Battery fault pre-detection and judgment method and device based on offline data

Abstract

The present invention provides a method and device for pre-detecting and determining battery faults based on offline data. The method comprises: acquiring offline data from a battery pack and, using the offline data, obtaining a battery pack voltage dataset for each cell at different time periods; processing each of the cell voltage datasets to obtain the voltage state attributes of each cell within its cell voltage dataset; and performing a pre-detection and determination of battery faults based on the voltage state attributes of the cells at different time periods and a battery reference table. By processing and analyzing offline data, the present invention can accurately and preemptively determine whether a battery pack is faulty. This accurate early warning ensures the safety of the battery pack and the electrical equipment using the battery pack, thereby improving the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular to a method and device for pre-detecting and determining battery faults based on offline data. Background Art

[0002] A battery pack or battery pack consists of several battery cells connected in series and parallel to meet high power requirements. Due to the large number of battery cells, inconsistent battery cell consistency can seriously affect the battery pack's capacity, energy, voltage drop, and service life, ultimately impacting the user experience.

[0003] Traditional lithium-ion battery fault analysis often involves first finding the fault at the scene of the fault, then analyzing the cause, and finally seeking the correlation between the cause and the fault. This method can often determine the type of single cell abnormality, but the analysis results often rely on the experience of after-sales personnel, which is inefficient and usually difficult to achieve the function of fault warning. For example, patent application document CN112965001A (application number 202110180579.4) discloses a power battery pack fault diagnosis method based on real vehicle data, which can diagnose overvoltage or undervoltage faults in battery pack cells and belongs to the field of fault diagnosis. This method obtains the charge and discharge voltage data of all battery cells in the late life cycle of the accident vehicle, calculates the difference between the state vector of each cell and the reference state vector in a specific time window, and determines whether there is an abnormality in the cell within the time window. Finally, the abnormal cell state is compared with the median of the cell state; if the abnormal cell state is higher than the median of the cell state, it indicates that the abnormal cell has an overvoltage fault; if it is lower, it indicates that the cell has an undervoltage fault. This technology is a fault diagnosis performed after a vehicle accident.

[0004] Currently, there are methods for battery fault diagnosis based on relevant battery data, but most cases tend to only stay at the level of whether a single cell has failed. Even if the fault data is classified, the data characteristics are not matched with the actual fault type; that is, the cause of the fault is not reflected, and subsequent maintenance work cannot be effectively guided. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method and device for pre-detecting and judging battery faults based on offline data, so as to solve the problem in the prior art that faults and their causes cannot be accurately predicted in advance.

[0006] To achieve the above objectives and other related objectives, the present invention provides a battery fault pre-detection and judgment method based on offline data:

[0007] Obtaining offline data of the battery pack, and obtaining a single cell voltage data set of the battery pack at different time periods through the offline data;

[0008] Processing each of the single cell voltage data sets to obtain a voltage state attribute of each single cell in its single cell voltage data set;

[0009] Perform battery fault pre-detection and judgment based on the voltage status attributes of single cells at different time periods and the battery reference table.

[0010] Preferably, obtaining a single cell voltage dataset of a battery pack at different time periods through the offline data at least includes:

[0011] Filter out data fragments with complete charge and discharge characteristics from offline data;

[0012] A single cell voltage data set of the battery pack in three different time periods is obtained according to the data segments.

[0013] Preferably, the three different time periods are respectively the charging end, the discharge current stabilization point period and the discharge end.

[0014] Preferably, the process of obtaining a voltage state attribute of a single battery from a single battery voltage data set includes:

[0015] The voltage values of all single cells in a single cell voltage data set are processed to obtain the rank average value of a single cell;

[0016] The voltage state attribute of a single cell in its single cell voltage data set is determined according to the rank average value of the single cell.

[0017] Preferably, processing the voltage values of all single cells in a single cell voltage data set to obtain a rank average value of a single cell includes:

[0018] Sort the cell voltages of a cell voltage data set at the same time, and obtain the voltage ranking corresponding to all cells at the same time according to the sorting results;

[0019] The ranking rank of a single cell at all times is processed to obtain the average rank of the single cell.

