is regarded as an inconsistent cell.

[0084] As an example, this embodiment provides a judgment standard as follows:

[0085] If Num_SOCmin before charging = Num_SOCmin after charging = Num_SOHmax, the inconsistency is caused by abnormally large self-discharge of the monomer, and the inconsistent monomer is the monomer numbered Num_SOHmax;

[0086] If Num_SOCmin before charging = Num_SOCmax after charging = Num_SOHmin, the inconsistency is caused by abnormal attenuation of the cell capacity, and the inconsistent cell is the cell numbered Num_SOHmin.

[0087] Among them, Num_SOCmin is the number of the minimum SOC unit, SOCmax is the number of the maximum SOC unit, Num_SOHmin is the number of the minimum SOH unit, and Num_SOHmax is the number of the maximum SOH unit.

[0088] It should be noted that, from the formula for calculating the monomer SOH in the above steps, it can be seen that since the charging capacity ΔAH of each monomer is the same, the rated capacity Q of the battery pack is a constant value. Therefore, the reason for the difference in the SOH of each monomer is the charge state change ΔSOCi of each monomer before and after charging. The health of the monomer with the smallest charge state change before and after charging is the maximum health. If its charge state before and after charging is still the minimum value among all monomers, it means that the monomer has an abnormally large self-discharge problem, which causes its charge state to always be the minimum charge state of all monomers; conversely, the health of the monomer with the largest charge state change before and after charging is the minimum health. If its charge state before charging is the minimum value among all monomers, but its charge state after charging is the maximum value among all monomers, it means that the monomer has an abnormal capacity attenuation problem, which causes its charge state to correspond to the minimum and maximum values ​​of all monomers before and after charging, respectively.

[0089] Step S30 : When it is determined that the cause is abnormal self-discharge, the root cause of the abnormal self-discharge is determined based on the charge state change trend of each cell in the faulty battery pack.

[0090] In this embodiment, a technical solution is provided for analyzing the root cause of abnormal self-amplification of a single cell without disassembling the battery pack. Specifically, the SOC of each single cell is calculated and analyzed for inconsistencies over a longer time dimension, and a corresponding trend graph is drawn. Then, the graphical morphology of the inconsistent results of the single cell SOC is obtained from the trend graph, and the root cause of the abnormally large self-discharge of the single cell is determined based on the main features of the inconsistent graphical morphology of the single cell SOC.

[0091] Furthermore, as a feasible embodiment, the step of determining the root cause of the abnormally large self-discharge according to the charge state change trend of each cell in the faulty battery pack in the above step S30 includes:

[0092] Step S31, calculating the state of charge deviation of each cell in the faulty battery pack;

[0093] Step S32, plotting a trend graph of the state of charge deviation of each cell over time, with date as the horizontal axis and the state of charge deviation of each cell as the vertical axis;

[0094] Step S33, obtaining a graphical morphology of inconsistent states of charge of cells based on the trend graph;

[0095] Step S34: determining the root cause of the abnormally large self-discharge according to the graphic morphology.

[0096] It should be noted that, in this embodiment, the cloud first constructs the SOC deviation of each cell in the battery pack, uses the SOC deviation of each cell as a reference indicator, and evaluates the consistency change of the cells in the battery pack based on the change trend of the indicator over time and the indicator value. Specifically, a change trend graph can be constructed to help monitor the consistency change of the cells. The steps of constructing the change trend graph may include:

[0097] (1) Data acquisition: From the daily operation data of the vehicle, filter out (pseudo) static data segments with current within the range of [-2, +2] A. This data segment contains the data acquisition time, the current value at each moment, and the voltage value information of all cells in the battery pack;

[0098] (2) Calculation of cell SOC deviation: According to the OCV-SOC curve (different battery models have different OCV-SOC curves, which can be obtained by looking up the information comparison table of the battery pack itself), all cell voltages in the acquired data are converted into cell SOC; the median SOC in each frame of data is taken as the benchmark, and the SOC of all other cells is subtracted from it to obtain the SOC deviation of the frame; the average deviation of each cell per day is calculated;

[0099] (3) With the date as the horizontal axis and the SOC deviation of all cells in the battery pack as the vertical axis, a trend graph of the change of cell SOC inconsistency over time is drawn.

