Lithium battery pack internal short circuit abnormality diagnosis method and system

Abstract

The present invention provides a method and system for diagnosing abnormal internal short circuit of a lithium battery pack, including: S1: obtaining the voltage of each battery cell and calculating the average voltage of the battery pack; S2: calculating the residual of each battery cell voltage and the average voltage of the battery pack, and respectively calculating the cumulative residual of each battery cell voltage; S3: setting the threshold range of the current diagnosis cycle, determining the battery cell whose cumulative residual of the battery cell voltage exceeds the threshold range of the current diagnosis cycle as an abnormal battery cell, and determining the battery cell whose cumulative residual of the battery cell voltage is within the threshold range of the current diagnosis cycle as a normal battery cell; S4: returning to step S1 after excluding the abnormal battery cell, until no abnormal battery cell is detected within a preset time period, the diagnosis ends and the diagnosis result is output. The present invention has a fast detection speed and high detection accuracy, can quickly and accurately realize the abnormal diagnosis of the internal short circuit of the battery pack, find the battery cell with a faster internal short circuit development, give an early warning, and improve the overall operation safety of the battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery pack internal short circuit detection, and in particular to a lithium battery pack internal short circuit abnormality diagnosis method and system. Background Art

[0002] In recent years, most lithium battery fire accidents are caused by thermal runaway, and the threat of thermal runaway to the operational safety of lithium batteries has become a prominent problem that needs to be solved urgently. In practical applications, lithium battery packs are usually composed of dozens, hundreds or even thousands of lithium batteries connected in series and parallel. When one of the batteries has thermal runaway, the phenomenon may spread within the battery pack, causing fire, explosion or other serious consequences. Usually, the main cause of thermal runaway is a serious short circuit inside the lithium battery caused by mechanical, electrical and thermal abuse. The Joule heat generated by the short-circuit current inside the battery will cause the battery temperature to rise. If the local heat accumulates enough to trigger thermal runaway, catastrophic accidents such as fire and explosion will occur. Internal short circuit has become an important cause of threat to the overall safety of batteries.

[0003] Internal short circuits in the middle and late stages have obvious thermal and electrical characteristics and are very easy to identify, but the corresponding probability of developing into thermal runaway also increases dramatically. The internal short circuit has a long development cycle, and the early effects are not significant, making it difficult to identify and monitor, but this also provides a sufficient time window for the discovery and early warning of internal short circuits, and provides the possibility for early prevention of thermal runaway. In order to increase the safety and reliability of battery packs, it is necessary to monitor the development of internal short circuits in battery packs. However, in the early stages of internal short circuits, the voltage and pressure difference, temperature and temperature difference changes of each battery cell in the battery pack are very small, and are easily affected by noise, load current and ambient temperature fluctuations. It is difficult to identify abnormal battery signals caused by early weak internal short circuits. Therefore, how to identify abnormal behavior of battery packs in the early stages of internal short circuits and improve the safety of battery packs has become one of the urgent problems to be solved by those skilled in the art. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method and system for diagnosing abnormal internal short circuit of a lithium battery pack, so as to solve the problem in the prior art that internal short circuit of a battery pack is difficult to identify and monitor at an early stage.

[0005] To achieve the above-mentioned object and other related objects, the present invention provides a method for diagnosing an abnormal short circuit in a lithium battery pack, the method for diagnosing an abnormal short circuit in a lithium battery pack at least comprising:

[0006] S1: Get the voltage of each battery cell and calculate the average voltage of the battery pack;

[0007] S2: Calculate the residual of each cell voltage and the average voltage of the battery pack, and calculate the cumulative residual of each cell voltage respectively;

[0008] S3: setting a threshold range of the current diagnosis cycle, determining a cell whose cumulative residual of cell voltage exceeds the threshold range of the current diagnosis cycle as an abnormal cell, and determining a cell whose cumulative residual of cell voltage is within the threshold range of the current diagnosis cycle as a normal cell;

[0009] S4: After eliminating the abnormal cells, return to step S1 until no abnormal cells are detected within a preset time period, the diagnosis is completed and the diagnosis result is output.

[0010] Optionally, in step S1, after acquiring the voltage of each battery cell, smoothing processing is performed on the voltage of each battery cell.

[0011] More optionally, the smoothing process is implemented by a filtering algorithm, and the filtering algorithm includes any one of a median filtering algorithm, a sliding average filtering algorithm, and a fast Fourier transform filtering algorithm.