[0020] Preferably, the process of determining the voltage state attribute of a single cell in its single cell voltage data set according to the rank average value of the single cell is: first determining the voltage state of the single cell, and then determining the voltage state attribute of the single cell according to the voltage state of the single cell.

[0021] Preferably, the process of performing fault pre-detection and determination on the battery according to the voltage state attributes of the single battery in different time periods and the battery reference table includes at least:

[0022] Determine the coordinates of the single cell in the battery reference table based on the voltage state attributes of the same single cell at different time periods;

[0023] Obtaining a comparison result of the single cell according to the coordinates of the single cell in the battery reference table;

[0024] Perform fault pre-inspection and judgment on the battery pack based on the comparison results of all single cells.

[0025] Preferably, the battery reference table includes a battery abnormality degree table and a battery abnormality type table.

[0026] Preferably, the battery abnormality degree table includes confirmed abnormality, no abnormality and to be observed; the battery abnormality type table includes none, large self-discharge, low capacity and large internal resistance.

[0027] To achieve the above-mentioned purpose and other related purposes, the present invention also provides a battery fault pre-detection and judgment device based on offline data, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned battery fault pre-detection and judgment method based on offline data are implemented.

[0028] As described above, the battery fault pre-detection and determination method and device based on offline data of the present invention have the following beneficial effects:

[0029] The present invention discloses a method and device for pre-detecting and determining battery faults based on offline data. The method comprises: acquiring offline data from a battery pack and, using the offline data, obtaining a battery pack voltage dataset for each cell at different time periods; processing each of the cell voltage datasets to obtain the voltage state attributes of each cell within its cell voltage dataset; and performing a pre-detection and determination of battery faults based on the cell voltage state attributes at different time periods and a battery reference table. By processing and analyzing offline data, the present invention can accurately and preemptively determine whether a battery pack is faulty. This accurate early warning ensures the safety of the battery pack and the electrical equipment using the battery pack, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It shows a flow chart of the battery fault pre-detection and determination method based on offline data according to the present invention.

[0031] Figure 2 Shown is a schematic diagram of the process of determining the voltage state attribute of a single battery according to the present invention.

[0032] Figure 3 A flow chart showing the criteria for determining when a single cell battery has a severe outlier is shown.

[0033] Figure 4 Shown is a structural schematic diagram of a battery fault pre-detection and determination device based on offline data according to the present invention. DETAILED DESCRIPTION

[0034] The following describes the embodiments of the present invention through specific examples. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention.

[0035] See also Figure 1-4 It should be noted that the diagrams provided in this embodiment are merely schematic illustrations of the basic concept of the present invention. Therefore, the diagrams only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0036] Taking into account the complexity of battery operating conditions and feature extraction, the present invention is able to determine the abnormal type of battery cells based on the offline data of the battery pack, and can simultaneously determine multiple specific fault types. Based on the above technical concepts, the present invention provides a battery fault diagnosis method and device based on offline data.

[0037] Method Example:

[0038] like Figure 1 The figure shows a flow chart of the battery fault pre-detection and judgment method based on offline data of the present invention. Figure 1 The battery fault pre-detection and determination method based on offline data of the present invention is described in detail.

[0039] S1, acquiring offline data of a battery pack, and obtaining a single cell voltage dataset of the battery pack at different time periods through the offline data;

[0040] This step aims to obtain offline data when the battery pack is still working. Offline data refers to data for a certain period of time. Pre-inspection and analysis of offline data in subsequent steps can realize early judgment and warning of faults.

[0041] In the present invention, the offline data includes single cell voltage, time, and SOC (state of charge). As another embodiment, the offline data may also include current and battery pack temperature (temperature at a temperature collection point within the battery pack).

[0042] In this step, the data sets of the battery pack in different time periods are obtained through the offline data, including:

[0043] S11, filtering out data segments with complete charge and discharge characteristics from the offline data;

[0044] A data segment with complete charge and discharge characteristics is defined as follows: the highest SOC in the offline data within a certain time period is greater than or equal to 99.8%, and the lowest SOC in the data time period is less than or equal to 70%. If a data segment in the offline data does not meet the above conditions, the data segment is directly discarded and acquisition continues in time until a data segment that meets the requirements is obtained as a data segment with complete charge and discharge characteristics.