[0100] Based on the experience database of battery failure analysis, it can be seen that different single-cell abnormal self-discharge failure modes have different SOC inconsistency trends. Therefore, after completing the construction of the trend graph, the root cause of the abnormal self-discharge corresponding to the inconsistent single cell can be determined based on the main characteristics of the graphical morphology of each single cell in the trend graph.

[0101] Furthermore, as a feasible embodiment, the root causes of the abnormally large self-discharge include: short circuit in the battery cell and copper deposition on the negative electrode. The above step S34 may specifically include:

[0102] Step S341, when the rate of change of the graphic morphology is stable and greater than a preset rate of change, determining that the root cause of the abnormally large self-discharge is a short circuit in the battery cell;

[0103] Step S342: When the rate of change of the graphic morphology decreases monotonically, it is determined that the root cause of the abnormally large self-discharge is copper deposition on the negative electrode.

[0104] As an example, this embodiment provides Figure 3 A battery pack SOC variation trend diagram shown in FIG. Figure 3It can be seen that the main characteristics of the SOC inconsistency graph are: the SOC deviation of a certain cell in the battery pack decreases rapidly over time and the trend is basically consistent, that is, the change rate of the graph is stable and greater than the preset change rate. The preset change rate can be set according to actual needs. Therefore, the cause of the deviation is the abnormally large self-discharge of the U93 cell, and the cause of the abnormally large self-discharge is the short circuit in the battery cell.

[0105] As an example, this embodiment also provides Figure 4 Another battery pack SOC change trend diagram shown is composed of Figure 4 It can be seen that the main characteristics of the SOC inconsistency graph are: the SOC deviation of a certain cell in the battery pack changes relatively slowly over time and the trend is gradually slowing down, that is, the rate of change of the graph morphology decreases monotonically. Therefore, the cause of this deviation is the abnormally large self-discharge of the U10 cell, and the cause of the abnormally large self-discharge is copper deposition on the negative electrode caused by over-discharge.

[0106] In addition, as an example, in an application scenario, for a certain battery pack, this embodiment can make the following Figure 5 The threshold setting and failure mode analysis shown in Figure 5 , Figure 5 The fault analysis flow chart for a battery pack provided in this embodiment is as follows: Figure 5 It can be seen that for this battery pack, the threshold k1 can be set to 5, and the threshold k2 can be set to 20. Based on the health difference calculation formula ΔSOH=SOHmax-Pack_SOH, it can be seen that when ΔSOH≤5, it means that the cell consistency of the battery pack is good; when 5<ΔSOH≤20, it means that the cell of the battery pack is in the early stage of consistency degradation; when ΔSOH≥20, it means that the cell consistency of the battery pack is seriously deteriorated. At this time, combined with the cell SOC and SOH to locate the cause, when the battery pack meets the Num_SOCmin before charging = Num_SOCmin after charging = Num_SOHmax, it means that the reason for the serious degradation of its cell consistency is abnormally large self-discharge, and combined with the SOC inconsistency trend to locate the root cause is short circuit in the battery cell or copper deposition on the negative electrode; when the battery pack meets the Num_SOCmin before charging = Num_SOCmax after charging = Num_SOHmin, it means that the reason for the serious degradation of its cell consistency is abnormal capacity attenuation.

[0107] In addition, as an example, in an application scenario, for a passenger electric vehicle equipped with a certain battery pack, the user complains that the driving range is insufficient and the vehicle-side alarm cell pressure difference is large. The fault analysis method provided in this embodiment is applied to this scenario as follows: (1) The cloud calculates Pack_SOH=73.69%, cell SOHmax=96.17%, and ΔSOH=22.48%, and determines that the consistency of the battery pack has seriously deteriorated; (2) Read the data of the vehicle's last charging process, identify the SOC extreme value cells before and after charging, and there is the following corresponding relationship: Num_SOCmin before charging=Nmu_SOCmin after charging=Num_SOHmax, and determine that the cell numbered Num_SOHmax is an inconsistent cell, and the reason for the inconsistency is that the cell self-discharge is abnormally large; (3) The data of the vehicle running for nearly 1 year is used to calculate the cell SOC and draw a SOC inconsistency change trend chart, such as Figure 6 As shown, according to Figure 6 It can be seen that the SOC deviation of the U91 cell in the battery pack decreases rapidly over time and the trend is basically consistent, that is, the rate of change of the graphical morphology is stable and greater than the preset rate of change. Therefore, the cause of the fault that causes the large voltage difference of the cell to be alarmed on the vehicle side can be located as the large voltage difference caused by the short circuit in the U91 cell, which in turn causes the cell voltage inconsistency phenomenon; (4) After the cause analysis is completed, after module replacement and cell disassembly analysis and verification, it is found that the cause of the failure is the short circuit in the cell caused by impurities inside the cell, such as Figure 7 The feasibility and accuracy of the fault analysis method provided by this embodiment have been verified through practical application in this application scenario.

