A Layered Fault Diagnosis Method and System for Battery Modules Based on Cumulative Probability Distribution

Abstract

This invention discloses a hierarchical fault diagnosis method and system for battery packs based on cumulative probability distribution, comprising: acquiring the maximum voltage sequence and minimum voltage sequence of the battery pack under test; calculating the cumulative probability distribution of the maximum voltage sequence and the minimum voltage sequence respectively; determining whether the battery pack has a fault and the fault type based on the obtained cumulative probability distribution; for battery packs determined not to have a fault, acquiring the voltage sequence of the individual battery cells in the battery pack; calculating the cumulative probability distribution of the individual battery cell voltage sequence, and determining whether the individual battery cell has a fault and the fault type based on the calculated cumulative probability distribution. This invention establishes a hierarchical fault diagnosis architecture, which can effectively detect the fault types of the battery pack and individual battery cells, and accurately detect early faults in individual battery cells.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery fault diagnosis technology, and in particular to a stratified fault diagnosis method and system for battery packs based on cumulative probability distribution. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Battery packs, as important energy storage devices, play a crucial role in electric vehicles, renewable energy power plants, and portable electronic devices. However, due to the different operating environments and conditions they face during long-term use, battery packs are susceptible to a series of potential failures.

[0004] Currently, battery pack fault diagnosis methods rely on traditional battery parameter measurements such as voltage, current, and temperature. While these methods can detect abnormal states in the battery pack to some extent, the complex chemical reactions and interactions within the battery pack prevent them from accurately and promptly identifying potential faults and providing necessary early warnings. Furthermore, existing fault diagnosis methods are often limited by high equipment costs, slow response times, and the impact on equipment operation. This makes real-time monitoring and fault diagnosis of the battery pack's health status difficult to achieve, thus affecting the battery pack's safety, reliability, and lifespan.

[0005] Existing battery pack fault diagnosis methods mainly include threshold-based methods, model-based methods, and data-driven methods.

[0006] Threshold-based methods detect battery faults by setting various thresholds, typically including parameters such as voltage, current, temperature, and internal resistance. For example, existing technology discloses a fault identification method for energy storage batteries. This method calculates various safety thresholds using a weighted average method, and then uses these thresholds to predict and warn of inconsistencies in voltage, temperature, and internal resistance among individual cells in the battery module in real time. However, threshold-based methods have several limitations: first, parameter fluctuations caused by early-stage faults generally do not reach the preset thresholds; second, selecting appropriate thresholds is challenging.

[0007] Model-based methods require the creation of accurate battery models for fault detection, and are computationally expensive, have poor robustness, and are difficult to implement online.

[0008] Data-driven approaches utilize real-world observational data and machine learning techniques to analyze battery behavior and performance, uncovering patterns and dynamics in battery failures to diagnose them. However, this method requires vast amounts of data to establish the underlying relationships between battery parameters and failures, resulting in high computational costs, poor robustness, and difficulty in online implementation. Summary of the Invention

[0009] To address the aforementioned issues, this invention proposes a battery pack stratified fault diagnosis method and system based on cumulative probability distribution. By using the cumulative probability distribution algorithm, the complex voltage data is transformed into cumulative probability values, alleviating data redundancy, reducing computational costs, and simultaneously identifying minor battery pack faults, achieving a balance between diagnostic accuracy and diagnostic efficiency.

[0010] In some implementations, the following technical solutions are adopted:

[0011] A stratified fault diagnosis method for battery packs based on cumulative probability distribution includes:

[0012] Obtain the maximum and minimum voltage sequences of the battery pack under test;

[0013] Calculate the cumulative probability distributions of the maximum and minimum voltage sequences respectively; based on the obtained cumulative probability distributions, determine whether the battery pack has failed and the type of failure.