[0012] Optionally, the threshold range of the current diagnosis cycle is obtained based on the statistical characteristics of the accumulated residual of each cell voltage in the previous step.

[0013] More optionally, the threshold range of the current diagnosis cycle is a 95% confidence interval of the cumulative residual of each cell voltage.

[0014] Optionally, the diagnosis result includes whether there are abnormal battery cells.

[0015] More optionally, the diagnosis result also includes the serial number corresponding to the abnormal battery cell.

[0016] In order to achieve the above-mentioned object and other related objects, the present invention provides a lithium battery pack internal short circuit abnormality diagnosis system, and the lithium battery pack internal short circuit abnormality diagnosis system at least comprises:

[0017] Input module, voltage processing module, voltage residual extraction module, threshold generation module, internal short circuit identification module and output module;

[0018] The input module obtains the voltage of each battery cell and inputs the voltage of each battery cell to the voltage processing module and the voltage residual extraction module;

[0019] The voltage processing module is connected to the input module and the output end of the voltage processing module, and when the internal short circuit identification module outputs a normal battery pack signal, the average voltage of the battery pack is calculated; when the internal short circuit identification module outputs an abnormal battery cell number, the corresponding battery cell voltage input by the input module is excluded and the average voltage of the battery pack is calculated;

[0020] The voltage residual extraction module is connected to the input module and the output end of the voltage processing module to extract the accumulated residual of the cell voltage in the current diagnosis cycle;

[0021] The threshold generation module is connected to the output end of the voltage residual extraction module, and updates and generates the threshold range of the current diagnosis cycle;

[0022] The internal short circuit identification module is connected to the output end of the threshold generation module, and determines whether there is an abnormal battery cell based on the threshold range of the current diagnosis cycle;

[0023] The output module is connected to the output end of the internal short circuit identification module and outputs the diagnosis result.

[0024] Optionally, the voltage processing module further includes a smoothing processing unit, and the smoothing processing unit performs smoothing processing on the cell voltage input by the input module.

[0025] More optionally, the smoothing processing unit includes but is not limited to a median filter, a sliding average filter or a fast Fourier transform filter.

[0026] Optionally, the threshold generation module generates a threshold range for the current diagnosis cycle based on statistical characteristics of the accumulated residuals of the voltages of the battery cells in the current diagnosis cycle.

[0027] More optionally, the threshold range of the current diagnosis cycle is a 95% confidence interval of the cumulative residual of each cell voltage.

[0028] As described above, the lithium battery pack internal short circuit abnormality diagnosis method and system of the present invention have the following beneficial effects:

[0029] 1. The lithium battery pack internal short circuit abnormality diagnosis method and system of the present invention has a short detection cycle and can diagnose abnormal cells with serious internal short circuits in the battery pack within a few hours.

[0030] 2. The lithium battery pack internal short circuit abnormality diagnosis method and system of the present invention have strong diagnostic recognition capabilities and can locate the position of abnormal battery cells. Abnormal battery cells can be discovered in the early stage of internal short circuit, preventing the internal short circuit from developing and worsening, causing serious accidents such as thermal runaway, and improving the safety level of the battery pack.

[0031] 3. The algorithm of the lithium battery pack internal short circuit abnormality diagnosis method and system of the present invention has low time and space complexity, simple diagnostic technical process, and low requirements for data accuracy, and has great application value in the safe operation of lithium-ion battery packs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of a curve showing the residual voltage of a battery cell of the present invention.

[0033] Figure 2 It is a schematic flow chart of the method for diagnosing anomalies of short circuit inside a lithium battery pack of the present invention.

[0034] Figure 3 It is a schematic diagram showing the principle of the method for diagnosing abnormal short circuit in a lithium battery pack according to the present invention for diagnosing abnormal battery cell No. 1.

[0035] Figure 4 It is a schematic diagram showing the principle of the method for diagnosing abnormal short circuit in a lithium battery pack according to the present invention for diagnosing abnormal cell No. 2.

[0036] Figure 5 Shown is a schematic structural diagram of the lithium battery pack internal short circuit anomaly diagnosis system of the present invention.

[0037] Component number description

[0038] 1 Input module

[0039] 2 Voltage Processing Module

[0040] 3 Voltage residual extraction module

[0041] 4 Threshold Generation Module

[0042] 5 Internal short circuit identification module

[0043] 6 Output Module

[0044] Steps S1 to S4 DETAILED DESCRIPTION

[0045] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0046] See also Figure 1 to Figure 5 It should be noted that the illustrations provided in this embodiment are only used to schematically illustrate the basic concept of the present invention, and the drawings only show components related to the present invention rather than being 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 complicated.

