A skewness-based series-parallel battery pack fault diagnosis method and system

Abstract

The application belongs to the field of battery fault diagnosis, and provides a series-parallel battery pack fault diagnosis method and system based on skewness. The series-parallel battery pack fault diagnosis method based on skewness comprises the following steps: obtaining voltage sequences of each battery monomer in a series-parallel battery module and branch current sequences; calculating the median values of battery voltages and the median values of branch currents at different time points, and respectively calculating the difference values of each battery voltage and branch current from the corresponding median value, which are respectively denoted as voltage difference value and current difference value; using the skewness calculation formula of the introduced sliding window and square wave signal to obtain the voltage skewness value of each voltage difference value and the current skewness value of the current difference value at different time points; and according to the comparison results of the voltage skewness value and the current skewness value with the corresponding skewness threshold, the fault type and position of the series-parallel battery pack are predicted.

Description

Technical Field

[0001] This invention belongs to the field of battery fault diagnosis, and particularly relates to a fault diagnosis method and system for series and parallel battery packs based on skewness. 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] To improve the safety and reliability of energy storage systems, advanced fault diagnosis algorithms are upgrading battery management systems (BMS) from basic diagnostic tasks such as monitoring overcharge, over-discharge, and overheating to more complex diagnostic tasks. For example, Chinese patent (CN201710417067.9) proposes a multi-fault diagnosis method for batteries, utilizing 2n voltage sensors to achieve online diagnosis of multiple faults in the battery system. Chinese patent (CN202110727244.X) proposes a method for diagnosing early internal short-circuit faults in batteries based on voltage cosine similarity, thereby improving the safety of battery systems. Although battery fault diagnosis technology has been extensively researched, applying the results to practical BMS systems remains challenging, with the following problems:

[0004] (1) Most existing fault diagnosis algorithms are designed for series battery packs. In practical engineering applications, lithium-ion batteries are usually connected in series and parallel in order to meet capacity and voltage requirements. However, there is very little research on fault diagnosis of series and parallel battery packs.

[0005] (2) Currently, many diagnostic algorithms are mostly designed for single-type faults. Since different fault types may exhibit similar external characteristics, such algorithms are prone to misdiagnosis. In addition, although some literature has implemented multi-fault diagnosis for batteries, these algorithms often require the addition of extra sensors.

[0006] (3) There are currently few fault diagnosis algorithms for current sensors. The implementation of BMS function relies on sensor measurement data in the battery system. If the current sensor fails, the BMS will have a deviation in estimating the state of charge (SOC) of the battery, which may lead to overcharging or over-discharging of the battery and pose a risk of thermal runaway. Moreover, the existing diagnostic algorithms are not designed for early fault detection, are highly sensitive to faults, are easily affected by noise, and have poor robustness. Summary of the Invention

[0007] To address the technical problems mentioned above, this invention provides a fault diagnosis method and system for series-parallel battery packs based on skewness. By introducing a square wave signal to reduce the impact of measurement noise on fault diagnosis, it can achieve multi-fault diagnosis of series-parallel battery packs and has good robustness and practical value.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] The first aspect of the present invention provides a fault diagnosis method for series-parallel battery packs based on skewness.

[0010] A fault diagnosis method for series-parallel battery packs based on skewness, comprising:

[0011] Obtain the voltage sequence of each battery cell and the current sequence of each branch in the series-parallel battery module;

[0012] Calculate the median battery voltage and the median branch current at different times, and calculate the difference between each battery voltage and branch current and the corresponding median, which are denoted as voltage difference and current difference, respectively.

[0013] By introducing a sliding window and a square wave signal skewness calculation formula, the voltage skewness value of each voltage difference and the current skewness value of each current difference at different times are obtained.

[0014] Based on the comparison results of voltage and current deviation values ​​with their corresponding deviation thresholds, the fault type and location of the series-parallel battery pack are obtained.

[0015] As one implementation method, the formula for calculating the skewness of a sliding window and a square wave signal is introduced as follows:

[0016]

[0017]

[0018]

[0019] In the formula x i S(x) is the difference between sensor x at time i and the median; i ) is x i The skewness value, where a is the length of the sliding window; c is the mean of the difference sequence within the sliding window; i Let c be the square wave signal function, where c is the amplitude, which is greater than the measured noise amplitude and less than the fault signal amplitude; T0 is the period, and a is an integer multiple of T0.

[0020] As one implementation method, if all current deviation values ​​are less than the first deviation threshold and all voltage deviation values ​​are less than the second deviation threshold, then it is determined that there is no fault in the series battery pack.