[0045] S12, obtaining a single cell voltage data set of the battery pack in three different time periods according to the data segments;

[0046] In the present invention, the set of single-cell voltages refers to the voltages of all single cells in the battery pack. When determining the three different time periods, the time closest to the current time, i.e., the latest time meeting the requirements within a certain time period, is selected based on the time in the offline data. The three different time periods are the end of charge, the discharge current stabilization point, and the end of discharge.

[0047] In the embodiment of the present invention, the screening interval of the charging terminal is: closed interval [t0, t umax ], where t umax Indicates the highest single cell voltage u during the data period max Peak M appears umax The corresponding moment, t0 represents when u max Closest to M umax -u0 corresponds to the moment, where the size of u0 is related to M umax For example, let u0 be proportional to M umax The relationship is u0=0.4×M umax -1320, u0 and M umax The unit is mV. umax =3650mV, t0 is selected as the moment when the highest cell voltage in the battery pack is closest to 3650-(0.4×3650-1320)=3510mV.

[0048] In this embodiment of the present invention, the screening interval for the discharge current stabilization point period is the period corresponding to the time period when the SOC value meets the set threshold range of the open interval (in this embodiment of the present invention, the set threshold range is 90% to 99%). The screening interval for the discharge end is the period corresponding to the portion of the discharge phase where the SOC is less than the set threshold (in this embodiment of the present invention, the set threshold is 80%).

[0049] S2, processing each single cell voltage data set to obtain the voltage state attribute of each single cell in its single cell voltage data set;

[0050] In this embodiment of the present invention, a single-cell voltage state attribute is obtained from a single-cell voltage dataset. Table 1 shows the single-cell voltages of a battery pack at different times (t1, t2, t3, t4, and t5) at the charging end, with the voltage unit in mV. Assume that the battery pack includes seven cells, namely a, b, c, d, e, f, and g.

[0051] Table 1

[0052] t1 t2 t3 t4 t5 a 3505 3488 3465 3350 3200 b 3504 3485 3480 3455 3390 c 3503 3480 3475 3460 3410 d 3502 3470 3468 3420 3380 e 3501 3460 3459 3450 3420 f 3506 3495 3490 3485 3455 g 3507 3500 3495 3488 3440

[0053] The process of obtaining the voltage state attributes of each single cell battery from a single cell voltage data set includes:

[0054] S21, processing the voltage values of all single cells in a single cell voltage data set to obtain a rank average value of the single cell;

[0055] S221, sorting the cell voltages of a cell voltage data set at the same time, and obtaining the voltage ranking corresponding to all the cell batteries at the same time according to the sorting result;

[0056] The voltage ranking of a cell at a certain moment is defined as the ranking value corresponding to each cell after the voltage values of all cells in the battery pack at the same moment are sorted from low to high.

[0057] In this embodiment of the present invention, the cell voltage dataset at the charging terminal includes voltage data for seven cells at five time points. Sorting the cell voltage values at each time point yields seven ranking ranks. Table 2 shows the ranking values corresponding to the voltage rankings for all cells in the battery pack at different time points at the charging terminal.

[0058] Table 2

[0059] t1 t2 t3 t4 t5 a 5 5 2 1 1 b 4 4 5 4 3 c 3 3 4 5 4 d 2 2 3 2 2 e 1 1 1 3 5 f 6 6 6 6 7 g 7 7 7 7 6

[0060] In Table 2, at time t1, the ranking value of single cell a in the battery pack is 5, the ranking value of single cell b is 4, the ranking value of single cell c is 3, the ranking value of single cell d is 2, the ranking value of single cell e is 1, the ranking value of single cell f is 6, and the ranking value of single cell g is 7; at time t2, the ranking value of single cell a in the battery pack is 5, ..., and the ranking value of single cell g is 7; at time t3, the ranking value of single cell a in the battery pack is 2, ..., and the ranking value of single cell g is 7; at time t4, the ranking value of single cell a in the battery pack is 1, ..., and the ranking value of single cell g is 7; at time t5, the ranking value of single cell a in the battery pack is 1, ..., and the ranking value of single cell g is 6;

[0061] S222 , processing the voltage ranking rank of a single cell at all times to obtain an average rank of the single cell.

[0062] In the present invention, the voltage ranking ranks of a single cell at all times are averaged to obtain the rank average value of the single cell.