[0108] This embodiment provides a fault analysis method to overcome the technical defects of the prior art, which is that the main causes of inconsistent cell voltages cannot be distinguished and the root cause analysis of the inconsistent phenomenon caused by abnormally large cell self-discharge has many problems. In the fault analysis method, data analysis is first performed based on a cloud database to determine a faulty battery pack in which cell voltage inconsistency has occurred, thereby quickly locating the faulty battery pack and the faulty vehicle. The state of charge extreme value cells and health extreme value cells in the faulty battery pack are then compared, and the cause of the cell voltage inconsistency and the inconsistent cells are determined based on the comparison results. The SOC of all cells in the faulty battery pack before and after charging is calculated, the cell numbers of the extreme SOCs are identified, and the maximum and minimum SOH values ​​of the cells are associated. Combined with the correspondence between SOC and SOH, it is possible to quickly determine whether the failure mode causing the cell voltage inconsistency is abnormal self-discharge or abnormal capacity attenuation. Finally, when it is determined that the cause is abnormal self-discharge, the root cause of the abnormal self-discharge is determined based on the state of charge change trend of each cell in the faulty battery pack. A trend graph of the SOC deviation of each cell over time is constructed, and based on the main features of the graph of the inconsistent cell SOC, it is possible to determine whether the root cause of the abnormal self-discharge is a short circuit in the cell or copper deposition on the negative electrode.

[0109] Compared with the existing technology, this embodiment can accurately locate the main fault cause of inconsistent cell voltage, and combined with the inconsistent SOC performance of the cell, it can accurately identify the two main causes of abnormally large cell self-discharge, namely short circuit within the battery cell and copper deposition on the negative electrode; the fault analysis method used in this embodiment to determine the failure cause of inconsistent cell voltage and locate the root cause of abnormally large self-discharge is simple and convenient, and can quickly realize closed-loop analysis of the fault cause, which is helpful to improve products and processes, and is of great significance to ensuring the performance of lithium-ion battery packs, improving reliability in operation, reducing the failure rate of electric vehicles and improving the safety of electric vehicles.

[0110] In addition, the present application also provides a fault analysis device, referring to Figure 8 , Figure 8 This is a structural diagram of an embodiment of a fault analysis device provided by the present application.

[0111] In this embodiment, the fault analysis device includes:

[0112] A fault location module 10 is configured to perform data analysis based on a cloud database to identify a faulty battery pack that has experienced a single cell voltage inconsistency;

[0113] An extreme value comparison module 20 is used to compare the state of charge extreme value cells and health extreme value cells in the faulty battery pack, and determine the cause of the cell voltage inconsistency phenomenon and the inconsistent cells based on the comparison results;

[0114] The root cause analysis module 30 is configured to determine the root cause of the abnormal self-discharge according to the charge state change trend of each cell in the faulty battery pack when it is determined that the cause is abnormal self-discharge.

[0115] As a feasible embodiment, the fault location module 10 includes:

[0116] A health acquisition submodule, configured to acquire the health of the battery pack and the health of each cell in the battery pack based on a cloud database;

[0117] The health analysis submodule is used to perform data analysis based on the health of the battery pack and the health of each cell to determine a faulty battery pack with inconsistent cell voltages.

[0118] As a feasible embodiment, the health acquisition submodule includes:

[0119] an acquisition unit, the acquisition unit being configured to acquire, based on a cloud database, the rated capacity of the battery pack, the state of charge of each cell in the battery pack, the charged capacity of each cell in the battery pack, and the difference in state of charge of each cell in the battery pack before and after charging;

[0120] a health calculation unit, configured to calculate the health of the battery pack based on the rated capacity, the state of charge of each cell, the charged capacity of each cell, and the difference in state of charge of each cell before and after charging;

[0121] The health calculation unit is further configured to calculate the health of each cell according to the rated capacity, the charging capacity of each cell, and the difference in state of charge of each cell before and after charging.