[0021] In one implementation, if all current deviation values ​​are less than the first deviation threshold, and the voltage deviation value of battery j in branch k is greater than or equal to the second deviation threshold, then the voltage sensor corresponding to battery j in branch k is faulty.

[0022] In one implementation, if the current deviation value of a certain branch is greater than or equal to the first deviation threshold, and the voltage deviation values ​​of all batteries in that branch are less than the second deviation threshold, then the current sensor corresponding to that branch is faulty.

[0023] In one implementation, if the current skewness value of branch k is greater than or equal to the first skewness threshold, and the voltage skewness value of battery j in branch k is greater than or equal to the second skewness threshold, and the branch current of branch k is greater than the average of all branch currents, then battery j in branch k has an internal short circuit fault.

[0024] As one implementation, if the current skewness value of branch k is greater than or equal to the first skewness threshold, and the voltage skewness value of battery j in branch k is greater than or equal to the second skewness threshold, and the branch current of branch k is less than the average of all branch currents, then there is a connection fault between battery j and battery j+1 in branch k.

[0025] A second aspect of the present invention provides a fault diagnosis system for series-parallel battery packs based on skewness.

[0026] A skewness-based fault diagnosis system for series-parallel battery packs, comprising:

[0027] Voltage and current sequence acquisition module, which is used to acquire the voltage sequence of each battery cell and the current sequence of each branch in the series-parallel battery module;

[0028] The voltage and current difference calculation module is used to calculate the median value of the battery voltage and the median value of the branch current at different times. It calculates the difference between each battery voltage and branch current and the corresponding median value, and records them as voltage difference and current difference, respectively.

[0029] The voltage and current deviation calculation module is used to obtain the voltage deviation value of each voltage difference and the current deviation value of the current difference at different times by using the deviation calculation formula that introduces a sliding window and a square wave signal.

[0030] The battery pack fault diagnosis module is used to determine the fault type and location of the series and parallel battery packs based on the comparison results of the voltage deviation value and the current deviation value with the corresponding deviation threshold.

[0031] A third aspect of the present invention provides a computer-readable storage medium.

[0032] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the skewness-based series-parallel battery pack fault diagnosis method described above.

[0033] A fourth aspect of the present invention provides a computer device.

[0034] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the skewness-based series-parallel battery pack fault diagnosis method described above.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] (1) Based on the comparison results of voltage deviation value and current deviation value with the corresponding deviation threshold, the present invention obtains the fault type and location of the series-parallel battery pack. Without adding additional sensors, the invention achieves early micro-fault location and multi-fault type identification of the series-parallel battery pack, including current / voltage sensor fault, internal short circuit fault, and connection fault.

[0037] (2) This invention utilizes the skewness calculation formula of a sliding window and a square wave to obtain the voltage skewness value of each voltage difference and the current skewness value of the current difference at different times, which has the advantages of small computational load and low complexity. By introducing a square wave signal, the influence of measurement noise is reduced, the robustness of the algorithm is improved, and it is suitable for real-time online fault detection.

[0038] (3) The present invention makes full use of the information of current and voltage of each branch in the series-parallel connected battery pack. Compared with only using voltage information, the present invention can achieve more fault type detection and higher fault diagnosis accuracy.

[0039] Advantages of additional aspects 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

[0040] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0041] Figure 1 This is a flowchart of a series-parallel battery pack fault diagnosis method based on skewness according to an embodiment of the present invention.

[0042] Figure 2 This is a measurement circuit diagram of a series-parallel battery pack according to an embodiment of the present invention.

[0043] Figure 3 This is the voltage-time curve of a battery cell when branch 1 of this embodiment of the invention has multiple faults.

[0044] Figure 4 These are the skewness curves of the current in each branch of this invention.

[0045] Figure 5This is the skewness curve of the battery cell voltage in branch 1 of this embodiment of the invention. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0047] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] 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 scope of exemplary embodiments according to the invention. 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.

[0049] Example 1

[0050] This embodiment provides a fault diagnosis method for series-parallel battery packs based on skewness, which specifically includes the following steps:

[0051] Step 1: Obtain the voltage sequence of each battery cell and the current sequence of each branch in the series-parallel battery module.

[0052] use Figure 2 The circuit diagram shown is for measuring the series and parallel battery packs. Record the voltage sequence of each battery cell in the module and the current sequence of each branch.

[0053] Assume that the series-parallel battery module has m branches connected in parallel, and each branch consists of n batteries connected in series, where m and n are positive integers not less than 3. The current of each branch is recorded as I1, I2…I… m .

