Battery self-discharge defect detection method and device, electronic equipment and vehicle

Abstract

The invention discloses a battery self-discharge defect detection method and device, electronic equipment and a vehicle. The method for detecting the self-discharge defect of the battery comprises the following steps: acquiring the low-voltage probability of a single battery to be detected; and calculating the change rate of the low-voltage probability of the to-be-detected single battery in a preset time based on the low-voltage probability of the to-be-detected single battery. And if the change rate of the low-voltage probability of the to-be-detected single battery is greater than a preset value, determining that the to-be-detected single battery has a self-discharge defect. The detection method does not need to wait for the natural change of the battery voltage for a long time, does not need to stand and observe the battery for a long time, greatly shortens the detection period, can complete the detection of a large number of battery monomers in a short time, remarkably improves the detection efficiency, and meets the requirements of large-scale battery production and detection.

Description

Technical Field

[0001] The present invention relates to the technical field of power batteries, and particularly to a method and device for detecting battery self-discharge defects, an electronic device, and a vehicle. Background Art

[0002] In the field of batteries, battery self-discharge defects are a major problem with batteries. If this problem is not addressed in a timely manner, it will cause a rapid decline in battery life at best, affecting the user experience, and at worst, it is likely to trigger a serious risk of battery thermal runaway, resulting in serious property losses and social impacts. In related technologies, by leaving the battery pack idle for a long time, the voltage change of each battery cell in the battery pack before and after being left idle is compared to calculate the K value of the battery, and whether the K value exceeds the standard is compared to determine whether there is a self-discharge defect in the power battery. However, the on-site measured K value is not a targeted detection, making it difficult to accurately locate the self-discharge defect, which requires a lot of time and has low detection efficiency. Summary of the Invention

[0003] The main object of the present invention is to provide a method and device for detecting battery self-discharge, an electronic device, and a vehicle, aiming to solve the technical problem that detecting battery self-discharge defects requires a lot of time and has low detection efficiency.

[0004] To achieve the above-mentioned invention object, a first aspect of the present invention proposes a method for detecting battery self-discharge defects.

[0005] A method for detecting battery self-discharge defects includes the following steps:

[0006] Obtain the low-voltage probability of the battery cell to be detected;

[0007] Based on the low-voltage probability of the battery cell to be detected, calculate the change rate of the low-voltage probability of the battery cell to be detected within a preset time;

[0008] If the change rate of the low-voltage probability of the battery cell to be detected is greater than a preset value, it is determined that the battery cell to be detected has a self-discharge defect.

[0009] In one of the embodiments, the step of obtaining the low-voltage probability of the battery cell to be detected includes:

[0010] Obtain the data source of the battery cell to be detected;

[0011] Divide the data source of the battery cell to be detected into multiple segments according to time to obtain multiple data segments;

[0012] Calculate the low-voltage probability of the battery cell to be detected for each data segment.

[0013] In one embodiment, the step of calculating the low-voltage probability of the battery cell to be detected for each segment includes:

[0014] In the data segment, obtain the frequency of the battery cell to be detected becoming the lowest voltage;

[0015] Obtain the sum of the lowest voltage frequencies of all battery cells in the data segment;

[0016] Based on the frequency of the battery cell to be detected becoming the lowest voltage in the data segment and the sum of the lowest voltage frequencies of all battery cells in the data segment, calculate the low-voltage probability of the battery cell to be detected.

[0017] In one embodiment, the step of calculating the change rate of the low-voltage probability of the battery cell to be detected within a preset time based on the low-voltage probability of the battery cell to be detected includes:

[0018] Set the time of each data segment as the X-axis coordinate value;

[0019] Set the low-voltage probability of the battery cell to be detected in each data segment as the Y-axis coordinate value;

[0020] Calculate the slope value of the low-voltage probability of the battery cell to be detected relative to the data segment time.

[0021] In one embodiment, the preset value is a threshold parameter. If the number of strings of the battery cells to be detected is larger, the threshold parameter is smaller; if the number of strings of the battery cells to be detected is smaller, the threshold parameter is larger.

[0022] In one embodiment, the threshold parameter is 1% - 3%.