[0047] like Figure 1 As shown, each cell in the battery pack is measured for 2 hours, and the curve of the residual voltage of each cell is obtained, among which the residuals of abnormal cell 1 and abnormal cell 2 obviously deviate from the residual curves of the other cells in the battery pack; thus, it can be seen that the residual of the cell voltage is a signal indicator that can effectively distinguish abnormal cells. The present invention is based on the detection of the residual of the cell voltage to realize the early identification of the short circuit of the battery pack in the internal short circuit, thereby improving the safety of the battery pack, and the implementation method is as follows.

[0048] Embodiment 1

[0049] like Figure 2 As shown, this embodiment provides a method for diagnosing an abnormal short circuit inside a lithium battery pack, and the method for diagnosing an abnormal short circuit inside a lithium battery pack includes:

[0050] S1 Battery pack voltage processing steps: obtain the voltage of each battery cell and calculate the average voltage of the battery pack.

[0051] Specifically, the collected voltages of the battery cells are acquired, and the sum of the voltages of the battery cells is divided by the number of battery cells to obtain the average voltage of the battery pack.

[0052] Specifically, in this embodiment, in order to improve the accuracy of diagnosis, the battery pack to be diagnosed is placed in an environment with a stable ambient temperature to avoid the influence of temperature on the voltage of each cell in the battery pack. In actual use, the influence of ambient temperature on the final battery pack internal short circuit abnormality diagnosis result can be ignored, which is not limited to this embodiment.

[0053] Specifically, since the collected cell voltage contains a large random error, in the early stage of the internal short circuit, the voltage change caused by the internal short circuit is weak, which is comparable to the random error and difficult to distinguish. Therefore, in this embodiment, in order to improve the accuracy of the diagnosis, before calculating the average voltage of the battery pack, each cell voltage is first smoothed to eliminate the random error of a single measurement. As an example, a filtering algorithm is used to achieve smoothing, and the filtering algorithm includes but is not limited to a median filtering algorithm, a sliding average filtering algorithm, and a fast Fourier transform filtering algorithm. Any filtering algorithm that can smooth the cell voltage is applicable to the present invention and will not be described one by one here.

[0054] S2: Step of obtaining the accumulated residual of each cell voltage: calculating the residual of each cell voltage and the average voltage of the battery pack, and calculating the accumulated residual of each cell voltage respectively.

[0055] Specifically, the difference between each cell voltage and the average voltage of the battery pack is obtained, which are recorded as the residual of each cell voltage, and the residuals of the same cell voltage are accumulated to obtain the cumulative residual of the cell voltage, wherein the cumulative residual of each cell voltage in the first diagnostic cycle is the residual of each current cell voltage.

[0056] It should be noted that after the voltage of each cell is smoothed in step S1, the voltage of each cell in step S2 is the voltage value after the smoothing process. Figure 1 It can be seen that the residual of the cell voltage has certain fluctuations. In order to obtain a more stable identification index signal, the present invention accumulates the residuals to amplify the residual signal and obtain a more stable judgment criterion, so that the accuracy of the diagnosis result is higher.

[0057] S3 threshold range acquisition and judgment step of whether the battery cell is abnormal: set the threshold range of the current diagnosis cycle, and judge the battery cell whose cumulative residual of the battery cell voltage exceeds the threshold range of the current diagnosis cycle as an abnormal battery cell, and judge the battery cell whose cumulative residual of the battery cell voltage is within the threshold range of the current diagnosis cycle as a normal battery cell.

[0058] Specifically, in this embodiment, the distribution of the cumulative residuals of each battery cell voltage in the current diagnosis cycle is obtained, and the threshold range of the current diagnosis cycle is set according to the statistical characteristics. The specific threshold range can be set according to actual needs. As an example, the threshold range of the current diagnosis cycle is set to the 95% confidence interval of the cumulative residuals of each battery cell voltage (that is, according to statistical laws, 95% of the battery cell voltage values ​​should be distributed within the threshold range). Further, the upper limit of the 95% confidence interval is the average value of the cumulative residuals of each battery cell voltage plus 1.96 times the standard deviation, and the lower limit is the average value of the cumulative residuals of each battery cell voltage minus 1.96 times the standard deviation.