[0122] As a feasible embodiment, the health analysis submodule includes:

[0123] a difference calculation unit, configured to subtract a maximum health value among the health values ​​of the cells from the health value of the battery pack to obtain a health value difference;

[0124] A threshold comparison unit is used to identify the battery pack as a faulty battery pack with inconsistent cell voltages when the health difference is greater than a preset threshold.

[0125] As a feasible embodiment, the causes of the cell voltage inconsistency phenomenon include: abnormally large self-discharge and abnormal capacity attenuation;

[0126] The extreme value comparison module 20 is further configured to determine that the cause of the cell voltage inconsistency is abnormally large self-discharge when the cell with the smallest state of charge before charging, the cell with the smallest state of charge after charging, and the cell with the highest health are the same cell, and to regard the cell with the highest health as an inconsistent cell;

[0127] The extreme value comparison module 20 is also used to determine that the cause of the cell voltage inconsistency phenomenon is abnormal capacity attenuation when the cell with the smallest state of charge before charging, the cell with the largest state of charge after charging, and the cell with the smallest health are all the same cell, and regard the cell with the smallest health as an inconsistent cell.

[0128] As a feasible embodiment, the root cause analysis module 30 includes:

[0129] a deviation calculation submodule, configured to calculate a state of charge deviation of each cell in the faulty battery pack;

[0130] A graph drawing submodule, the graph drawing submodule is used to draw a trend graph of the state of charge deviation of each of the cells changing over time, using date as the horizontal axis and the state of charge deviation of each of the cells as the vertical axis;

[0131] an inconsistency capturing submodule, the inconsistency capturing submodule being used to obtain a graphical appearance of inconsistent states of charge of a cell based on the trend graph;

[0132] A root cause analysis submodule is used to determine a root cause of abnormally large self-discharge based on the graphic morphology.

[0133] As a feasible embodiment, the root causes of the abnormally large self-discharge include: short circuit in the battery cell and copper deposition on the negative electrode;

[0134] The root cause analysis submodule is further configured to determine that the root cause of the abnormally large self-discharge is a short circuit in the battery cell when the rate of change of the graphic morphology is stable and greater than a preset rate of change;

[0135] The root cause analysis submodule is further configured to determine that the root cause of the abnormally large self-discharge is copper deposition on the negative electrode when the rate of change of the graphic morphology decreases monotonically.

[0136] The expanded content of the specific implementation of the fault analysis device is basically the same as that of the above-mentioned embodiments of the fault analysis method. The fault analysis device can achieve the same technical effects as the above-mentioned embodiments of the fault analysis method, and will not be repeated here.

[0137] In addition, the present application also provides a fault analysis device, referring to Figure 9 , Figure 9 This is a schematic diagram of the structure of the fault analysis device involved in the embodiment of the present application.

[0138] like Figure 9 As shown, the fault analysis device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0139] Those skilled in the art will understand that Figure 9 The structure shown in the figure does not constitute a limitation to the fault analysis device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0140] like Figure 9 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module and a fault analysis program.

[0141] exist Figure 9 In the fault analysis device shown, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in this embodiment can be set in the fault analysis device, and the fault analysis device calls the fault analysis program stored in the memory 1005 through the processor 1001 and performs the following operations:

[0142] Analyze data based on the cloud database to identify faulty battery packs that have experienced inconsistent cell voltages.

[0143] Comparing the state of charge extreme value cells and health extreme value cells in the faulty battery pack, and determining the cause of the cell voltage inconsistency phenomenon and the inconsistent cells based on the comparison results;

[0144] When it is determined that the cause is abnormally large self-discharge, the root cause of the abnormally large self-discharge is determined based on the charge state change trend of each cell in the faulty battery pack.

[0145] Furthermore, the processor 1001 may call the fault analysis program stored in the memory 1005 and perform the following operations:

[0146] Obtaining the health of the battery pack and the health of each cell in the battery pack based on a cloud database;

[0147] Data analysis is performed based on the health of the battery pack and the health of each cell to determine a faulty battery pack in which cell voltage inconsistency has occurred.