[0023] In one embodiment, the preset value is the upper control limit UCL of the single-value control chart of the change rate of the low-voltage probability of the battery cell to be detected, and the upper control limit UCL = Ω 均 + 3*σ, where Ω 均 is the average value of the change rate of the low-voltage probability of the battery cell to be detected, and σ is the standard deviation of the change rate of the low-voltage probability of the battery cell to be detected.

[0024] The second aspect of the present invention proposes a detection device for battery self-discharge, which executes the above detection method for battery self-discharge defects, including:

[0025] An acquisition unit, configured to acquire the low-voltage probability of the battery cell to be detected;

[0026] A calculation unit, configured to calculate the change rate of the low-voltage probability of the battery cell to be detected within a preset time based on the low-voltage probability of the battery cell to be detected;

[0027] A judging unit, configured to determine that the battery cell to be detected has a self-discharge defect if a change rate of the low-voltage probability of the battery cell to be detected is greater than a preset value.

[0028] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, the detection method for battery self-discharge as described above is implemented.

[0029] A fourth aspect of the present invention provides a vehicle, including the above-mentioned electronic device.

[0030] Beneficial effects:

[0031] The detection method for battery self-discharge defect of the present invention includes the following steps: obtaining the low-voltage probability of the battery cell to be detected; calculating a change rate of the low-voltage probability of the battery cell to be detected within a preset time based on the low-voltage probability of the battery cell to be detected; and determining that the battery cell to be detected has a self-discharge defect if the change rate of the low-voltage probability of the battery cell to be detected is greater than a preset value. By obtaining the low-voltage probability of the battery cell to be detected and calculating its change rate within a preset time, the low-voltage probability and its change rate can quickly reflect the situation of the battery self-discharge defect, without the need to wait for a long time for the natural change of the battery voltage, and no longer need to leave the battery for a long time for observation, greatly shortening the detection period, and a large number of battery cells can be detected in a short time, significantly improving the detection efficiency, and meeting the requirements of large-scale battery production and detection. Description of the Drawings

[0032] Figure 1 is a flowchart of the detection method for battery self-discharge defect according to an embodiment of the present invention.

[0033] Figure 2 is a graph of the low-voltage probability and time of the battery cell to be detected according to an embodiment of the present invention.

[0034] Figure 3 is a control chart of the low-voltage probability value of the battery to be detected according to an embodiment of the present invention.

[0035] Figure 4 is a graph of the differential pressure change of the whole battery pack to be detected according to an embodiment of the present invention.

[0036] Figure 5 is a graph of the measured self-discharge K value of the battery pack returned to the factory according to an embodiment of the present invention.

[0037] Figure 6 is a comparison graph of time, the low-voltage probability Pi of each battery cell in the battery pack, and the change rate Ki of the battery cell to be detected according to an embodiment of the present invention.

[0038] The implementation, functional features, and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Specific Embodiments

[0039] It should be understood that the specific embodiments described herein are merely for explaining the present invention and are not used to limit the present invention.

[0040] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. These are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, so it cannot be understood as a limitation of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0041] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium, and it may be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] In the present invention, unless otherwise clearly specified and limited, the first feature being "above" or "below" the second feature may include the direct contact between the first and second features, or may include the situation where the first and second features are not in direct contact but in contact through other features therebetween. Moreover, the first feature being "above", "over", and "on" the second feature includes that the first feature is directly above and obliquely above the second feature, or simply means that the horizontal height of the first feature is higher than that of the second feature. The first feature being "below", "beneath", and "under" the second feature includes that the first feature is directly below and obliquely below the second feature, or simply means that the horizontal height of the first feature is lower than that of the second feature.

[0043] Such as Figure 1As shown, in some embodiments, a method for detecting battery self-discharge defects includes the following steps:

[0044] S100. Obtain the low-voltage probability of the battery cell to be detected.

[0045] S200. Based on the low-voltage probability of the battery cell to be detected, calculate the change rate of the low-voltage probability of the battery cell to be detected within a preset time.

[0046] S300. If the change rate of the low-voltage probability of the battery cell to be detected is greater than a preset value, it is determined that the battery cell to be detected has a self-discharge defect.

[0047] By obtaining the low-voltage probability of the battery cell to be detected and calculating its change rate within a preset time, the low-voltage probability and its change rate can quickly reflect the situation of battery self-discharge defects, without the need to wait for a long time for the natural change of the battery voltage, and there is no need to leave the battery for a long time for observation, which greatly shortens the detection cycle, can complete the detection of a large number of battery cells in a short time, significantly improves the detection efficiency, and meets the requirements of large-scale battery production and detection.

