Battery cell fault diagnosis method and device

Abstract

The application discloses a battery cell fault diagnosis method and device, and relates to the technical field of batteries. According to the charging voltage of a battery cell, the sum of the absolute values of MN of a to-be-tested cell and the Euclidean distance are calculated, the sample entropy-scale factor curve slope of the to-be-tested cell is calculated according to the sampling time of the charging voltage, the first Hamming closeness of the sum of the absolute values of MN, the Euclidean distance and the sample entropy-scale factor curve slope of the to-be-tested cell to the normal cell clustering center is calculated, the second Hamming closeness of the sum of the absolute values of MN, the Euclidean distance and the sample entropy-scale factor curve slope of the to-be-tested cell to the fault cell clustering center is calculated, and if the second Hamming closeness is greater than the first Hamming closeness, the to-be-tested cell is judged to be faulty. The battery cell fault is comprehensively diagnosed from three dimensions.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a method and apparatus for diagnosing battery cell faults. Background Technology

[0002] A battery typically consists of multiple cells. A cell malfunction can lead to abnormal charging and discharging, or even fire. To ensure battery safety, it is necessary to diagnose the battery cells. Summary of the Invention

[0003] This invention solves the technical problem of how to diagnose battery cell faults by providing a method and apparatus for diagnosing battery cell faults.

[0004] On the one hand, the present invention provides the following technical solution:

[0005] A method for diagnosing battery cell faults, comprising:

[0006] Acquire the charging voltage of multiple battery cells at multiple sampling times;

[0007] The sum of the absolute values ​​of MN of the battery cell under test and the Euclidean distance are calculated based on the multiple charging voltages mentioned above.

[0008] The slope of the sample entropy-scale factor curve of the battery cell under test is calculated based on multiple sampling times.

[0009] Obtain the preset cluster centers for normal and faulty battery cells;

[0010] The first Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cell under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the normal cell.

[0011] The second Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the faulty cells.

[0012] If the second Hamming proximity is greater than the first Hamming proximity, then the cell under test is determined to be faulty.

[0013] Optionally, calculating the sum of the absolute values ​​of MN based on the plurality of said charging voltages includes:

[0014] Determine the maximum value among the multiple charging voltages collected at the sampling time;

[0015] Calculate the average value of the multiple charging voltages collected at this sampling time;

[0016] The MN value corresponding to the sampling time is calculated based on the charging voltage, the maximum value, and the average value of the battery cell under test collected at the sampling time.

[0017] The sum of the absolute values ​​of the multiple MN values ​​corresponding to the multiple sampling times is calculated to obtain the sum of the absolute values ​​of MN.

[0018] Optionally, calculating the Euclidean distance based on a plurality of said charging voltages includes:

[0019] Calculate the average value of the multiple charging voltages collected at the sampling time;

[0020] Calculate the difference between the charging voltage of the battery cell under test collected at the sampling time and the average value;

[0021] The Euclidean distance is calculated based on the multiple differences corresponding to the multiple sampling times.

[0022] Optionally, the slope of the sample entropy-scale factor curve of the cell under test is calculated based on multiple sampling times, including:

[0023] The sampling times are downsampled according to multiple different scale factors to obtain multiple sampling times under each scale factor;

[0024] Calculate the sample entropy corresponding to the scale factor based on the multiple sampling times under the scale factor;

[0025] The sample entropy-scale factor curve is obtained by fitting multiple scale factors and corresponding sample entropies.

[0026] Determine the slope of the sample entropy-scale factor curve.

[0027] Optionally, the normal cell cluster centers include normal cell MN cluster centers, normal cell Euclidean distance cluster centers, and normal cell slope cluster centers; the faulty cell cluster centers include faulty cell MN cluster centers, faulty cell Euclidean distance cluster centers, and faulty cell slope cluster centers.