[0063] In an embodiment of the present invention, at the end of charging, the rank average values of each single cell in the battery pack are as follows: the rank average value of single cell a = (5+5+2+1+1) / 5 = 2.8; the rank average value of single cell b = (4+4+5+4+3) / 5 = 4; the rank average value of single cell c = (3+3+4+5+4) / 5 = 3.8; the rank average value of single cell d = (2+2+3+2+2) / 5 = 2.2; the rank average value of single cell e = (1+1+1+3+5) / 5 = 2.2; the rank average value of single cell f = (6+6+6+6+7) / 5 = 6.2; the rank average value of single cell g = (7+7+7+7+6) / 5 = 6.8.

[0064] S22, determining a voltage state attribute of a single cell in its single cell voltage data set according to the rank average value of the single cell;

[0065] In the embodiment of the present invention, the voltage state includes a first voltage state, a second voltage state, a third voltage state, and a fourth voltage state, and the corresponding voltage state attributes are 1, 2, 3, and 4, respectively. Therefore, the present invention first determines the voltage state of the single cell, and then determines the voltage state attribute of the single cell based on the voltage state of the single cell.

[0066] In the present invention, the process of determining the voltage state attribute of a single cell is as follows: Figure 2 Shown include:

[0067] If the rank average Zm of a single cell is greater than or equal to half of the number N of single cells in the battery pack, the voltage state of the single cell in the single cell voltage data set is the first voltage state, and accordingly, the voltage state attribute of the single cell in the single cell voltage data set is 1.

[0068] If the rank average Zm of a single cell is less than or equal to half the number N of single cells in the battery pack but greater than or equal to a set threshold T, the voltage state of the single cell in the single cell voltage data set is the second voltage state, and correspondingly, the voltage state attribute of the single cell in the single cell voltage data set is 2. The set threshold of the present invention is approximately 10% of the total number of single cells.

[0069] If the rank average Zm of the single cell is less than the set threshold T, and there is no moment when the voltage of the single cell does not seriously outlier in its single cell voltage data set, then the voltage state of the single cell in the single cell voltage data set is the third voltage state. Correspondingly, the voltage state attribute of the single cell in the single cell voltage data set is 3.

[0070] If the rank average value of a single cell is less than the set threshold T, and there is a moment when the voltage of the single cell is seriously outlier in its single cell voltage data set, then the voltage state of the single cell in the single cell voltage data set is the fourth voltage state, and correspondingly, the voltage state attribute of the single cell in the single cell voltage data set is 4.

[0071] Among them, the judgment criteria for single battery when there is a serious outlier (such as Figure 3 )for:

[0072] For the cell voltage dataset corresponding to the charging terminal, if at the same moment, the first voltage difference d1 is greater than the first set value t1 and the second voltage difference d2 is greater than the second set value t2, then the cell is considered to have a severe outlier moment at the charging terminal. The first set value t1 is greater than the second set value t2, and the third set value t3 is greater than the fourth set value t4.

[0073] For the non-charging end, if at the same moment, the first pressure difference d1 is greater than the third set value t3 and the second pressure difference d2 is greater than the fourth set value t4, it is considered that at the non-charging end, the single cell has a serious outlier moment at the non-charging end (discharge current stable point period or discharge end).

[0074] Among them, the first pressure difference is the pressure difference between the voltage value of a single cell and the highest single cell voltage value at the same time, and the second pressure difference is the pressure difference between the voltage value of the single cell and the average voltage value of all single cells at the same time; it should be noted that the second set value and the third set value can be the same or different.

[0075] In an embodiment of the present invention, according to the above table, the single-cell voltage data set of the charging terminal of the present invention includes voltage data of 7 cells at 5 moments, and the threshold is set to 3; the first setting value is 250mV, the second setting value is 80mV; the third setting value is 80mV, and the fourth setting value is 30mV.

[0076] At time t5, the voltage difference d1 = 255 mV between cell a's voltage value of 3200 mV and cell f's voltage value of 3455 mV is greater than the first set value t1 = 250 mV. Furthermore, the voltage difference d2 = 185 mV between cell a's voltage value of 3200 mV and the average voltage value of all cells at time t5 of 3385 mV is greater than the second set value t2 = 80 mV. Therefore, cell a is a significant outlier in the cell voltage dataset corresponding to the end of charge, and the significant outlier time is t5. The average voltage value of all cells at time t5 is (3200 + 3390 + 3410 + 3380 + 3420 + 3455 + 3440) / 7 = 3385 mV.