[0048] In addition, in the related art, based on vehicle operation data, the pressure difference change of the vehicle in time series is calculated, and whether the power battery has a self-discharge defect is determined by comparing whether the pressure difference change exceeds the standard. However, this technical route can only be applied to vehicles with a large pressure difference and an obvious change in the pressure difference of the battery pack, and cannot be applied to vehicles without an obvious pressure difference change. In addition, this technical solution can only identify that the battery pack has self-discharge, and cannot accurately locate the self-discharge defective cells in the battery pack or can only locate to the lowest voltage string of the battery pack. The method for detecting battery self-discharge defects of the present application can effectively identify that a specific string of battery cells inside the power battery has a self-discharge defect, which is convenient for subsequent maintenance of abnormal power batteries. In addition, the detection method of the present application can be applied to vehicles with no obvious change in the overall pressure difference of the battery pack, effectively supplementing and improving the technical deficiencies of identifying self-discharge defects by observing the change in the overall pressure difference of the battery pack in the related art. This detection method can also realize real-time online monitoring of running vehicles, timely discover and handle batteries with self-discharge defects, and reduce the operation risk of market vehicles.

[0049] Specifically, if the change rate of the low-voltage probability of the battery cell to be detected is less than or equal to the preset value, it is determined that the battery cell to be detected does not have a self-discharge defect. That is, if the battery cell to be detected has no self-discharge defect or the self-discharge is within the normal range, then the low-voltage probability of the battery cell to be detected is relatively stable and the change rate is small.

[0050] Specifically, the battery cell to be detected can be a power battery.

[0051] Specifically, the battery grouping method is multi-string grouping, that is, multiple battery cells in the battery pack are connected in series to form a battery. Define the grouping structure of the battery pack as n strings of battery cells to be detected connected in series. Let i denote the i-th string of battery cells to be detected in the battery pack, where 1 ≤ i ≤ n and i is an integer. Among them, the battery to be tested selected in this embodiment is formed by connecting 24 strings of battery cells in series, that is, n = 24 for the power battery to be tested in this embodiment, 1 ≤ i ≤ 24, and i is an integer. The probability of a low voltage occurring in each string of battery cells to be detected is related to its own self-discharge situation. If a string of battery cells to be detected has a self-discharge defect, over time, the likelihood of its voltage dropping increases, the probability of a low voltage occurring increases, and the rate of change of its low voltage probability within a preset time will also increase accordingly. By monitoring the rate of change of the low voltage probability of each string of battery cells to be detected, it is possible to determine whether there is a self-discharge defect in that string of battery cells.

[0052] Specifically, a low voltage refers to a certain battery cell to be detected with the lowest voltage among multiple battery cells to be detected within a unit time, that is, the low-voltage string. For example, among 24 strings of battery cells, the battery cells in the 4th string have the lowest voltage. Under normal circumstances, for multiple series-connected battery cells of the same batch, the same specification, and in good condition, their voltages should be relatively stable and close. However, when a certain battery cell has self-discharge, the process of converting chemical energy into electrical energy inside it will be abnormal, resulting in the battery cell consuming power faster than other battery cells, and thus showing a relatively lower voltage within a unit time. For example, among 24 strings of battery cells, if the battery cells in the 4th string have self-discharge, over time, the voltage of the battery cells in the 4th string will gradually be lower than that of the other strings of battery cells, becoming the battery cell with the lowest voltage.

[0053] In some embodiments, step S100 of obtaining the low voltage probability of the battery cells to be detected includes:

[0054] S110. Obtain the data source of the battery cells to be detected.

[0055] S120. Divide the data source of the battery cells to be detected into multiple segments according to time to obtain multiple data segments.

[0056] S130. Sort the multiple data segments in chronological order.

[0057] S140. Calculate the low voltage probability of the battery cells to be detected for each data segment.

[0058] As described in S110 above: To obtain the data source of the battery cells to be detected, the vehicle operation data of the battery cells to be detected in the most recent 10 days can be selected as the data source for self-discharge defect detection calculation.