[0028] Obtain the preset cluster centers for normal and faulty battery cells, including:

[0029] Obtain the sum of the absolute values ​​of MN, the Euclidean distance, and the slope of the sample entropy-scale factor curve for each of the multiple training sample cells;

[0030] The sum of the absolute values ​​of the multiple MNs is normalized, the Euclidean distances are normalized, and the slopes of the multiple sample entropy-scale factor curves are normalized.

[0031] Take the absolute value of the slope of the normalized sample entropy-scale factor curve, and assign weights to the sum of the absolute values ​​of the normalized MN, the Euclidean distance, and the absolute value of the slope of the sample entropy-scale factor curve.

[0032] K-means clustering is performed on the sum of the absolute values ​​of the multiple weighted MN values ​​to obtain the MN cluster centers of the normal battery cells and the MN cluster centers of the faulty battery cells;

[0033] K-means clustering is performed on the weighted Euclidean distances to obtain the Euclidean distance cluster centers of the normal battery cells and the Euclidean distance cluster centers of the faulty battery cells;

[0034] K-means clustering is performed on the absolute values ​​of the slopes of the multiple weighted sample entropy-scale factor curves to obtain the cluster centers of the normal cell slope and the faulty cell slope.

[0035] Optionally, the first Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster centers of the normal cells, including:

[0036]

[0037] N1 is the first Hamming proximity score, n1, n2, and n3 are the sum of the absolute values ​​of MN of the cell under test, the Euclidean distance, and the slope of the sample entropy-scale factor curve, respectively, and n4, n5, and n6 are the MN cluster center of the normal cell, the Euclidean distance cluster center of the normal cell, and the slope cluster center of the normal cell, respectively.

[0038] Optionally, the second Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster centers of the faulty cells, including:

[0039]

[0040] N2 is the second Hamming proximity, n1, n2, and n3 are the sum of the absolute values ​​of MN of the cell under test, the Euclidean distance, and the slope of the sample entropy-scale factor curve, respectively, and n7, n8, and n9 are the MN cluster center of the faulty cell, the Euclidean distance cluster center of the faulty cell, and the slope cluster center of the faulty cell, respectively.

[0041] On the other hand, the present invention also provides the following technical solution:

[0042] A battery cell fault diagnosis device, comprising:

[0043] The acquisition module is used to acquire the charging voltage of multiple battery cells collected at multiple sampling times;

[0044] The calculation module is used to calculate the sum of the absolute values ​​of MN of the battery cell under test and the Euclidean distance based on the multiple charging voltages.

[0045] The slope of the sample entropy-scale factor curve of the battery cell under test is calculated based on multiple sampling times.

[0046] The acquisition module is also used to acquire preset normal cell cluster centers and faulty cell cluster centers;

[0047] The calculation module is also used to calculate the first Hamming proximity based on the sum of the absolute values ​​of MN of the cell under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the normal cell.

[0048] The second Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the faulty cells.

[0049] The judgment module is used to determine that the battery cell under test is faulty if the second Hamming proximity is greater than the first Hamming proximity.

[0050] On the other hand, the present invention also provides the following technical solution:

[0051] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a method for diagnosing faults in any battery cell.

[0052] On the other hand, the present invention also provides the following technical solution:

[0053] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for diagnosing faults in any battery cell.

[0054] One or more technical solutions provided by this invention have at least the following technical effects or advantages:

[0055] This invention calculates the sum of absolute values ​​of MN and Euclidean distance of the tested cell based on the charging voltage of the battery cell. It also calculates the slope of the sample entropy-scale factor curve of the tested cell based on the sampling time of the charging voltage. Furthermore, it calculates the first Hamming proximity of the sum of absolute values ​​of MN, Euclidean distance, and slope of the sample entropy-scale factor curve of the tested cell to the cluster centers of normal cells. Finally, it calculates the second Hamming proximity of the sum of absolute values ​​of MN, Euclidean distance, and slope of the sample entropy-scale factor curve of the tested cell to the cluster centers of faulty cells. If the second Hamming proximity is greater than the first Hamming proximity, the tested cell is judged to be faulty. This invention provides a comprehensive diagnosis of battery cell faults from three dimensions. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a graph showing the change of cell charging voltage over time in an embodiment of the present invention.