[0014] For battery packs that are determined not to have faults, obtain the voltage sequence of the individual battery cells in the battery pack; calculate the cumulative probability distribution of the individual battery cell voltage sequence, and determine whether the individual battery cell has faulted and the type of fault based on the calculated cumulative probability distribution;

[0015] For individual cells that are determined not to have malfunctioned, obtain the voltage sequence of the individual cell, calculate the average voltage sequence of the battery pack, and sort the voltage sequence and the average voltage sequence of the battery pack.

[0016] The sorted voltage sequence and average voltage sequence are divided into multiple data windows using a preset data window;

[0017] Calculate the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack within each data window;

[0018] Calculate the total error between the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack within each data window, and determine whether the individual cell has failed and the type of failure based on the total error.

[0019] Specifically, the cumulative probability distribution of the individual cell voltage sequence within each data window is calculated as follows:

[0020] For the voltage sequence of a single cell within each data window, it is divided into a set number of numerical intervals of equal length; the number of voltage data points falling within each numerical interval is calculated in turn, and this number is divided by the total number of data points to calculate the cumulative probability of that numerical interval; the cumulative probabilities of all numerical intervals are summed to form the cumulative probability distribution of the single cell within the data window.

[0021] In other embodiments, the following technical solutions are adopted:

[0022] A battery pack stratified fault diagnosis system based on cumulative probability distribution, comprising:

[0023] The battery pack fault diagnosis module is used to obtain the maximum voltage sequence and minimum voltage sequence of the battery pack under test; calculate the cumulative probability distribution of the maximum voltage sequence and minimum voltage sequence respectively; and determine whether the battery pack has a fault and the type of fault based on the obtained cumulative probability distribution.

[0024] The first battery cell fault diagnosis module is used to obtain the voltage sequence of the battery cells in the battery pack for battery packs that have been determined not to have faults; calculate the cumulative probability distribution of the battery cell voltage sequence; and determine whether the battery cell has faulted and the fault type based on the calculated cumulative probability distribution.

[0025] The second battery cell fault diagnosis module is used to acquire the voltage sequence of a single battery cell that has been determined not to have a fault, calculate the average voltage sequence of the battery pack, and sort the voltage sequence and the average voltage sequence of the battery pack. The sorted voltage sequence and the average voltage sequence are divided into multiple data windows through a preset data window. The cumulative probability distribution of the single battery cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack are calculated in each data window. The total error between the cumulative probability distribution of the single battery cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack is calculated in each data window, and the single battery cell is determined to have a fault and the fault type based on the total error.

[0026] In other embodiments, the following technical solutions are adopted:

[0027] A terminal device includes a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded and executed by the processor to perform the above-described battery pack stratified fault diagnosis method based on cumulative probability distribution.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] (1) The present invention establishes a hierarchical fault diagnosis architecture, which can effectively detect the fault types of battery packs and individual battery cells, and can accurately detect early faults in individual battery cells.

[0030] Specifically, this invention first calculates the cumulative probability distribution of the battery pack voltage sequence to diagnose the battery pack fault; for battery packs that are determined to be fault-free, the cumulative probability distribution of the individual battery cell voltage sequence is further used to diagnose the individual battery cells fault; the fault diagnosis method is simple, the calculation process is simple, and it can achieve rapid fault diagnosis.

[0031] For battery cells determined to be fault-free, a data window was divided. Within each data window, voltage fluctuations caused by minor faults are more pronounced. A cumulative probability algorithm is used to process the individual cell voltage and the average voltage of the battery pack within that window. Finally, the total error between the cumulative probability distributions of the two is calculated to diagnose minor faults, thereby improving diagnostic accuracy and avoiding potential safety hazards to the battery caused by minor faults. This achieves a balance between fault diagnosis accuracy and efficiency.

[0032] (2) The method of the present invention transforms the complex voltage data into cumulative probability values ​​through the cumulative probability distribution algorithm, which alleviates data redundancy, reduces the difficulty of data retrieval, and improves the algorithm's calculation efficiency and diagnostic efficiency.