[0054] To detect connection faults between batteries, the voltage sensor measures the voltage between the negative terminals of the previous and next batteries in the same branch. The measured voltages are recorded sequentially as V. 1-1 V 1-2 …V m-n .

[0055] Step 2: Calculate the median battery voltage and the median branch current at different times. Calculate the difference between each battery voltage and branch current and the corresponding median, and record them as the voltage difference and current difference, respectively.

[0056] Based on the battery voltage and branch current recorded in step 1, calculate the median V of the battery voltage at different times.med And the median value of the branch current I med The differences between the voltage of each battery and the current of each branch and their corresponding median values ​​are calculated and recorded as dV. 1-1 dV 1-2 …dV m-n and dI1, dI2...dI m .

[0057] The median value is chosen because there are many individual battery cells in a battery module, while faulty batteries are always in the minority. Therefore, the median value is considered a normal battery. The difference is used for subsequent skewness calculation because the difference between normal batteries and the median value is small, while the difference between faulty batteries is large. This makes the skewness of the difference for faulty batteries higher than that for normal batteries, thus achieving the purpose of fault diagnosis.

[0058] Step 3: Using the skewness calculation formula of the sliding window and square wave signal, obtain the voltage skewness value of each voltage difference and the current skewness value of the current difference at different times.

[0059] Specifically, the formula for calculating the skewness of the sliding window and square wave signal is as follows:

[0060]

[0061]

[0062]

[0063] In the formula x i S(x) is the difference between sensor x at time i and the median; i ) is x i The skewness value, where a is the length of the sliding window; c is the mean of the difference sequence within the sliding window; i Let c be the square wave signal function, where c is the amplitude, which is greater than the amplitude of the measured noise and less than the amplitude of the fault signal; T0 is the period, and a is an integer multiple of T0, thus ensuring that the mean of the difference sequence remains unchanged.

[0064] This embodiment improves traditional skewness by introducing a sliding window and a square wave signal. Generally, excessively long sample data may mask fault information, while excessively short data makes the algorithm overly sensitive to noise, leading to misdiagnosis. This embodiment uses a sliding window to control the length of the battery data, maintaining the skewness's sensitivity to fault information. The square wave signal reduces the impact of noise on skewness. By adding a square wave signal to the original signal that is much higher than the measurement error amplitude but much lower than the fault signal amplitude, the corrected signal exhibits a certain trend, thereby reducing the impact of noise on skewness calculation.

[0065] In this embodiment, c is 0.05, T0 is 2, and a is 10.

[0066] It should be noted here that the size of c, T0, and a can be specifically set by those skilled in the art according to the actual situation.

[0067] Step 4: Based on the comparison results of the voltage deviation value and the current deviation value with the corresponding deviation threshold, the fault type and location of the series and parallel battery pack are obtained.

[0068] In this embodiment, both the current bias threshold S1 and the voltage bias threshold S2 are set to 0.01. This value depends on the measurement accuracy and the noise level. Those skilled in the art can set it according to the actual situation, which will not be described in detail here.

[0069] Based on the difference dV obtained in step 2 1-1 dV 1-2 …dV m-n and dI1, dI2...dI m Calculate the skewness value S(dV) of the difference at different times. 1-1 ), S(dV 1-2 )…S(dV m-n ) and S(dI1), S(dI2)...S(dI m And perform fault diagnosis according to the following rules:

[0070] (1) If all current deviation values ​​are less than the first deviation threshold and all voltage deviation values ​​are less than the second deviation threshold, then it is determined that there is no fault in the series battery pack.

[0071] For example, if all current deviation values ​​are less than threshold S1 and all voltage deviation values ​​are less than threshold S2, it indicates that there is no fault in the series battery pack.

[0072] (2) If all current deviation values ​​are less than the first deviation threshold, and the voltage deviation value of battery j in branch k is greater than or equal to the second deviation threshold, then the voltage sensor corresponding to battery j in branch k is faulty.

[0073] For example, if all current skewness values ​​are less than S1, and the voltage skewness value S(dV) of battery j in branch k is less than S1, then... k-j If S ≥ S2, it indicates that voltage sensor j in branch k is faulty.

[0074] (3) If the current deviation value of a certain branch is greater than or equal to the first deviation threshold, and the voltage deviation values ​​of all batteries in the branch are less than the second deviation threshold, then the current sensor corresponding to the branch is faulty.

[0075] For example, if the current deflection value of branch k is S(dI) kIf S1 ≥ S2, and all battery voltage deviation values ​​in this branch are less than S2, then the current sensor k is faulty.