[0059] It should be noted that in actual use, the threshold range of the current diagnostic cycle can be a preset value, and there is no need to obtain it based on the distribution of the cumulative residuals of the voltages of each battery cell in the current diagnostic cycle. In this embodiment, setting the threshold range of the current diagnostic cycle to the 95% confidence interval of the cumulative residuals of the voltages of each battery cell can greatly speed up the diagnosis while ensuring accuracy.

[0060] Specifically, if the accumulated residual of the cell voltage is within the threshold range of the current diagnosis cycle, the cell is determined to be a normal cell; if the accumulated residual of the cell voltage exceeds the threshold range of the current diagnosis cycle, the cell is determined to be an abnormal cell.

[0061] S4 loop diagnosis step: after eliminating abnormal cells, return to step S1 until no abnormal cells are detected within a preset time period, the diagnosis is completed and the diagnosis result is output.

[0062] Specifically, the abnormal cells are excluded, and then the cell voltages are reacquired based on the remaining cells and the next diagnostic cycle is performed. The threshold range of each diagnostic cycle needs to be updated; in the present embodiment, the threshold range of each diagnostic cycle is acquired based on the statistical characteristics of the cumulative residuals of the cell voltages in the previous step. If no abnormal cells are detected within the preset time period, the diagnosis is deemed to be completed, and the diagnostic result is output. The output result includes whether there are abnormal cells, and if there are abnormal cells, the number corresponding to the abnormal cells is output. Among them, the duration of the preset time period (multiple diagnostic cycles can be executed within the preset time period) can be set according to actual needs to ensure that accurate diagnostic results can be obtained, and they will not be elaborated here.

[0063] like Figure 3As shown, it is the cumulative residual distribution of each cell voltage. Each cell (distinguished by different cell numbers) has a corresponding cumulative residual, where the dotted lines represent the upper and lower limits of the threshold range, respectively. Between the upper and lower limits is within the threshold range, and greater than the upper limit or less than the lower limit is beyond the threshold range. Assume that there are abnormal cell No. 1 and abnormal cell No. 2, and the other cells are normal. The cumulative residual distribution of the normal cells is uniform and within the threshold range; and in the current diagnosis cycle, the cumulative residual of the cell voltage of abnormal cell No. 1 is lower than the lower limit of the threshold range (exceeding the threshold range), and cell No. 1 is diagnosed as an abnormal cell; the cumulative residual of the cell voltage of abnormal cell No. 2 is within the threshold range of the current diagnosis cycle. Therefore, although its cumulative residual obviously deviates from the cumulative residual distribution of normal cells, it is not diagnosed as an abnormal cell. After excluding abnormal cell No. 1, continue to perform the cycle diagnosis steps.

[0064] like Figure 4 As shown, after the next diagnosis cycle or multiple diagnosis cycles (within the preset time period), the accumulated residual of the cell voltage of abnormal cell No. 2 is lower than the lower limit of the threshold range (new threshold range) of the current diagnosis cycle (exceeding the threshold range), and cell No. 2 is diagnosed as an abnormal cell. After excluding abnormal cell No. 2, the steps of cyclic diagnosis are continued. If no abnormal cell is detected after the preset time, the diagnosis ends and the diagnosis result is output: there are abnormal cells, and the abnormal cells are numbered V102 and V103.

[0065] The present invention uses the cumulative residual of the cell voltage as an intuitive quantitative determination index to identify and diagnose abnormal cells, and can effectively distinguish abnormal cells. It is worth noting that in this embodiment, the abnormal cells in the battery pack can be determined after only 2 hours of measurement, which proves that this method can quickly diagnose abnormal cells with short circuits inside the battery pack.

[0066] Embodiment 2

[0067] like Figure 5 As shown, this embodiment provides a lithium battery pack internal short circuit abnormality diagnosis system, and the lithium battery pack internal short circuit abnormality diagnosis system includes:

[0068] Input module 1, voltage processing module 2, voltage residual extraction module 3, threshold generation module 4, internal short circuit identification module 5 and output module 6.

[0069] like Figure 5 As shown, the input module 1 obtains the voltage of each battery cell and inputs the voltage of each battery cell to the voltage processing module 2 and the voltage residual extraction module 3.

[0070] like Figure 5As shown, the voltage processing module 2 is connected to the input module 1 and the output end of the voltage processing module 2, and when the internal short circuit identification module 5 outputs a normal battery pack signal, the average voltage of the battery pack is calculated; when the internal short circuit identification module 5 outputs an abnormal battery cell number, the corresponding battery cell voltage input by the input module 1 is excluded and the average voltage of the battery pack is calculated.