[0033] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0034] Figure 1 This is a flowchart of a battery pack stratified fault diagnosis based on cumulative probability distribution in an embodiment of the present invention.

[0035] Figure 2 This is a schematic diagram illustrating the process of calculating the cumulative probability distribution of a voltage sequence in an embodiment of the present invention;

[0036] Figure 3 This is a voltage curve of the battery pack under UDDS operating conditions in an embodiment of the present invention;

[0037] Figure 4 This is a sequence diagram of the maximum and minimum voltage values ​​of the battery pack in an embodiment of the present invention;

[0038] Figure 5 This is a cumulative probability distribution diagram corresponding to the maximum voltage sequence in an embodiment of the present invention;

[0039] Figure 6 This is a cumulative probability distribution diagram corresponding to the minimum voltage sequence in an embodiment of the present invention;

[0040] Figure 7This is a schematic diagram of the battery pack fault diagnosis results in an embodiment of the present invention;

[0041] Figure 8 This is a voltage sequence diagram of the battery cell in section 7 of this embodiment of the invention;

[0042] Figure 9 This is a schematic diagram of the battery cell fault diagnosis results in an embodiment of the present invention;

[0043] Figure 10 This is the single-unit voltage sequence diagram in Section 6 of the embodiments of the present invention;

[0044] Figure 11 This is a schematic diagram of the battery cell minor fault diagnosis results in an embodiment of the present invention. Detailed Implementation

[0045] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0046] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0047] Example 1

[0048] Probabilistic analysis is a mathematical and statistical tool used to study and quantify the likelihood and uncertainty of events. Algorithms based on probabilistic analysis have wide applications in fields such as financial investment, engineering reliability analysis, and risk assessment. The cumulative probability distribution algorithm proposed in this invention can transform time series data into non-time series data, which has potential application value in the field of battery fault diagnosis.

[0049] Voltage is a crucial parameter of a battery, dynamically reflecting certain battery characteristics. Voltage fluctuations or changes over time can be represented as a time series, which can characterize battery malfunctions under specific circumstances. When a battery cell experiences a short circuit at a certain moment, its voltage will suddenly drop at that instant. Conversely, if a cell's voltage suddenly surges, this phenomenon may indicate an open circuit fault or sensor malfunction in that cell.

[0050] Combination Figure 2 The specific process of optimizing the data structure of the voltage data sequence based on the cumulative probability distribution algorithm in this embodiment is as follows:

[0051] Obtain the voltage sequence to be processed. The voltage sequence can be the maximum and minimum voltage sequence in the battery pack, or it can be the voltage sequence of a single battery cell.

[0052] Extract the maximum and minimum voltage values ​​from the voltage sequence to be processed;

[0053] Based on the maximum and minimum voltage values, all voltage data are sorted in ascending or descending order, and the resulting new sequence is stored.

[0054] Based on the maximum voltage value, the minimum voltage value, and the new sequence, set 10 numerical intervals of equal length;

[0055] Based on the new sequence and numerical intervals, the number of voltage data points falling within each numerical interval is calculated sequentially, and this number is divided by the total number of data points to calculate the cumulative probability.

[0056] Summarize all cumulative probabilities to obtain the cumulative probability distribution of the voltage sequence to be processed.

[0057] As a concrete example, suppose a battery pack consists of m individual cells, and the total number of voltage data points for each cell is t. The voltage sequence of each cell in the battery pack is defined as:

[0058]

[0059] Taking the first single cell as an example, the voltage sequence of this single cell is defined as follows:

[0060] v1={v1(1),v1(2),v1(3),…,v1(t-1),v1(t)} (2)

[0061] Based on the minimum and maximum voltage values, the individual voltage data points are sorted in ascending order, and this new sequence is stored in set D. Set D is defined as follows:

[0062]

[0063] Based on set D, 10 numerical intervals of equal length are defined as follows:

[0064]