[0148] Furthermore, the processor 1001 may call the fault analysis program stored in the memory 1005 and perform the following operations:

[0149] Obtaining, based on a cloud database, the rated capacity of a battery pack, the state of charge of each cell in the battery pack, the charged capacity of each cell in the battery pack, and the difference in state of charge of each cell in the battery pack before and after charging;

[0150] Calculating the health of the battery pack based on the rated capacity, the state of charge of each cell, the charged capacity of each cell, and the difference in state of charge of each cell before and after charging;

[0151] The health of each cell is calculated according to the rated capacity, the charging capacity of each cell, and the difference in state of charge of each cell before and after charging.

[0152] Furthermore, the processor 1001 may call the fault analysis program stored in the memory 1005 and perform the following operations:

[0153] subtracting the maximum health value among the health values ​​of the cells from the health value of the battery pack to obtain a health value difference;

[0154] When the health difference is greater than a preset threshold, the battery pack is identified as a faulty battery pack with inconsistent cell voltages.

[0155] Furthermore, the causes of the cell voltage inconsistency phenomenon include: abnormally large self-discharge and abnormal capacity attenuation. The processor 1001 may call a fault analysis program stored in the memory 1005 and perform the following operations:

[0156] When the cell with the smallest state of charge before charging, the cell with the smallest state of charge after charging, and the cell with the highest health are all the same cell, determining that the cause of the cell voltage inconsistency is abnormally large self-discharge, and the cell with the highest health is regarded as an inconsistent cell;

[0157] When the cell with the smallest state of charge before charging, the cell with the largest state of charge after charging, and the cell with the smallest health are all the same cell, it is determined that the cause of the cell voltage inconsistency phenomenon is abnormal capacity attenuation, and the cell with the smallest health is regarded as an inconsistent cell.

[0158] Furthermore, the processor 1001 may call the fault analysis program stored in the memory 1005 and perform the following operations:

[0159] Calculating the state of charge deviation of each cell in the faulty battery pack;

[0160] With date as the horizontal axis and the charge state deviation of each monomer as the vertical axis, a trend graph of the charge state deviation of each monomer over time is drawn;

[0161] Obtaining a graphical morphology of inconsistent states of charge of monomers based on the trend graph;

[0162] The root cause of the abnormally large self-discharge is determined based on the graphic morphology.

[0163] Furthermore, the root causes of the abnormally large self-discharge include: short circuit in the battery cell and copper deposition on the negative electrode. The processor 1001 may call the fault analysis program stored in the memory 1005 and perform the following operations:

[0164] When the rate of change of the graphic morphology is stable and greater than a preset rate of change, it is determined that the root cause of the abnormally large self-discharge is a short circuit in the battery cell;

[0165] When the rate of change of the graphical morphology decreases monotonically, it is determined that the root cause of the abnormally large self-discharge is copper deposition on the negative electrode.

[0166] In addition, an embodiment of the present application also proposes a computer-readable storage medium, which is applied to a computer. The computer-readable storage medium may include: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes. The computer-readable storage medium stores a fault analysis program, and when the fault analysis program is executed by the processor, the steps of the fault analysis method of the present application as described above are implemented.

[0167] The various embodiments of the fault analysis device and computer-readable storage medium of the present application can refer to the various embodiments of the fault analysis method of the present application, and will not be repeated here.

[0168] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0169] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0170] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0171] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A fault analysis method, characterized in that: The fault analysis method comprises the following steps: Analyze data based on the cloud database to identify faulty battery packs that have experienced inconsistent cell voltages. Comparing the state of charge extreme value cells and health extreme value cells in the faulty battery pack, and determining the cause of the cell voltage inconsistency phenomenon and the inconsistent cells based on the comparison result, including: When the cell with the smallest state of charge before charging, the cell with the smallest state of charge after charging, and the cell with the highest health are all the same cell, determining that the cause of the cell voltage inconsistency is abnormally large self-discharge, and the cell with the highest health is regarded as an inconsistent cell; When the cell with the smallest state of charge before charging, the cell with the largest state of charge after charging, and the cell with the smallest health are all the same cell, determining that the cause of the cell voltage inconsistency is abnormal capacity attenuation, and the cell with the smallest health is regarded as an inconsistent cell; When it is determined that the cause is abnormally large self-discharge, the root cause of the abnormally large self-discharge is determined based on the charge state change trend of each cell in the faulty battery pack.