[0077] When the rank average value of cell a is 2.8, which is less than the set threshold value 3, and a serious voltage outlier exists in the cell voltage data set of cell a, the voltage state of cell a in the cell voltage data set is the fourth voltage state. Correspondingly, the voltage state attribute of cell a in the cell voltage data set is 4.

[0078] This step determines the voltage state attributes of each cell in its cell voltage dataset by determining the voltage state attributes of the cell. Specifically, the voltage state attributes of each cell at the end of charge, during the discharge current stabilization period, and at the end of discharge are determined.

[0079] S3, performing a fault pre-detection and determination on the battery according to the voltage state attributes of the single battery at different time periods and a battery reference table.

[0080] In the present invention, the battery reference table includes a battery abnormality level table and a battery abnormality type table. The battery abnormality level table is shown in Table 1, where the abnormality level of a single cell is determined by the coordinates (i, j). The battery abnormality type table is shown in Table 2, where the abnormality type of a single cell is determined by the coordinates (h, i, j).

[0081] Where h is the voltage state attribute of the single cell during the discharge current stabilization period, i is the voltage state attribute of the single cell at the end of charge, and j is the voltage state attribute of the single cell at the end of discharge. The abnormality degree is indicated by A for confirmed abnormality, B for no abnormality, and C for observation. The abnormality type is indicated by W for no abnormality, K for high self-discharge, Q for low capacity, and R for high internal resistance.

[0082] Table 1

[0083]

[0084] Table 2

[0085]

[0086] The process of pre-checking and determining battery faults based on the voltage state attributes of the single cells at different time periods and the battery reference table includes at least:

[0087] S31, determining the coordinates of the single cell in the battery reference table based on the voltage state attributes of the same single cell at different time periods;

[0088] The coordinates of the single cell in the battery abnormality degree table are determined based on the voltage state attributes of the same single cell at the charging end and the voltage state attributes of the single cell at the discharging end; the coordinates of the single cell abnormality degree are (i, j), where i is the voltage state attribute of the single cell at the charging end and j is the voltage state attribute of the single cell at the discharging end.

[0089] The coordinates of the target cell in the battery anomaly type table are determined based on the voltage state attributes of the same cell at the end of charge, the voltage state attributes during the discharge current stabilization period, and the voltage state attributes at the end of discharge. The coordinates of the cell anomaly type are (h, i, j), where h is the voltage state attribute of the cell during the discharge current stabilization period, i is the voltage state attribute of the cell at the end of charge, and j is the voltage state attribute of the cell at the end of discharge.

[0090] In the embodiment of the present invention, for example, the voltage state attribute of the single cell a in the battery pack is 4 at the end of charging, 3 during the discharge current stabilization point period, and 4 at the end of discharging.

[0091] Then the coordinates of single cell a in the battery abnormality degree table are (4, 4), and the coordinates in the battery abnormality type table are (3, 4, 4).

[0092] S32, obtaining a comparison result of the single cell according to the coordinates of the single cell in the battery reference table;

[0093] In the embodiment of the present invention, the battery abnormality degree of the single cell a is obtained as A according to the coordinates of the single cell a in the battery abnormality degree table being (4, 4), and the battery abnormality type of the single cell a is obtained as K according to the coordinates of the single cell a in the battery abnormality type table being (3, 4, 4).

[0094] S33, performing a fault pre-detection and determination on the battery pack based on the comparison results of all the single cells.

[0095] In the present invention, when the control results of all single cells are all negative, the battery pack is determined to be fault-free, otherwise it is considered to be faulty. When a fault is determined, a more accurate fault determination can be achieved based on the degree and type of abnormality.

[0096] In the embodiment of the present invention, since A indicates confirmed abnormality and K indicates large self-discharge, the single cell a has a fault and is confirmed to be abnormal and has large self-discharge.

[0097] Device Example:

[0098] A battery fault pre-detection and judgment device based on offline data Figure 4 As shown, it includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned battery fault pre-detection and determination method based on offline data are implemented.