[0059] As described above in S120: The data source of the battery cell to be detected is divided into multiple segments according to time, obtaining multiple data segments. As in S130: The multiple data segments are sorted according to the time sequence. The data source of the battery cell to be detected is sliced with a uniform time span according to the time sequence of data occurrence. In this embodiment, for the vehicle operation data of the selected battery cell to be detected in the most recent 10 days, it is sliced uniformly on a daily basis according to the time sequence of data occurrence, and each data segment has 1 day of vehicle operation data. Record the time of each data segment. Extract the last data time in the data segment as the time t corresponding to this segment, with the time accurate to days, denoted as t1, t2...t m . In this embodiment, the data segment times extracted are t1, t2,......t 10 ; Specifically as Figure 6 shown.

[0060] It should be noted that sorting the multiple data segments according to the time sequence is based on the ordered characteristic of time series data. Through sorting, it can be ensured that in subsequent analysis, the data is processed and compared in the order of time, which conforms to the actual use and change process of the battery. For example, when calculating the change rate of the low voltage probability, it is necessary to determine the probability values at different time points according to the order of the data, so as to accurately calculate the change rate and determine whether the battery has a self-discharge defect.

[0061] As described above in S140: Calculate the low voltage probability of the battery cell to be detected in each data segment. The low voltage string probability P of each battery cell to be detected in the battery pack is denoted as Pi. The low voltage probability of each battery cell to be detected in each data segment selected in this embodiment is as Figure 2 shown. The ordinate refers to the low voltage probability Pi of each battery cell to be detected in each data segment within the data source range, and the abscissa is the time of each data segment of the battery cell to be detected within the data source range.

[0062] In some embodiments, the above step S140 of calculating the low voltage probability of the battery cell to be detected in each segment includes:

[0063] S141. In the data segment, obtain the frequency M of the battery cell to be detected becoming the lowest voltage, where i refers to the i-th string of battery cells to be detected in the battery pack, 1 ≤ i ≤ n, and i is an integer. This step is based on the real-time monitoring and counting of the voltage state of each battery cell to be detected within the data segment to obtain the frequency information of this battery cell becoming a low voltage. i , i refers to the i-th string of battery cells to be detected in the battery pack, 1 ≤ i ≤ n, and i is an integer. This step is based on the real-time monitoring and counting of the voltage state of each battery cell to be detected within the data segment to obtain the frequency information of this battery cell becoming a low voltage.

[0064] S142. Obtain the sum of the lowest voltage frequencies of all the battery cells in the data segment. That is, the sum of the low voltage frequencies of each battery cell to be detected, M iThe accumulated value is recorded as S. The accumulation operation is based on the low voltage frequency statistics of all battery cells in the same data segment, and the total frequency of all possible low voltage situations of the entire battery pack in the data segment is obtained.

[0065] S143. Based on the frequency of the battery cell to be detected in the data segment becoming the lowest voltage and the sum of the lowest voltage frequencies of all battery cells in the data segment, calculate the low voltage probability of the battery cell to be detected, denoted as Pi. Wherein, Pi = Mi / S. According to the basic definition of probability, the frequency Mi of the battery cell to be detected in the i-th string becoming the lowest voltage is divided by the sum of the lowest voltage frequencies of all battery cells S, and the obtained Pi is the low voltage probability of the battery cell to be detected in this data segment. By calculating the proportion of the frequency of a specific battery cell becoming low voltage in the total low voltage frequency, the possibility of the battery cell having low voltage in the current data segment is quantified.

[0066] In some embodiments, the step S200 of calculating the change rate of the low voltage probability of the battery cell to be detected within a preset time based on the low voltage probability of the battery cell to be detected includes:

[0067] S210, setting the time of each data segment as the X-axis coordinate value.

[0068] S220 , setting the low voltage probability of the battery cell to be detected in each data segment as the Y-axis coordinate value.

[0069] S230 calculates the slope value of the low voltage probability of the battery cell to be detected relative to the data segment time. The slope value represents the rate of change of the low voltage probability over time. By calculating the slope between adjacent data points, the change of the low voltage probability in unit time can be quantified. The larger the slope, the more drastic the change of the low voltage probability over time. The smaller the slope, the more gradual the change of the low voltage probability over time. If the slope is positive, it means that the low voltage probability increases over time. If the slope is negative, it means that the low voltage probability decreases over time.