2. The fault analysis method according to claim 1, wherein: The step of performing data analysis based on the cloud database to determine the faulty battery pack that has experienced cell voltage inconsistency includes: Obtaining the health of the battery pack and the health of each cell in the battery pack based on a cloud database; Data analysis is performed based on the health of the battery pack and the health of each cell to determine a faulty battery pack in which cell voltage inconsistency has occurred.

3. The fault analysis method according to claim 2, wherein: The step of obtaining the health of the battery pack and the health of each cell in the battery pack based on the cloud database includes: Obtaining, based on a cloud database, the rated capacity of a battery pack, the state of charge of each cell in the battery pack, the charged capacity of each cell in the battery pack, and the difference in state of charge of each cell in the battery pack before and after charging; Calculating the health of the battery pack based on the rated capacity, the state of charge of each cell, the charged capacity of each cell, and the difference in state of charge of each cell before and after charging; The health of each cell is calculated according to the rated capacity, the charging capacity of each cell, and the difference in state of charge of each cell before and after charging.

4. The fault analysis method according to claim 2, wherein: The step of performing data analysis based on the health of the battery pack and the health of each cell to determine a faulty battery pack with inconsistent cell voltages includes: subtracting the maximum health value among the health values ​​of the cells from the health value of the battery pack to obtain a health value difference; When the health difference is greater than a preset threshold, the battery pack is identified as a faulty battery pack with inconsistent cell voltages.

5. The fault analysis method according to any one of claims 1 to 4, characterized in that: The step of determining the root cause of the abnormally large self-discharge according to the charge state change trend of each cell in the faulty battery pack includes: Calculating the state of charge deviation of each cell in the faulty battery pack; With date as the horizontal axis and the charge state deviation of each monomer as the vertical axis, a trend graph of the charge state deviation of each monomer over time is drawn; Obtaining a graphical morphology of inconsistent states of charge of monomers based on the trend graph; The root cause of the abnormally large self-discharge is determined based on the graphic morphology.

6. The fault analysis method according to claim 5, wherein: The root causes of abnormally large self-discharge include: short circuit in the battery cell and copper deposition on the negative electrode; The step of determining the root cause of abnormally large self-discharge according to the graphic morphology includes: When the rate of change of the graphic morphology is stable and greater than a preset rate of change, it is determined that the root cause of the abnormally large self-discharge is a short circuit in the battery cell; When the rate of change of the graphical morphology decreases monotonically, it is determined that the root cause of the abnormally large self-discharge is copper deposition on the negative electrode.

7. A fault analysis device, characterized in that: The fault analysis device comprises: A fault location module, which is used to perform data analysis based on a cloud database to identify faulty battery packs that have experienced cell voltage inconsistency; An extreme value comparison module is used to compare the state of charge extreme value cells and health extreme value cells in the faulty battery pack, and determine the cause of the cell voltage inconsistency phenomenon and the inconsistent cells based on the comparison results, including: when the cell with the smallest state of charge before charging, the cell with the smallest state of charge after charging, and the cell with the largest health are all the same cell, determining that the cause of the cell voltage inconsistency phenomenon is abnormally large self-discharge, and treating the cell with the largest health as an inconsistent cell; when the cell with the smallest state of charge before charging, the cell with the largest state of charge after charging, and the cell with the smallest health are all the same cell, determining that the cause of the cell voltage inconsistency phenomenon is abnormal capacity attenuation, and treating the cell with the smallest health as an inconsistent cell; The root cause analysis module is used to determine the root cause of the abnormal self-discharge according to the charge state change trend of each cell in the faulty battery pack when it is determined that the cause is abnormal self-discharge.

8. A fault analysis device, characterized in that: The fault analysis device includes: a memory, a processor, and a fault analysis program stored in the memory and executable on the processor. When the fault analysis program is executed by the processor, the steps of the fault analysis method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a fault analysis program, which, when executed by a processor, implements the steps of the fault analysis method according to any one of claims 1 to 6.

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