[0065] Calculate the number K of voltage data points falling within each numerical range in turn. i+1 The cumulative probability p is calculated by dividing this number by the total number of data points. i+1 :

[0066]

[0067] Summarizing all cumulative probabilities, the cumulative probability distribution P1 of the first individual is defined as:

[0068] P1 = {p1, p2, p3, ..., p} l} (6)

[0069] Based on the aforementioned voltage sequence data structure optimization method, in one or more embodiments, a battery pack stratified fault diagnosis method based on cumulative probability distribution is disclosed, combined with... Figure 1 Specifically, it includes the following process:

[0070] (1) Method A: The cumulative probability distribution algorithm is applied to the maximum and minimum voltage sequences of the battery pack for rapid detection and diagnosis of battery pack faults; the specific process is as follows:

[0071] (1-1) Obtain the maximum voltage sequence and minimum voltage sequence of the battery pack under test;

[0072] In this embodiment, the maximum voltage sequence of the battery pack is composed of the maximum voltage of each individual battery cell in the battery pack; the minimum voltage sequence of the battery pack is composed of the minimum voltage of each individual battery cell in the battery pack.

[0073] The relationship between the maximum voltage data point of the battery pack and the voltage data point of a single cell is defined as follows:

[0074]

[0075] The relationship between the minimum voltage data point of the battery pack and the voltage data point of a single cell is defined as follows:

[0076]

[0077] The sequence of maximum voltages of individual cells in the battery pack is as follows:

[0078] v max ={v max (1),v max (2),…,v max (t)}

[0079] The minimum voltage sequence of individual cells in the battery pack is as follows:

[0080] v min ={v min (1),v min (2),…,v min (t)}

[0081] Among them, v max (1) v max (2) and v max (t) represent the largest voltage values ​​of the 1st, 2nd, and tth cells, respectively; vmin (1) v min (2) and v min (t) represent the smallest 1st, 2nd, and tth voltage values ​​among all individual cells, respectively; v m (1) v m (2) and v m (t) represents the 1st, 2nd and tth voltage values ​​of the mth cell, respectively.

[0082] (1-2) Using the method described above for calculating the cumulative probability distribution of the voltage sequence to be processed, the maximum voltage sequence and the minimum voltage sequence are taken as the voltage sequences to be processed, and the cumulative probability distribution of the maximum voltage sequence and the minimum voltage sequence are calculated respectively; the specific process will not be described in detail.

[0083] (1-3) Based on the obtained cumulative probability distribution, determine whether the battery pack has failed and the type of failure; specifically:

[0084] Select the middle range of values ​​as the reference range;

[0085] If the cumulative probability of one or more value intervals in the reference value interval or the value interval before the reference value interval is lower than the set threshold, then the unit has a short circuit fault.

[0086] If, in the numerical range following the reference range, the cumulative probability of one or more numerical ranges is lower than the set threshold, then the battery pack has an open circuit fault.

[0087] In this embodiment, the threshold value is set to r = 0.008.

[0088] For example: Assuming there are a total of 10 numerical intervals, the 5th numerical interval is selected as the reference numerical interval; if the cumulative probability of one or more numerical intervals in the 1st to 5th numerical intervals is lower than the set threshold of 0.008, then the single cell has a short circuit fault; if the cumulative probability of one or more numerical intervals in the 6th to 10th numerical intervals is lower than the set threshold of 0.008, then the battery pack has an open circuit fault.

[0089] (2) Method B: The cumulative probability distribution algorithm is applied to the fault-free single-cell voltage sequence detected by Method A, for rapid detection and diagnosis of single-cell faults; the specific process is as follows:

[0090] (2-1) Extract the voltage sequence of the cell for which faulty cell was not detected by method A;

[0091] (2-2) Using the method described above for calculating the cumulative probability distribution of the voltage sequence to be processed, the individual battery voltage sequence is taken as the voltage sequence to be processed, and the cumulative probability distribution of the individual battery voltage sequence is calculated. The process includes: reordering the individual battery voltage sequences according to a set order; dividing the reordered voltage sequence into a set number of equal-length numerical intervals; calculating the cumulative probability of each numerical interval; and summing the cumulative probabilities of all numerical intervals to form the cumulative probability distribution of the individual battery. Detailed steps will not be elaborated further.