[0058] Figure 2 This is a graph showing the change of the MN value of the battery cell with the sampling time in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the sum of the absolute values ​​of MN of the battery cells in an embodiment of the present invention;

[0060] Figure 4 This is a sample entropy-scale factor curve of the battery cell in an embodiment of the present invention;

[0061] Figure 5 This is a schematic diagram of the slope of the sample entropy-scale factor curve in an embodiment of the present invention;

[0062] Figure 6 This is a schematic diagram of the Euclidean distance of the battery cells in an embodiment of the present invention;

[0063] Figure 7 This is a flowchart of the battery cell fault diagnosis method in an embodiment of the present invention;

[0064] Figure 8 This is a schematic diagram of a battery cell fault diagnosis device in an embodiment of the present invention. Detailed Implementation

[0065] The embodiments of the present invention provide a method and apparatus for diagnosing battery cell faults, thereby solving the technical problem of how to diagnose battery cell faults.

[0066] To better understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] The embodiments of the present invention have obtained the following results through experiments: Figure 1 The curves showing the charging voltage of the six battery cells over time are shown. Figure 1 The bottom curve represents cell #5. It can be seen that the charging voltage of cell #5 is too low, indicating that it is a faulty cell.

[0068] The experiment yielded the following results: Figure 2 The curves showing the MN value of the six battery cells changing over time are shown. Figure 2The top curve is the MN value curve for the faulty cell #5. According to... Figure 2 The calculation yielded the following results: Figure 3 The sum of the absolute values ​​of MN for the six cells shown. From Figure 3 It can be seen that the sum of the absolute values ​​of MN of the faulty cell #5 is significantly greater than that of the other cells, indicating that the sum of the absolute values ​​of MN can reflect the difference between the faulty cell and the normal cell, that is, the sum of the absolute values ​​of MN can reflect the cell fault.

[0069] The experiment yielded the following results: Figure 4 The sample entropy-scaling factor curves of the six battery cells shown are as follows: Figure 4 The bottom curve is the sample entropy-scale factor curve of the faulty cell #5. According to... Figure 4 get Figure 5 The slope of each sample entropy-scale factor curve is shown. It can be seen that the slope of the sample entropy-scale factor curve for the faulty cell #5 is significantly lower than that of the other cells, indicating that the slope of the sample entropy-scale factor curve can reflect the cell fault.

[0070] The experiment yielded the following results: Figure 6 The Euclidean distances of the six cells shown indicate that the Euclidean distance of the faulty cell #5 is significantly greater than that of the other cells. The Euclidean distance between the surface cell charging voltage curve and the average voltage curve can also reflect the cell fault.

[0071] Based on the theory above, this invention proposes a battery cell fault diagnosis method, such as... Figure 7 As shown, it includes:

[0072] Step S1: Obtain the charging voltage of multiple battery cells collected at multiple sampling times;

[0073] Step S2: Calculate the sum of the absolute values ​​of MN of the cell under test and the Euclidean distance based on multiple charging voltages;

[0074] Step S3: Calculate the slope of the sample entropy-scale factor curve of the cell under test based on multiple sampling times;

[0075] Step S4: Obtain the preset cluster centers for normal battery cells and faulty battery cells;

[0076] Step S5: Calculate the first Hamming proximity based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster centers of normal cells.

[0077] Step S6: Calculate the second Hamming proximity based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the faulty cells.

[0078] Step S7: If the second Hamming proximity is greater than the first Hamming proximity, then the cell under test is determined to be faulty.

[0079] In step S1, assuming that the charging voltage of multiple battery cells is collected every 1 second for a total of 20 seconds, the multiple sampling times are 1, 2, 3, ..., 19, 20.