[0076] (4) If the current skewness value of branch k is greater than or equal to the first skewness threshold, and the voltage skewness value of battery j in branch k is greater than or equal to the second skewness threshold, and the branch current of branch k is greater than the average of all branch currents, then battery j in branch k has an internal short circuit fault.

[0077] For example, if the current deflection value of branch k is S(dI) k )≥S1, and the voltage deviation value S(dV) of battery j in this branch is greater than or equal to S1. k-j )≥S2, and at this moment the branch current I k Greater than the average current of all branches I avg If so, then battery j in branch k has an internal short circuit fault.

[0078] (5) If the current skewness value of branch k is greater than or equal to the first skewness threshold, and the voltage skewness value of battery j in branch k is greater than or equal to the second skewness threshold, and the branch current of branch k is less than the average of all branch currents, then there is a connection fault between battery j and battery j+1 in branch k.

[0079] For example, if the current deflection value of branch k is S(dI) k )≥S1, and the voltage deviation value S(dV) of battery j in this branch is greater than or equal to S1. k-j )≥S2, and at this moment the branch current I k Less than I avg If there is a connection fault between battery j and battery j+1 in branch k, then there is a connection fault between them.

[0080] To make the analysis more intuitive, the fault diagnosis strategies are summarized in Table 1:

[0081] Table 1 Fault Diagnosis Strategy

[0082]

[0083] The following specific examples illustrate the methods and effects of the embodiments:

[0084] The experiment was conducted according to Figure 2The current and voltage sensors are installed at the locations shown in the measurement circuit diagram. A total of 15 batteries are used, forming a series-parallel battery module with 5 series and 3 parallel connections. All batteries have an initial SOC of 90%. Discharge is performed under Standardized Urban Cyclic Discharge (UDDS) conditions, simulating different faults during the discharge process. Specifically, from 500-600s, the reading of voltage sensor #1 in branch 1 stops changing, simulating a voltage sensor jamming fault. From 1000-1100s, noise with an amplitude of 0.01 is injected into the current sensor in branch 1 to simulate a current sensor accuracy degradation fault. From 1500-1600s, a 10Ω resistor is connected in parallel with battery #2 in branch 1 to simulate an early internal short circuit fault. From 2000-2100s, a 0.03Ω resistor is connected in series between batteries #3 and #4 in branch 1 to simulate a connection fault. The measured currents for each branch are recorded as I1, I2, and I3, and the measured voltages are recorded as V. 1-1 V 1-2 …V 3-5 .

[0085] Figure 3 For the voltage-time curves of each battery cell in branch 1, from... Figure 3 As can be seen, when no fault occurs, all batteries exhibit similar trends, while during fault periods (except for current sensor failure), the voltage curves show significant changes. Subsequently, the median battery voltage V at different times was calculated. med and the median current I of the branch med The differences between the voltage of each battery and the current of each branch and their corresponding median values ​​are calculated and recorded as dV. 1-1 dV 1-2 …dV 3-5 And dI1, dI2, dI3. Calculate the skewness values ​​of the current difference sequence of each branch and the voltage difference sequence of each battery according to formulas (1) to (3), such as... Figure 4 and Figure 5 As shown.

[0086] Combination Figure 4 and Figure 5 As can be seen, at 500s, S(dV) 1-1 The value of S(dI1) ≥ 0.01, while S(dI1) < 0.01, indicates that voltage sensor No. 1 in branch 1 is faulty. Furthermore, from... Figure 5 As can be seen, although the voltage sensor fault disappeared at 600s, the voltage deflection value returned to normal at 610s, a delay of about 10s. This is due to the hysteresis caused by the sliding window. At 1000s, Figure 4The display shows S(dI1)≥0.01, and the voltage deviation values ​​of all battery cells in branch 1 are below 0.01, indicating a fault in the current sensor in branch 1. At 1500s, both graphs show S(dI1)≥0.01, S(dV) 1-2 If ≥0.01, calculate the current I1 in branch 1 and the average current I in all branches at that moment. avg I1>I avg By referring to Table 1, it can be determined that battery #2 in branch 1 has an internal short circuit fault at 1500s. At 2000s, S(dI1)≥0.01, S(dV) 1-3 )≥0.01, and at this time I1 avg This indicates a connection fault between batteries 3 and 4 in branch 1. The above experimental data confirms the effectiveness of the method proposed in this embodiment.

[0087] Example 2

[0088] This embodiment provides a series-parallel battery pack fault diagnosis system based on skewness, which specifically includes the following modules:

[0089] Voltage and current sequence acquisition module, which is used to acquire the voltage sequence of each battery cell and the current sequence of each branch in the series-parallel battery module;

[0090] The voltage and current difference calculation module is used to calculate the median value of the battery voltage and the median value of the branch current at different times. It calculates the difference between each battery voltage and branch current and the corresponding median value, and records them as voltage difference and current difference, respectively.