[0092] (2-3) Based on the cumulative probability distribution of the battery cell voltage sequence, determine whether the cell has failed and, if so, the type of failure; the specific determination method is as follows:

[0093] Select the middle range of values ​​as the reference range;

[0094] If the cumulative probability of one or more value intervals in the reference value interval or the value interval before the reference value interval is lower than the set threshold, then the unit has a short circuit fault.

[0095] If, in the numerical intervals following the reference numerical interval, the cumulative probability of one or more numerical intervals is lower than the set threshold, then the individual unit has an open circuit fault.

[0096] In this embodiment, the threshold value is set to r = 0.008.

[0097] (3) Method C: The cumulative probability distribution algorithm is applied to the voltage sequences of fault-free cells detected by methods A and B, for more precise detection and diagnosis of minor individual faults; the specific process is as follows:

[0098] (3-1) Extract the voltage sequences of cells for which no fault was detected by methods A and B, and calculate the average voltage of the battery pack; sort the voltage sequences and the average voltage sequence of the battery pack.

[0099] (3-2) Divide the data window into multiple windows according to the preset data window size (100 seconds);

[0100] (3-3) Calculate the cumulative probability distribution of individual cell voltage and the cumulative probability distribution of average battery pack voltage within different data windows. Using the method described above for calculating the cumulative probability distribution of the voltage sequence to be processed, the sorted individual cell voltage sequence and the average battery pack voltage sequence are respectively used as the voltage sequences to be processed, as follows:

[0101] Based on the voltage sequence of individual cells within each data window, a set number of equal-length numerical intervals are defined. The number of voltage data points falling within each numerical interval is calculated sequentially, and this number is divided by the total number of data points to calculate the cumulative probability of that numerical interval. The cumulative probabilities of all numerical intervals are summarized to form the cumulative probability distribution of individual cells within that data window.

[0102] (3-4) Calculate the total error σ between the cumulative probability distribution of individual cell voltage and the cumulative probability distribution of average battery pack voltage within each numerical window; the specific process is as follows:

[0103] The cumulative probability distribution of individual cell voltage and the cumulative probability distribution of average battery pack voltage each consist of 10 cumulative probability values. For each cumulative probability value, a cumulative probability error is calculated. The total error is obtained by adding up all the cumulative probability errors for the 10 cumulative probability values.

[0104] (3-5) Based on the cumulative probability distribution and the total error σ, determine whether the individual unit has failed, and if so, the type of failure. The specific determination method is as follows:

[0105] Within each data window, calculate the error between the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack; determine the data window corresponding to the maximum error, and select the intermediate data window as the reference data window;

[0106] If the total error σ>t, and the data window corresponding to the maximum error is located before or within the reference data window, then the single cell has a short circuit fault.

[0107] If the total error σ > t, and the data window corresponding to the maximum error is located after the reference data window, then the single cell has a short circuit fault; t is a preset threshold, which is 0.15 in this embodiment.

[0108] For example: a total of 10 data windows are divided, and the 5th data window is selected as the reference data window; if the data window corresponding to the maximum error is the 4th data window, then the unit has a short circuit fault; if the data window corresponding to the maximum error is the 6th data window, then the unit has an open circuit fault.