[0080] In step S2, calculating the sum of the absolute values ​​of MN based on multiple charging voltages includes: determining the maximum value among the multiple charging voltages collected at the sampling time; calculating the average value of the multiple charging voltages collected at that sampling time; calculating the MN value corresponding to that sampling time based on the charging voltage, maximum value, and average value of the cell under test collected at that sampling time; and calculating the sum of the absolute values ​​of the multiple MN values ​​corresponding to multiple sampling times to obtain the sum of the absolute values ​​of MN. Specifically, calculating the MN value corresponding to that sampling time based on the charging voltage, maximum value, and average value of the cell under test collected at that sampling time includes: MN is the MN value corresponding to a certain sampling time, and U is the charging voltage of the battery cell under test collected at that sampling time. U is the average value of the charging voltage of all cells collected at this sampling time. max This is the maximum value among all cell charging voltages collected at that sampling time. This allows us to obtain the sum of the absolute values ​​of MN for each cell under test at a single sampling time. Each sampling time corresponds to a sum of absolute values ​​of MN, and we can obtain the sum of all absolute values ​​of MN for all sampling times. Taking the absolute value of MN prevents the cell's MN value from fluctuating between positive and negative values ​​in certain special cases, which could result in a very small sum that fails to highlight the fault.

[0081] In step S2, calculating the Euclidean distance based on multiple charging voltages includes: calculating the average value of multiple charging voltages collected at the sampling time; calculating the difference between the charging voltage of the cell under test collected at that sampling time and the average value; and calculating the Euclidean distance based on the multiple differences corresponding to multiple sampling times. Specifically, calculating the Euclidean distance based on the multiple differences corresponding to multiple sampling times includes: d1 is the Euclidean distance, a is the sampling time number, n is the number of sampling times (e.g., 20), U a Let be the charging voltage of the battery cell under test collected at the a-th sampling time. This represents the average charging voltage of all battery cells collected at the a-th sampling time.

[0082] Step S3 includes: downsampling multiple sampling times according to multiple different scale factors to obtain multiple sampling times under each scale factor; calculating the sample entropy corresponding to the scale factor based on the multiple sampling times under the scale factor; fitting multiple scale factors and the corresponding multiple sample entropies to obtain the sample entropy-scale factor curve; and determining the slope of the sample entropy-scale factor curve.

[0083] This involves downsampling multiple sampling times based on multiple different scale factors to obtain multiple sampling times for each scale factor, including: b is the sampling time number, y b Let t be the b-th sampling time, τ be the scale factor, a be the sampling time number, and t be the sampling time index. a Let t be the a-th sampling time, and n be the number of sampling times. For example, when n = 20 and τ = 1, 1 ≤ b ≤ 20, y1 = t1, y2 = t2, ..., y 20 =t 20 Multiple sampling times are the original sampling times; when τ = 2, 1 ≤ b ≤ 10, y1 = (t1 + t2) / 2, y2 = (t3 + t4) / 2, ..., y 10 =(t 19 +t 20 When τ = 3, 1 ≤ b ≤ 20 / 3, y1 = (t1 + t2 + t3) / 3, y2 = (t4 + t5 + t6) / 3, ..., y6 = (t 16 +t 17 +t 18 ) / 3.

[0084] The process of calculating the sample entropy corresponding to the scaling factor is as follows:

[0085] Let the number of sampling times be e, 1≤b≤e, and let the e sampling times be y. b Reconstruct into e-m+1 m-dimensional vectors X(1), X(2), ..., X(e-m+1), where X(b) = {y b y b:1 , ..., y b:m；1 For example, if m = 2, then the e sampling times y b Reconstructing into e-1 two-dimensional vectors X(1), X(2), ..., X(e-1), where e = 10 when τ = 2, then X(1) = {y1, y2}, X(2) = {y2, y3}, ..., X(9) = {y9, y1}. 10};

[0086] Calculate d2 = max k<0,1,...,m；1[|X(i+k)-X(j+k)|]; i,j=1,2,...e-m+1,i≠j; d2 is the maximum absolute value of the difference between corresponding elements of the two vectors;

[0087] calculate r is a given threshold, for example, r = 0.2std, where std is the standard deviation of the sampling time;

[0088] calculate

[0089] Let f = m + 1, calculate

[0090] Sample Entropy When e takes a finite value

[0091] Calculate the sample entropy S once for each scale factor τ, thus obtaining multiple sample entropies S corresponding to multiple scale factors τ.