[0070] It should be noted that the change rate of the low voltage probability of each battery cell to be detected over time is calculated, and is denoted by Ki. The time t recorded in each data segment is used as the X-axis coordinate value, and the low voltage probability Pi of each battery cell to be detected calculated in each data segment is used as the Y-axis coordinate value. The change rate of the low voltage probability of the battery cell to be detected is calculated, that is, the slope value of the low voltage probability Pi of each battery cell to be detected in the battery pack relative to the data segment time t, and is denoted by Ki. In this embodiment, the low voltage probability of each battery cell to be detected in each selected data segment relative to the data segment time curve is as follows: Figure 2 As shown. By calculation Figure 2 The slope value Ki of each battery cell curve to be tested is shown in Figure 6 .

[0071] As described in S300 above: If the change rate of the low-voltage probability of the battery cell to be detected is greater than the preset value, it is determined that the battery cell to be detected has a self-discharge defect.

[0072] It should be noted that the principle for determining the self-discharge defect of the battery cell to be detected can be that within the data source range of the battery pack, the low-voltage probability Pi of a certain battery cell i to be detected generally increases with respect to the data segment time t. Based on this principle for determining the self-discharge defect of the power battery, there are different specific determination conditions for different battery models. For example, in the appendix Figure 6 the low-voltage probability of battery cell v004 on July 10, 2023 is significantly higher than that on July 1, 2023, and during the period from July 1, 2023 to July 10, 2023, the low-voltage probability of battery cell v004 generally shows an upward trend, and it is determined that there is a problem with the self-discharge defect of battery cell v004. In addition, the overall voltage difference of the battery pack has no obvious change on the time scale of the analysis data source, as Figure 4 shown.

[0073] In some embodiments, the preset value is the threshold parameter a. If the number of series of the battery cell to be detected is larger, the threshold parameter a is smaller; if the number of series of the battery cell to be detected is smaller, the threshold parameter a is larger. For different models of the battery cells to be detected, the values of the threshold parameter a are different. The value of the threshold parameter a is related to the measured self-discharge battery pack sample data.

[0074] Specifically, the threshold parameters for different battery models are different. The threshold parameter a can be 1% - 3%. More specifically, the threshold parameter a can be 2%.

[0075] As Figure 3 shown, in some embodiments, the preset value is the upper control limit UCL of the single-value control chart of the change rate of the low-voltage probability of the battery cell to be detected, and the upper control limit UCL = Ω 均 + 3*σ, where Ω 均is the average value of the change rate of the low - voltage probability of the battery cell to be detected. σ is the standard deviation of the change rate of the low - voltage probability of the battery cell to be detected. The individual control chart is a statistical tool used to monitor the process quality characteristics, mainly for observing and analyzing the changes of individual data points to determine whether the process is in a stable state. The upper control limit (UCL) is an important parameter in the individual control chart. The upper control limit UCL is a boundary value used in the individual control chart to determine whether the data points exceed the normal fluctuation range. If the data point exceeds the upper control limit UCL, it indicates that the process may have an abnormal situation, and the process needs to be investigated and analyzed to find out the cause of the abnormality. By setting such an upper control limit, it is possible to quickly and effectively identify whether the change rate of the low - voltage probability of the battery cell to be detected exceeds the normal range. Once the change rate exceeds the upper control limit UCL, it can be immediately determined that the battery cell may be abnormal, providing a clear signal for taking timely measures, helping to avoid further deterioration of the problem and reducing the damage to the entire battery system.

[0076] In some embodiments, when a battery cell with a self - discharge defect is detected, a warning message for the self - discharge defect of the battery cell is generated and sent. Specifically, the warning message is used to give a self - discharge defect warning to relevant personnel through communication technology.

[0077] In some embodiments, in order to verify the detection method of the present application, the battery cell to be detected in this embodiment is returned to the factory and the self - discharge K - value test is carried out. As Figure 5 shown, the battery cell of the power battery determined by this method to have a self - discharge defect has an obvious self - discharge defect. The power battery cell with the self - discharge defect is disassembled, and it is found that the diaphragm of the 4th string of this battery cell is damaged. This structure is consistent with the determination result of the above - mentioned detection method for the battery cell to be detected.