[0109] The method and effects of this embodiment are illustrated below by comparison and specific examples:

[0110] The battery pack consists of eight cells connected in series: Cell 1, Cell 2, Cell 3, Cell 4, Cell 5, Cell 6, Cell 7, and Cell 8. The UDDS (Underlying Variable Displacement) test was conducted on this series-connected battery pack. Under UDDS conditions, the measured voltage curves of each cell in the battery pack are shown below. Figure 3 As shown. Figure 3As shown, cause Cell 4 to experience both open-circuit and short-circuit faults. Figure 3 The second and third dashed boxes in the middle cause a short circuit fault in Cell 6. Figure 3 The first dashed box in the middle) causes a short circuit fault in Cell7. Figure 3 (The fourth dashed box in the middle). All other units are working normally, and no faults have occurred.

[0111] 1. Rapid detection and diagnosis of battery pack faults based on method A

[0112] (1) Extract the maximum / minimum voltage sequence of the battery pack.

[0113] like Figure 4 As shown, the maximum / minimum voltage sequences of the battery pack were extracted. The abnormal voltage fluctuations of the fourth cell were retained in the maximum voltage sequence of the battery pack, while the abnormal voltage fluctuations of the fourth, sixth, and seventh cells were retained in the minimum voltage sequence of the battery pack.

[0114] (2) Calculate the cumulative probability distribution of the maximum and minimum voltage sequences.

[0115] like Figure 5 As shown, the cumulative probability distribution of the maximum voltage sequence was calculated. Figure 6 As shown, the cumulative probability distribution of the minimum voltage sequence was calculated.

[0116] (3) Based on the cumulative probability distribution of the maximum voltage sequence and the minimum voltage sequence, determine whether the battery pack has failed and the type of failure.

[0117] like Figure 5 As shown, the cumulative probability of the 5th to 10th value ranges is below the threshold, and these value ranges cover a relatively high voltage range (4.08V, 4.17V), indicating an open-circuit fault in the battery pack. Figure 6 As shown, the cumulative probability of the first to third numerical intervals is below the threshold, and these numerical intervals cover a low voltage range (3.89V, 3.95V), indicating a short-circuit fault in the battery pack. Figure 7 As shown, fault diagnosis and location were performed, and it was found that there was an open circuit fault at 621s and short circuit faults near 659s and 1108s.

[0118] The diagnostic time for rapid battery pack fault detection based on Method A is only 0.0003 seconds. It is worth noting that Method A only requires running the cumulative probability distribution algorithm once for each of the maximum and minimum voltage sequences to complete the fault detection task.

[0119] 2. Rapid detection and diagnosis of battery cell faults based on method B

[0120] (1) Extract the voltage sequence of individual battery cells

[0121] This specific case uses the 7th cell as an example to extract the voltage sequence of that cell.

[0122] (2) Calculate the cumulative probability distribution of the battery cell voltage sequence.

[0123] like Figure 8 As shown, the cumulative probability distribution of the voltage of the 7th individual cell was calculated.

[0124] (3) Based on the cumulative probability distribution of the maximum and minimum voltage sequences, determine whether the battery pack has failed and the type of failure.

[0125] like Figure 8 As shown, the cumulative probability of the first to fourth value ranges is below the threshold, and these value ranges cover a relatively high voltage range (3.92V, 3.98V), indicating an open-circuit fault in the battery pack. Figure 9 As shown, fault diagnosis and location were performed, and a short circuit fault was found to exist near 1108s.

[0126] The diagnostic time for battery cell fault detection based on Method B is only 0.001 seconds. It is worth noting that Method B only needs to run the algorithm once for each cell voltage sequence to complete the fault detection task.

[0127] 3. Detection and diagnosis of minor faults in battery cells based on method C

[0128] (1) Extract the voltage sequence of individual battery cells and the average voltage sequence of the battery pack.

[0129] like Figure 10 As shown, this specific case takes the 6th cell as an example, and extracts the voltage sequence and average voltage sequence of the cell.

[0130] (2) Divide the data window into multiple windows according to the preset data window size (100 seconds).

[0131] like Figure 10 As shown, multiple data windows have been created.

[0132] (3) Calculate the cumulative probability distribution of individual cell voltage and the cumulative probability distribution of average battery pack voltage within different data windows.