[0092] In step S4, the normal cell cluster centers include the normal cell MN cluster center, the normal cell Euclidean distance cluster center, and the normal cell slope cluster center; the faulty cell cluster centers include the faulty cell MN cluster center, the faulty cell Euclidean distance cluster center, and the faulty cell slope cluster center. Step S4 specifically includes: obtaining the sum of the absolute values ​​of MN, Euclidean distance, and slope of the sample entropy-scale factor curve for each training sample cell in multiple training sample cells; normalizing the sum of the absolute values ​​of MN, the Euclidean distance, and the slope of the sample entropy-scale factor curve; taking the absolute value of the normalized slope of the sample entropy-scale factor curve, and assigning weights to the absolute values ​​of the sum of the absolute values ​​of MN, the Euclidean distance, and the slope of the sample entropy-scale factor curve; performing k-means clustering on the weighted sum of the absolute values ​​of MN to obtain the MN cluster centers of normal cells and the MN cluster centers of faulty cells; performing k-means clustering on the weighted Euclidean distances to obtain the Euclidean distance cluster centers of normal cells and the Euclidean distance cluster centers of faulty cells; and performing k-means clustering on the absolute values ​​of the weighted slopes of the sample entropy-scale factor curves to obtain the slope cluster centers of normal cells and the slope cluster centers of faulty cells.

[0093] In this process, steps S1-S3 are performed by replacing the test cell with each training sample cell to obtain the sum of the absolute values ​​of MN, the Euclidean distance, and the slope of the sample entropy-scale factor curve for each training sample cell. Normalization can be performed using the Z-score normalization method. Taking the absolute value of the slope of the normalized sample entropy-scale factor curve is for the convenience of horizontal comparison. The slope of the sample entropy-scale factor curve is of high importance, and weights of the sum of the absolute values ​​of MN, the Euclidean distance, and the absolute value of the slope of the sample entropy-scale factor curve can be assigned in a weight ratio of 1:1:2.

[0094] Taking the sum of absolute values ​​of MN as an example, the k-means clustering process is as follows: First, randomly select two sums of absolute values ​​of MN as initial centroids; second, assign each sum of absolute values ​​of MN to the nearest initial centroid, forming two clusters; third, calculate the average of the sums of absolute values ​​of MN in the cluster containing the first initial centroid and use it as the new centroid of that cluster, and calculate the average of the sums of absolute values ​​of MN in the cluster containing the second initial centroid and use it as the new centroid of that cluster; fourth, repeat steps two and three until the centroids of the two clusters no longer change. The centroids of the two clusters are the MN cluster centers of normal cells and the MN cluster centers of faulty cells. The k-means clustering process using Euclidean distance and the slope of the sample entropy-scale factor curve is the same as that for the sum of absolute values ​​of MN. Here, the objective function of k-means clustering is SSE.

[0095] Step S5 includes: N1 is the first Hamming proximity score, n1, n2, and n3 are the sum of the absolute values ​​of MN of the cell under test, the Euclidean distance, and the slope of the sample entropy-scale factor curve, respectively, and n4, n5, and n6 are the MN cluster center, the Euclidean distance cluster center, and the slope cluster center of the normal cell, respectively.

[0096] Step S6 includes: N2 is the second Hamming proximity, n1, n2, and n3 are the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, and the slope of the sample entropy-scale factor curve, respectively, and n7, n8, and n9 are the cluster centers of faulty cells (MN, Euclidean distance, and slope), respectively.

[0097] In this embodiment of the invention, if the second Hamming proximity of the battery cell under test is not greater than the first Hamming proximity, then the battery cell under test is determined to be normal.