[0071] Specifically, the voltage processing module 2 includes an average value calculation unit, and any hardware circuit or software code that can select an input signal and calculate the average value of the selected signal is applicable to the present invention.

[0072] Specifically, as another implementation of the present invention, the voltage processing module 2 further includes a smoothing processing unit, which smoothes the cell voltage input by the input module 1. The smoothing processing unit includes but is not limited to a median filter, a sliding average filter or a fast Fourier transform filter. In this embodiment, a sliding average filter is used for implementation, and the sliding window length of the sliding average filter can be adjusted as needed; any filtering algorithm unit that can smooth the cell voltage is applicable to the present invention, and will not be described one by one here.

[0073] like Figure 5 As shown, the voltage residual extraction module 3 is connected to the input module 1 and the output end of the voltage processing module 2 to extract the accumulated residual of the cell voltage in the current diagnosis cycle.

[0074] Specifically, the voltage residual extraction module 3 receives the voltage of each battery cell and the average voltage of the battery pack in the current diagnosis cycle, calculates the difference between each battery cell voltage and the average voltage of the battery pack, and adds the difference corresponding to each battery cell with the residual accumulated in the previous diagnosis cycle to obtain the accumulated residual of each battery cell voltage.

[0075] like Figure 5 As shown, the threshold generation module 4 is connected to the output end of the voltage residual extraction module 3 to update and generate the threshold range of the current diagnosis cycle.

[0076] Specifically, in this embodiment, the threshold generation module 4 generates a threshold range for the current diagnostic cycle based on the statistical characteristics of the cumulative residuals of the voltages of each battery cell in the current diagnostic cycle. As an example, the threshold range for the current diagnostic cycle is a 95% confidence interval of the cumulative residuals of the voltages of each battery cell. In actual use, the threshold range can be set as needed, not limited to this embodiment.

[0077] like Figure 5 As shown, the internal short circuit identification module 5 is connected to the output end of the threshold generation module 4, and determines whether there is an abnormal battery cell based on the diagnosis threshold range.

[0078] Specifically, if the cumulative residual of the cell voltage is within the threshold range of the current diagnostic cycle, the cell is determined to be a normal cell and the output of the battery pack is normal; if the cumulative residual of the cell voltage exceeds the threshold range of the current diagnostic cycle, the cell is determined to be an abnormal cell and the abnormal cell label is further output.

[0079] like Figure 5 As shown, the output module 6 is connected to the output end of the internal short circuit identification module 5 to output the diagnosis result.

[0080] The lithium battery pack internal short circuit abnormality diagnosis system of this embodiment can be used to implement the lithium battery pack internal short circuit abnormality diagnosis method of the first embodiment, and the specific principles are not described in detail here.

[0081] The present invention accumulates the voltage residuals of each cell in the battery pack, amplifies the voltage difference caused by the internal short circuit of the battery, especially the early internal micro short circuit, and then compares horizontally based on the statistical information of the accumulated residuals of each cell in the battery pack, and uses the consistency of the accumulated residuals of each cell voltage to diagnose and identify abnormal cells in the battery pack. There is no need to disassemble the cells, only the static cell voltage is collected by the battery management system on the battery board, and the differences in cell manufacturing and capacity will cause differences in the voltages of each cell. Therefore, the present invention uses the residual of the cell voltage, which is a relative quantity, to avoid the voltage difference between cells, so that cells with severe internal short circuits can be extracted within a shorter measurement time range.