[0133] Taking the fourth data window as an example, such as Figure 11 As shown, the cumulative probability distribution of individual cell voltage and the cumulative probability distribution of average battery pack voltage are calculated.

[0134] (4) Calculate the total error σ between the cumulative probability distribution of individual cell voltage and the cumulative probability distribution of average battery pack voltage within each numerical window.

[0135] Taking the fourth data window as an example, such as Figure 11 As shown, the total error σ between the cumulative probability distribution of individual cell voltage and the cumulative probability distribution of average battery pack voltage is 1.000.

[0136] (5) Based on the cumulative probability distribution and the total error σ, determine whether the unit has failed and the type of failure.

[0137] Taking the fourth data window as an example, the total error σ>0.15, and the range with the largest error is the fourth data range, which corresponds to a smaller voltage range, indicating that this single unit has a short circuit fault.

[0138] The diagnostic accuracy of battery cell fault detection based on method C is higher than that of methods A and B. It is worth noting that method C requires running the algorithm multiple times for each cell voltage sequence to complete the fault detection task.

[0139] Example 2

[0140] In one or more embodiments, a battery pack stratified fault diagnosis system based on cumulative probability distribution is disclosed, comprising:

[0141] The battery pack fault diagnosis module is used to obtain the maximum voltage sequence and minimum voltage sequence of the battery pack under test; calculate the cumulative probability distribution of the maximum voltage sequence and minimum voltage sequence respectively; and determine whether the battery pack has a fault and the type of fault based on the obtained cumulative probability distribution.

[0142] The first battery cell fault diagnosis module is used to obtain the voltage sequence of the battery cells in the battery pack for battery packs that have been determined not to have faults; calculate the cumulative probability distribution of the battery cell voltage sequence; and determine whether the battery cell has faulted and the fault type based on the calculated cumulative probability distribution.

[0143] The second battery cell fault diagnosis module is used to acquire the voltage sequence of a single battery cell that has been determined not to have a fault, calculate the average voltage sequence of the battery pack, and sort the voltage sequence and the average voltage sequence of the battery pack. The sorted voltage sequence and the average voltage sequence are divided into multiple data windows through a preset data window. The cumulative probability distribution of the single battery cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack are calculated in each data window. The total error between the cumulative probability distribution of the single battery cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack is calculated in each data window, and the single battery cell is determined to have a fault and the fault type based on the total error.

[0144] It should be noted that the specific implementation methods of the above modules are the same as those in Example 1, and will not be described in detail here.

[0145] Example 3

[0146] In one or more embodiments, a terminal device is disclosed, including a server. The server includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the battery pack stratified fault diagnosis method based on cumulative probability distribution described in Embodiment 1. For the sake of brevity, further details are omitted here.

[0147] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0148] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0149] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0150] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A stratified fault diagnosis method for battery packs based on cumulative probability distribution, characterized in that, include: Obtain the maximum and minimum voltage sequences of the battery pack under test; Calculate the cumulative probability distributions of the maximum and minimum voltage sequences, respectively; Based on the obtained cumulative probability distribution, determine whether the battery pack has failed and the type of failure. For battery packs that are determined not to have malfunctioned, obtain the voltage sequence of the individual battery cells in the battery pack; Calculate the cumulative probability distribution of the battery cell voltage sequence, and determine whether the battery cell has failed and the type of failure based on the calculated cumulative probability distribution; Also includes: For individual cells that are determined not to have malfunctioned, obtain the voltage sequence of the individual cell, calculate the average voltage sequence of the battery pack, and sort the voltage sequence and the average voltage sequence of the battery pack. The sorted voltage sequence and average voltage sequence are divided into multiple data windows using a preset data window; Calculate the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack within each data window; Calculate the total error between the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack within each data window, and determine whether an individual cell has failed and the type of failure based on the total error; specifically: Within each data window, calculate the error between the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack; determine the data window corresponding to the maximum error, and select the intermediate data window as the reference data window; If the total error If the data window corresponding to the maximum error is located before or within the reference data window, then the single cell has a short circuit fault. If the total error If the data window corresponding to the maximum error is located after the reference data window, then the single cell has a short circuit fault; t is a preset threshold.