[0098] As described above, the battery cell fault diagnosis method of this invention calculates the sum of absolute values ​​of MN and Euclidean distance of the cell under test based on the charging voltage of the battery cell, calculates the slope of the sample entropy-scale factor curve of the cell under test based on the sampling time of the charging voltage, calculates the first Hamming proximity of the sum of absolute values ​​of MN, Euclidean distance, and slope of the sample entropy-scale factor curve of the cell under test with the cluster center of normal cells, and calculates the second Hamming proximity of the sum of absolute values ​​of MN, Euclidean distance, and slope of the sample entropy-scale factor curve of the cell under test with the cluster center of faulty cells. If the second Hamming proximity is greater than the first Hamming proximity, the cell under test is judged to be faulty. The battery cell fault is diagnosed comprehensively from three dimensions.

[0099] like Figure 8 As shown, this embodiment of the invention also provides a battery cell fault diagnosis device, comprising:

[0100] The acquisition module is used to acquire the charging voltage of multiple battery cells collected at multiple sampling times;

[0101] The calculation module is used to calculate the sum of the absolute values ​​of MN of the battery cell under test and the Euclidean distance based on multiple charging voltages.

[0102] The slope of the sample entropy-scale factor curve of the battery cell under test is calculated based on multiple sampling times.

[0103] The acquisition module is also used to acquire preset cluster centers for normal battery cells and cluster centers for faulty battery cells;

[0104] The calculation module is also used to calculate the first Hamming proximity based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster centers of normal cells.

[0105] The second Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the faulty cells.

[0106] The judgment module is used to determine that the cell under test is faulty if the second Hamming proximity is greater than the first Hamming proximity.

[0107] Furthermore, the calculation module can also be used to: determine the maximum value among multiple charging voltages collected at the sampling time; calculate the average value of multiple charging voltages collected at the sampling time; calculate the MN value corresponding to the sampling time based on the charging voltage, maximum value, and average value of the cell under test collected at the sampling time; and calculate the sum of the absolute values ​​of multiple MN values ​​corresponding to multiple sampling times to obtain the sum of the absolute values ​​of MN.

[0108] Furthermore, the calculation module can also be used to: calculate the average value of multiple charging voltages collected at the sampling time; calculate the difference between the charging voltage of the cell under test collected at the sampling time and the average value; and calculate the Euclidean distance based on the multiple differences corresponding to multiple sampling times.

[0109] Furthermore, the calculation module can also be used to: downsample multiple sampling times according to multiple different scale factors to obtain multiple sampling times under each scale factor; calculate the sample entropy corresponding to the scale factor based on the multiple sampling times under the scale factor; fit multiple scale factors and the corresponding multiple sample entropies to obtain the sample entropy-scale factor curve; and determine the slope of the sample entropy-scale factor curve.

[0110] Furthermore, the normal cell cluster centers include the normal cell MN cluster centers, the normal cell Euclidean distance cluster centers, and the normal cell slope cluster centers; the faulty cell cluster centers include the faulty cell MN cluster centers, the faulty cell Euclidean distance cluster centers, and the faulty cell slope cluster centers.

[0111] The acquisition module can also be used to: acquire the sum of the absolute values ​​of MN, Euclidean distance, and slope of the sample entropy-scale factor curve for each training sample cell in multiple training sample cells; normalize the sum of the absolute values ​​of MN, the Euclidean distance, and the slope of the sample entropy-scale factor curve; take the absolute value of the normalized slope of the sample entropy-scale factor curve, and assign weights to the absolute values ​​of the sum of the absolute values ​​of MN, Euclidean distance, and slope of the sample entropy-scale factor curve; perform k-means clustering on the weighted sum of the absolute values ​​of MN to obtain the MN cluster centers of normal cells and the MN cluster centers of faulty cells; perform k-means clustering on the weighted Euclidean distance to obtain the Euclidean distance cluster centers of normal cells and the Euclidean distance cluster centers of faulty cells; and perform k-means clustering on the absolute values ​​of the weighted slopes of the sample entropy-scale factor curve to obtain the slope cluster centers of normal cells and the slope cluster centers of faulty cells.