[0082] In summary, the present invention provides a method and system for diagnosing an abnormal short circuit in a lithium battery pack, including: S1: obtaining the voltage of each battery cell and calculating the average voltage of the battery pack; S2: calculating the residual between the voltage of each battery cell and the average voltage of the battery pack, and respectively calculating the cumulative residual of each battery cell voltage; S3: setting the threshold range of the current diagnosis cycle, determining the battery cell whose cumulative residual of the battery cell voltage exceeds the threshold range of the current diagnosis cycle as an abnormal battery cell, and determining the battery cell whose cumulative residual of the battery cell voltage is within the threshold range of the current diagnosis cycle as a normal battery cell; S4: returning to step S1 after excluding the abnormal battery cell, until no abnormal battery cell is detected within the preset time period, the diagnosis ends and the diagnosis result is output. The lithium battery pack internal short circuit abnormality diagnosis method and system of the present invention amplifies the voltage difference caused by internal short circuit, especially early internal micro short circuit, by accumulating voltage residuals, and then uses the distribution of the cumulative residuals of each battery cell in the battery pack and the 95% confidence interval as the threshold, and compares horizontally, and uses the consistency of the cumulative residuals of each battery cell voltage to diagnose and identify abnormal batteries in the battery pack; the detection speed is fast, the detection accuracy is high, and the internal short circuit abnormality diagnosis of the battery pack can be realized quickly and accurately, and the battery cells with faster internal short circuit development can be found, and early warning can be given to improve the overall operation safety of the battery pack. Therefore, the present invention effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0083] 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 familiar with 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 a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for diagnosing an internal short circuit abnormality of a lithium battery pack, characterized in that: The lithium battery pack internal short circuit abnormality diagnosis method at least includes: S1: Get the voltage of each battery cell and calculate the average voltage of the battery pack; S2: Calculate the residual of each cell voltage and the average voltage of the battery pack, and calculate the cumulative residual of each cell voltage respectively; S3: setting a threshold range of the current diagnosis cycle, determining a cell whose cumulative residual of cell voltage exceeds the threshold range of the current diagnosis cycle as an abnormal cell, and determining a cell whose cumulative residual of cell voltage is within the threshold range of the current diagnosis cycle as a normal cell; S4: After eliminating the abnormal cells, return to step S1 until no abnormal cells are detected within a preset time period, the diagnosis is completed and the diagnosis result is output.

2. The method for diagnosing an internal short circuit abnormality of a lithium battery pack according to claim 1, characterized in that: In step S1, after the voltage of each battery cell is acquired, the voltage of each battery cell is smoothed.

3. The method for diagnosing an internal short circuit abnormality of a lithium battery pack according to claim 2, characterized in that: The smoothing process is implemented by using a filtering algorithm, and the filtering algorithm includes any one of a median filtering algorithm, a sliding average filtering algorithm, and a fast Fourier transform filtering algorithm.

4. The method for diagnosing an internal short circuit abnormality of a lithium battery pack according to claim 1, characterized in that: The threshold range of the current diagnosis cycle is obtained based on the statistical characteristics of the cumulative residual of each cell voltage in the previous step.

5. The method for diagnosing an internal short circuit abnormality of a lithium battery pack according to claim 1 or 4, characterized in that: The threshold range of the current diagnosis cycle is the 95% confidence interval of the cumulative residual of each cell voltage.

6. The method for diagnosing an internal short circuit abnormality of a lithium battery pack according to claim 1, characterized in that: The diagnosis result includes whether there is an abnormal battery cell and the abnormal battery cell number.

7. A lithium battery pack internal short circuit abnormality diagnosis system, characterized in that: The lithium battery pack internal short circuit abnormality diagnosis system at least includes: Input module, voltage processing module, voltage residual extraction module, threshold generation module, internal short circuit identification module and output module; The input module obtains the voltage of each battery cell and inputs the voltage of each battery cell to the voltage processing module and the voltage residual extraction module; The voltage processing module is connected to the input module and the output end of the voltage processing module, and when the internal short circuit identification module outputs a normal battery pack signal, the average voltage of the battery pack is calculated; when the internal short circuit identification module outputs an abnormal battery cell number, the corresponding battery cell voltage input by the input module is excluded and the average voltage of the battery pack is calculated; The voltage residual extraction module is connected to the input module and the output end of the voltage processing module to extract the accumulated residual of the cell voltage in the current diagnosis cycle; The threshold generation module is connected to the output end of the voltage residual extraction module, and updates and generates the threshold range of the current diagnosis cycle; The internal short circuit identification module is connected to the output end of the threshold generation module, and determines whether there is an abnormal battery cell based on the threshold range of the current diagnosis cycle; The output module is connected to the output end of the internal short circuit identification module and outputs the diagnosis result.

8. The lithium battery pack internal short circuit abnormality diagnosis system according to claim 7, characterized in that: The voltage processing module further includes a smoothing processing unit, which performs smoothing processing on the cell voltage input by the input module.

9. The lithium battery pack internal short circuit abnormality diagnosis system according to claim 8, characterized in that: The smoothing processing unit includes a median filter, a sliding average filter or a fast Fourier transform filter.

10. The lithium battery pack internal short circuit abnormality diagnosis system according to claim 7, characterized in that: The threshold generation module generates a threshold range of the current diagnosis cycle based on statistical characteristics of the accumulated residuals of the voltages of the cells in the current diagnosis cycle.

11. The lithium battery pack internal short circuit abnormality diagnosis system according to claim 10, characterized in that: The threshold range of the current diagnosis cycle is the 95% confidence interval of the cumulative residual of each cell voltage.

Images