2. The battery pack stratified fault diagnosis method based on cumulative probability distribution as described in claim 1, characterized in that, The cumulative probability distribution of the individual cell voltage sequence within each data window is calculated as follows: For the voltage sequence of a single cell within each data window, it is divided into a set number of numerical intervals of equal length; the number of voltage data points falling within each numerical interval is calculated in turn, and this number is divided by the total number of data points to calculate the cumulative probability of that numerical interval; the cumulative probabilities of all numerical intervals are summed to form the cumulative probability distribution of the single cell within the data window.

3. The battery pack stratified fault diagnosis method based on cumulative probability distribution as described in claim 1, characterized in that, The total error between the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack within each data window is calculated as follows: The cumulative probability distribution of individual cell voltage and the cumulative probability distribution of average battery pack voltage are each composed of a set number of cumulative probability values. A cumulative probability difference is calculated for each cumulative probability value, and the total error is obtained by adding up all the cumulative probability differences.

4. The battery pack stratified fault diagnosis method based on cumulative probability distribution as described in claim 1, characterized in that, The cumulative probability distribution of the individual cell voltage sequence is calculated as follows: The individual cell voltage sequences are reordered according to a set sequence; The reordered voltage sequence is divided into a set number of equal-length numerical intervals; Calculate the cumulative probability for each numerical interval; The cumulative probabilities of all numerical intervals are summarized to form the cumulative probability distribution of a single battery cell.

5. The battery pack stratified fault diagnosis method based on cumulative probability distribution as described in claim 4, characterized in that, The determination of whether a battery cell has failed is based on the calculated cumulative probability distribution, specifically: Select the middle range of values ​​as the reference range; If the cumulative probability of one or more value intervals in the reference value interval or the value interval before the reference value interval is lower than the set threshold, then the unit has a short circuit fault. If, in the numerical intervals following the reference numerical interval, the cumulative probability of one or more numerical intervals is lower than the set threshold, then the individual unit has an open circuit fault.

6. A battery pack stratified fault diagnosis system based on cumulative probability distribution, used to implement the battery pack stratified fault diagnosis method based on cumulative probability distribution as described in any one of claims 1-5, characterized in that, include: The battery pack fault diagnosis module is used to obtain the maximum and minimum voltage sequences of the battery pack under test. Calculate the cumulative probability distributions of the maximum and minimum voltage sequences respectively; based on the obtained cumulative probability distributions, determine whether the battery pack has failed and the type of failure. The first battery cell fault diagnosis module is used to obtain the voltage sequence of the battery cells in the battery pack for battery packs that have been determined not to have faults. Calculate the cumulative probability distribution of the battery cell voltage sequence, and determine whether the battery cell has failed and the type of failure based on the calculated cumulative probability distribution.

7. The battery pack stratified fault diagnosis system based on cumulative probability distribution as described in claim 6, characterized in that, Also includes: The second battery cell fault diagnosis module is used to obtain the voltage sequence of the battery cell that has been determined to be without fault, calculate the average voltage sequence of the battery pack, and sort the voltage sequence and the average voltage sequence of the battery pack. The sorted voltage sequence and average voltage sequence are divided into multiple data windows using a preset data window; Calculate the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack within each data window; Calculate the total error between the cumulative probability distribution of the individual cell voltage sequence and the cumulative probability distribution of the average voltage sequence of the battery pack within each data window, and determine whether the individual cell has failed and the type of failure based on the total error.

8. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions, characterized in that, The instructions are adapted to be loaded by a processor and executed as described in any one of claims 1-5, which are battery pack stratified fault diagnosis methods based on cumulative probability distribution.

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