[0112] Furthermore, the calculation module calculates the first Hamming proximity based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster centers of normal cells. This may include:

[0113]

[0114] N1 is the first Hamming proximity score, n1, n2, and n3 are the sum of the absolute values ​​of MN of the cell under test, the Euclidean distance, and the slope of the sample entropy-scale factor curve, respectively, and n4, n5, and n6 are the MN cluster center, the Euclidean distance cluster center, and the slope cluster center of the normal cell, respectively.

[0115] Furthermore, the calculation module calculates the second Hamming proximity based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster centers of the faulty cells. This may include:

[0116]

[0117] N2 is the second Hamming proximity, n1, n2, and n3 are the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, and the slope of the sample entropy-scale factor curve, respectively, and n7, n8, and n9 are the cluster centers of faulty cells (MN, Euclidean distance, and slope), respectively.

[0118] Based on the same inventive concept as the battery cell fault diagnosis method described above, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the battery cell fault diagnosis methods described above.

[0119] The bus architecture (represented by a bus) can include any number of interconnected buses and bridges, linking various circuits including one or more processors (represented by a processor) and memory (represented by memory). The bus can also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface between the bus and receivers and transmitters. Receivers and transmitters can be the same element, a transceiver, providing a unit for communicating with various other devices over a transmission medium. The processor is responsible for managing the bus and general processing, while memory can be used to store data used by the processor during operation.

[0120] Since the computer device described in this embodiment of the invention is the computer device used to implement the battery cell fault diagnosis method in this embodiment, those skilled in the art can understand the specific implementation methods and various variations of the computer device in this embodiment based on the battery cell fault diagnosis method described in this embodiment. Therefore, how the computer device implements the method in this embodiment will not be described in detail here. Any computer device used by those skilled in the art to implement the battery cell fault diagnosis method in this embodiment falls within the scope of protection of this invention.

[0121] Based on the same inventive concept as the aforementioned battery cell fault diagnosis method, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the aforementioned battery cell fault diagnosis methods.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] 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 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0125] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0126] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for diagnosing battery cell faults, characterized in that, include: Acquire the charging voltage of multiple battery cells at multiple sampling times; The sum of the absolute values ​​of MN of the battery cell under test and the Euclidean distance are calculated based on the multiple charging voltages mentioned above. The slope of the sample entropy-scale factor curve of the battery cell under test is calculated based on multiple sampling times. Obtain the preset cluster centers for normal and faulty battery cells; The first Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cell under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the normal cell. The second Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the faulty cells. If the second Hamming proximity is greater than the first Hamming proximity, then the cell under test is determined to be faulty. Calculating the sum of the absolute values ​​of MN based on the plurality of said charging voltages includes: Determine the maximum value among the multiple charging voltages collected at the sampling time; Calculate the average value of the multiple charging voltages collected at this sampling time; The MN value corresponding to the sampling time is calculated based on the charging voltage, the maximum value, and the average value of the battery cell under test collected at the sampling time. The sum of the absolute values ​​of the multiple MN values ​​corresponding to the multiple sampling times is calculated to obtain the sum of the absolute values ​​of MN.

2. The battery cell fault diagnosis method as described in claim 1, characterized in that, Calculating the Euclidean distance based on multiple charging voltages includes: Calculate the average value of the multiple charging voltages collected at the sampling time; Calculate the difference between the charging voltage of the battery cell under test collected at the sampling time and the average value; The Euclidean distance is calculated based on the multiple differences corresponding to the multiple sampling times.

3. The battery cell fault diagnosis method as described in claim 1, characterized in that, The slope of the sample entropy-scale factor curve of the battery cell under test is calculated based on multiple sampling times, including: The sampling times are downsampled according to multiple different scale factors to obtain multiple sampling times under each scale factor; Calculate the sample entropy corresponding to the scale factor based on the multiple sampling times under the scale factor; The sample entropy-scale factor curve is obtained by fitting multiple scale factors and corresponding sample entropies. Determine the slope of the sample entropy-scale factor curve.