[0078] In another embodiment, a detection device for battery self - discharge, which executes the above - mentioned detection method for battery self - discharge defects, includes:

[0079] An acquisition unit for acquiring the low - voltage probability of the battery cell to be detected;

[0080] A calculation unit for calculating the change rate of the low - voltage probability of the battery cell to be detected within a preset time based on the low - voltage probability of the battery cell to be detected;

[0081] A judgment unit for determining that the battery cell to be detected has a self - discharge defect if the change rate of the low - voltage probability of the battery cell to be detected is greater than a preset value.

[0082] In another embodiment, an electronic device includes a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, it implements a method for detecting self-discharge of a battery as described above.

[0083] In another embodiment, a vehicle includes the above-mentioned electronic device. Those skilled in the art know that the present application can be implemented as a device, a method, or a computer program product. Therefore, the present disclosure can be specifically implemented in the following forms: it can be completely hardware, can be completely software (including firmware, resident software, microcode, etc.), or can be a combination of hardware and software, which is generally referred to as "circuit", "module", or "system" in this article. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable media contains computer-readable program code.

[0084] Any combination of one or more computer-readable media can be adopted. The computer-readable media can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or component.

[0085] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for detecting battery self-discharge defects, characterized in that, Including the following steps: Obtain the low-voltage probability of the battery cell to be detected; Based on the low-voltage probability of the battery cell to be detected, calculate the change rate of the low-voltage probability of the battery cell to be detected within a preset time; If the change rate of the low-voltage probability of the battery cell to be detected is greater than a preset value, it is determined that the battery cell to be detected has a self-discharge defect.

2. The detection method according to claim 1, wherein The step of obtaining the low-voltage probability of the battery cell to be detected includes: Obtain the data source of the battery cell to be detected; Divide the data source of the battery cell to be detected into multiple segments according to time to obtain multiple data segments; Calculate the low-voltage probability of the battery cell to be detected for each data segment.

3. The detection method according to claim 2, wherein The step of calculating the low-voltage probability of the battery cell to be detected for each data segment includes: In the data segment, obtain the frequency of the voltage of the battery cell to be detected becoming the lowest voltage; Obtain the sum of the lowest voltage frequencies of all battery cells to be detected in the data segment; Based on the frequency of the voltage of the battery cell to be detected becoming the lowest voltage in the data segment and the sum of the lowest voltage frequencies of all battery cells in the data segment, calculate the low-voltage probability of the battery cell to be detected.

4. The detection method according to claim 2, characterized in that The step of calculating the change rate of the low-voltage probability of the battery cell to be detected within a preset time based on the low-voltage probability of the battery cell to be detected includes: Set the time of each data segment as the X-axis coordinate value; Set the low-voltage probability of the battery cell to be detected for each data segment as the Y-axis coordinate value; Calculate the slope value of the low-voltage probability of the battery cell to be detected relative to the data segment time.

5. The detection method according to claim 1, characterized in that, The preset value is a threshold parameter. If the number of strings of the battery cell to be detected is larger, the threshold parameter is smaller; if the number of strings of the battery cell to be detected is smaller, the threshold parameter is larger.

6. The detection method according to claim 5, wherein The threshold parameter is 1% - 3%.

7. The detection method according to claim 1, wherein The preset value is the upper control limit UCL of the single-value control chart for the change rate of the low-voltage probability of the battery cell to be detected, and the upper control limit UCL = Ω 均 + 3*σ, where Ω 均 is the average value of the change rate of the low-voltage probability of the battery cell to be detected, and σ is the standard deviation of the change rate of the low-voltage probability of the battery cell to be detected.

8. A detection device for self-discharge of a battery, which executes the detection method for self-discharge defects of the battery according to any one of claims 1 to 7, characterized in that, Including: An acquisition unit for obtaining the low-voltage probability of the battery cell to be detected; A calculation unit for calculating the change rate of the low-voltage probability of the battery cell to be detected within a preset time based on the low-voltage probability of the battery cell to be detected; A judgment unit for determining that the battery cell to be detected has a self-discharge defect if the change rate of the low-voltage probability of the battery cell to be detected is greater than a preset value.

9. An electronic device, characterized in that, Including a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, it implements a method for detecting battery self-discharge according to any one of claims 1 to 7.

10. A vehicle, characterized in that, Including an electronic device according to claim 9.

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