[0099] The detailed process of the steps of the method for pre-detection and determination of battery faults based on offline data has been described in detail in the method embodiment and will not be repeated here.

[0100] In summary, the present invention's offline data-based battery fault pre-detection and determination method and device can easily obtain data segments with complete charge and discharge characteristics from the battery pack's offline data. By screening and obtaining single-cell voltage data sets for three different time periods, fault analysis and early warning determination of single-cell batteries can be performed. Compared to data obtained from any time period under normal circumstances, the offline data of the present invention has higher reliability. Furthermore, by first classifying the voltage state attributes based on the analysis results, and then accurately analyzing and locating the battery reference table to obtain a comparison result, the degree and type of abnormality of the single-cell battery can be determined simultaneously and simply and efficiently, thereby achieving early warning of battery single-cell failures based on offline data. Therefore, the present invention effectively overcomes the various shortcomings of the existing technology and has high industrial application value.

[0101] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A battery fault pre-detection and determination method based on offline data, characterized in that: At least the following steps are included: Obtaining offline data of the battery pack, and obtaining a single cell voltage data set of the battery pack at different time periods through the offline data; Each of the single cell voltage data sets is processed to obtain the voltage state attributes of each single cell in its single cell voltage data set, specifically including: sorting the single cell voltages at the same moment to obtain the voltage ranking rank of each single cell, averaging the ranks of the same single cell at different moments, and obtaining the rank average value corresponding to each single cell; if the rank average value of the single cell is greater than or equal to half of the number of single cells in the battery pack, the voltage state of the single cell is a first voltage state; if the rank average value of the single cell is less than half of the number of single cells in the battery pack and greater than or equal to a set threshold, the voltage state of the single cell is a second voltage state; if the rank average value of the single cell is less than the set threshold, and there is no moment when the voltage of the single cell is seriously outlier in its single cell voltage data set, the voltage state of the single cell is a third voltage state; if the rank average value of the single cell is less than the set threshold, and there is a moment when the voltage of the single cell is seriously outlier in its single cell voltage data set, the voltage state of the single cell is a fourth voltage state; Perform battery fault pre-detection and judgment based on the voltage status attributes of single cells at different time periods and the battery reference table.

2. The battery fault pre-detection and determination method based on offline data according to claim 1, characterized in that: Obtaining a single cell voltage data set of a battery pack at different time periods using the offline data at least includes: Filter out data fragments with complete charge and discharge characteristics from offline data; A single cell voltage data set of the battery pack in three different time periods is obtained according to the data segments.

3. The battery fault pre-detection and determination method based on offline data according to claim 1, characterized in that: The three different time periods are the charging end, the discharge current stabilization point period and the discharge end.

4. The battery fault pre-detection and determination method based on offline data according to claim 1, characterized in that: The voltage values of all cells in a cell voltage data set are processed to obtain the rank average value of a cell, including: Sort the cell voltages of a cell voltage data set at the same time, and obtain the voltage ranking corresponding to all cells at the same time according to the sorting results; The ranking rank of a single cell at all times is processed to obtain the average rank of the single cell.

5. The battery fault pre-detection and determination method based on offline data according to claim 1, characterized in that: The process of pre-checking and determining a battery fault based on the voltage state attributes of the single battery at different time periods and the battery reference table includes at least: Determine the coordinates of the single cell in the battery reference table based on the voltage state attributes of the same single cell at different time periods; Obtaining a comparison result of the single cell according to the coordinates of the single cell in the battery reference table; Perform fault pre-inspection and judgment on the battery pack based on the comparison results of all single cells.

6. The battery fault pre-detection and determination method based on offline data according to claim 1, characterized in that: The battery reference table includes a battery abnormality degree table and a battery abnormality type table.

7. The battery fault pre-detection and determination method based on offline data according to claim 6, characterized in that: The battery abnormality degree table includes confirmed abnormality, no abnormality and to be observed; the battery abnormality type table includes none, large self-discharge, low capacity and large internal resistance.

8. A battery fault pre-detection and determination device based on offline data, characterized in that: The invention comprises a memory, a processor and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the battery fault pre-detection and determination method based on offline data according to any one of claims 1 to 7 are implemented.

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