4. The battery cell fault diagnosis method as described in claim 1, characterized in that, The normal cell cluster centers include normal cell MN cluster centers, normal cell Euclidean distance cluster centers, and normal cell slope cluster centers; the faulty cell cluster centers include faulty cell MN cluster centers, faulty cell Euclidean distance cluster centers, and faulty cell slope cluster centers. Obtain the preset cluster centers for normal and faulty battery cells, including: Obtain the sum of the absolute values ​​of MN, the Euclidean distance, and the slope of the sample entropy-scale factor curve for each of the multiple training sample cells; The sum of the absolute values ​​of the multiple MNs is normalized, the Euclidean distances are normalized, and the slopes of the multiple sample entropy-scale factor curves are normalized. Take the absolute value of the slope of the normalized sample entropy-scale factor curve, and assign weights to the sum of the absolute values ​​of the normalized MN, the Euclidean distance, and the absolute value of the slope of the sample entropy-scale factor curve. K-means clustering is performed on the sum of the absolute values ​​of the multiple weighted MN values ​​to obtain the MN cluster centers of the normal battery cells and the MN cluster centers of the faulty battery cells; K-means clustering is performed on the weighted Euclidean distances to obtain the Euclidean distance cluster centers of the normal battery cells and the Euclidean distance cluster centers of the faulty battery cells; K-means clustering is performed on the absolute values ​​of the slopes of the multiple weighted sample entropy-scale factor curves to obtain the cluster centers of the normal cell slope and the faulty cell slope.

5. The battery cell fault diagnosis method as described in claim 4, characterized in that, The first Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster centers of the normal cells, including: N1=1- ； N1 is the first Hamming proximity score. , , The values ​​are, in order, the sum of the absolute values ​​of MN of the tested battery cell, the Euclidean distance, and the slope of the sample entropy-scale factor curve. , , The cluster centers are, in order: the MN cluster center of the normal battery cell, the Euclidean distance cluster center of the normal battery cell, and the slope cluster center of the normal battery cell.

6. The battery cell fault diagnosis method as described in claim 4, characterized in that, The second Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster centers of the faulty cells, including: N2=1- ； N2 is the second Hamming proximity score. , , The values ​​are, in order, the sum of the absolute values ​​of MN of the tested battery cell, the Euclidean distance, and the slope of the sample entropy-scale factor curve. , , The cluster centers are, in order: the MN cluster center of the faulty battery cell, the Euclidean distance cluster center of the faulty battery cell, and the slope cluster center of the faulty battery cell.

7. A battery cell fault diagnosis device, characterized in that, include: The acquisition module is used to acquire the charging voltage of multiple battery cells collected at multiple sampling times; The calculation module is used to calculate the sum of the absolute values ​​of MN of the battery cell under test and the Euclidean distance based on the multiple charging voltages. The slope of the sample entropy-scale factor curve of the battery cell under test is calculated based on multiple sampling times. The acquisition module is also used to acquire preset normal cell cluster centers and faulty cell cluster centers; The calculation module is also used to calculate the first Hamming proximity based on the sum of the absolute values ​​of MN of the cell under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the normal cell. The second Hamming proximity is calculated based on the sum of the absolute values ​​of MN of the cells under test, the Euclidean distance, the slope of the sample entropy-scale factor curve, and the cluster center of the faulty cells. The judgment module is used to determine that the battery cell under test is faulty if the second Hamming proximity is greater than the first Hamming proximity. Calculating the sum of the absolute values ​​of MN based on the plurality of said charging voltages includes: Determine the maximum value among the multiple charging voltages collected at the sampling time; Calculate the average value of the multiple charging voltages collected at this sampling time; The MN value corresponding to the sampling time is calculated based on the charging voltage, the maximum value, and the average value of the battery cell under test collected at the sampling time. The sum of the absolute values ​​of the multiple MN values ​​corresponding to the multiple sampling times is calculated to obtain the sum of the absolute values ​​of MN.

8. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1-6.

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