[0091] The voltage and current deviation calculation module is used to obtain the voltage deviation value of each voltage difference and the current deviation value of the current difference at different times by using the deviation calculation formula that introduces a sliding window and a square wave signal.

[0092] The battery pack fault diagnosis module is used to determine the fault type and location of the series and parallel battery packs based on the comparison results of the voltage deviation value and the current deviation value with the corresponding deviation threshold.

[0093] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0094] Example 3

[0095] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the skewness-based series-parallel battery pack fault diagnosis method described above.

[0096] Example 4 ​

[0097] This embodiment provides a computer device, including 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 steps in the skewness-based series-parallel battery pack fault diagnosis method described above.

[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fault diagnosis method for series-parallel battery packs based on skewness, characterized in that, include: Obtain the voltage sequence of each battery cell and the current sequence of each branch in the series-parallel battery module; Calculate the median battery voltage and the median branch current at different times, and calculate the difference between each battery voltage and branch current and the corresponding median, which are denoted as voltage difference and current difference, respectively. By introducing a sliding window and a square wave signal skewness calculation formula, the voltage skewness value of each voltage difference and the current skewness value of each current difference at different times are obtained. Based on the comparison results of voltage skewness and current skewness values ​​with the corresponding skewness thresholds, the fault type and location of the series-parallel battery pack are predicted. The formula for calculating the skewness of a sliding window and a square wave signal is as follows: In the formula x i S(x) is the difference between sensor x at time i and the median; i ) is x i The skewness value, where a is the length of the sliding window; c is the mean of the difference sequence within the sliding window; i Let c be the square wave signal function, where c is the amplitude, which is greater than the measured noise amplitude and less than the fault signal amplitude; T0 is the period, and a is an integer multiple of T0.

2. The fault diagnosis method for series-parallel battery packs based on skewness as described in claim 1, characterized in that, If all current skew values ​​are less than the first skew threshold and all voltage skew values ​​are less than the second skew threshold, then it is determined that there is no fault in the series battery pack.

3. The fault diagnosis method for series-parallel battery packs based on skewness as described in claim 1, characterized in that, If all current deviation values ​​are less than the first deviation threshold, and the voltage deviation value of battery j in branch k is greater than or equal to the second deviation threshold, then the voltage sensor corresponding to battery j in branch k is faulty.

4. The fault diagnosis method for series-parallel battery packs based on skewness as described in claim 1, characterized in that, If the current skewness value of a certain branch is greater than or equal to the first skewness threshold, and the voltage skewness value of all batteries in that branch is less than the second skewness threshold, then the current sensor corresponding to that branch is faulty.

5. The fault diagnosis method for series-parallel battery packs based on skewness as described in claim 1, characterized in that, If the current skewness value of branch k is greater than or equal to the first skewness threshold, and the voltage skewness value of battery j in branch k is greater than or equal to the second skewness threshold, and the branch current of branch k is greater than the average of all branch currents, then battery j in branch k has an internal short circuit fault.

6. The fault diagnosis method for series-parallel battery packs based on skewness as described in claim 1, characterized in that, If the current skewness value of branch k is greater than or equal to the first skewness threshold, and the voltage skewness value of battery j in branch k is greater than or equal to the second skewness threshold, and the branch current of branch k is less than the average of all branch currents, then there is a connection fault between battery j and battery j+1 in branch k.

7. A fault diagnosis system for series-parallel battery packs based on skewness, characterized in that, The fault diagnosis method for series-parallel battery packs based on skewness, as described in any one of claims 1-6, is implemented by: Voltage and current sequence acquisition module, which is used to acquire the voltage sequence of each battery cell and the current sequence of each branch in the series-parallel battery module; The voltage and current difference calculation module is used to calculate the median value of the battery voltage and the median value of the branch current at different times. It calculates the difference between each battery voltage and branch current and the corresponding median value, and records them as voltage difference and current difference, respectively. The voltage and current deviation calculation module is used to obtain the voltage deviation value of each voltage difference and the current deviation value of the current difference at different times by using the deviation calculation formula that introduces a sliding window and a square wave signal. The battery pack fault prediction module is used to predict the fault type and location of the series and parallel battery packs based on the comparison results of the voltage deviation value and the current deviation value with the corresponding deviation threshold.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the skewness-based series-parallel battery pack fault diagnosis method as described in any one of claims 1-6.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps in the skewness-based series-parallel battery pack fault diagnosis method as described in any one of claims 1-6.

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