Electric vehicle battery fault detection method and system, electronic device, and storage medium

Abstract

The application discloses a kind of electric vehicle battery fault detection method and system, electronic equipment, storage medium, it gives consideration to the electric heat data of battery multiple dimensions, can more comprehensively, accurately reflect the working condition of battery, to improve the accuracy of electric vehicle battery fault diagnosis, and, by the high-dimensional electric heat data of acquisition dimension reduction processing, while retaining electric heat data characteristics greatly reduce data processing amount, improve the efficiency of diagnosis, simultaneously, the distance between the electric heat data feature sequence is calculated using the improved Minkowski algorithm, so that distance measurement is not affected by each dimension data scale, improve the accuracy of distance calculation result, to further improve the accuracy of electric vehicle battery fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of battery fault detection, and in particular to a method and system for detecting battery faults in an electric vehicle, an electronic device, and a computer-readable storage medium. Background Art

[0002] With the rapid development of charging infrastructure, electric vehicles (EVs) are gaining significant attention and support as an effective means of ensuring national energy security and addressing environmental crises. Batteries, the "heart" of EVs, have rapidly developed and become widely used. However, safety incidents caused by battery failures are frequent, posing a serious threat to user safety. Therefore, a fast and accurate EV battery fault detection method is urgently needed to maintain safe operation.

[0003] Currently, there are three main battery fault detection methods. The first is a model-based method that compares the residual signal of the model output and the measured value, or compares the calculation using different model parameters. However, this method requires an accurate battery model, and the establishment of battery models is often complex and depends on specific operating conditions. For unknown or complex fault modes, the model may not accurately predict. The second is a knowledge-based method, such as neural networks, expert systems, and fault trees. This method has poor real-time performance, while fault detection requires timely detection. Once the knowledge base is established, its rules and logic are usually fixed, with poor flexibility and scalability. The third is a data-driven method, such as comparing battery consistency or capturing abnormal fluctuations and other characteristics of measured values. This method does not rely on a specific battery model and has good real-time performance, meeting the requirements of fault detection. However, existing data-driven methods detect battery faults based solely on measured voltage. For example, patent CN116840725A discloses a battery pack fault detection method that identifies faulty cells based on the voltage characteristics of the cells. However, the voltage characteristics of some faults are very similar and difficult to distinguish, resulting in poor detection accuracy. Summary of the Invention

[0004] The present invention provides an electric vehicle battery fault detection method and system, electronic equipment, and a computer-readable storage medium, which can improve the accuracy of electric vehicle battery fault diagnosis and also improve the diagnosis efficiency.

[0005] According to one aspect of the present invention, a method for detecting a battery failure in an electric vehicle is provided, comprising the following steps:

[0006] Collecting characteristic data of each battery cell in the power battery in real time, constructing a characteristic data sequence of each battery cell, and preprocessing and normalizing the collected characteristic data, wherein the characteristic data includes multiple electrical characteristic data and multiple thermal characteristic data;

[0007] Perform dimensionality reduction processing on the normalized feature data sequence of each battery cell to extract the electric and thermal data feature sequence of each battery cell;

[0008] The improved Minkowski algorithm is used to calculate the distance between the electrothermal data feature sequences of any two battery cells. If the distance between any two electrothermal data feature sequences is less than a preset threshold, the battery status is determined to be normal; otherwise, the battery is determined to be faulty.

[0009] Furthermore, the process of performing dimensionality reduction processing on the normalized feature data sequence of each battery cell to extract the electrothermal data feature sequence of each battery cell includes the following:

[0010] Divide all data points in the normalized feature data sequence into two data point sets;

[0011] Using multiple query data points to search between two data point sets, each data point set is divided into multiple local data sets, each local data set includes the (k+1) nearest neighbor points of each query data point;

[0012] Find the local data set where each data point in the feature data sequence is located, and calculate the weight between this data point and all other data points in the local data set to construct a weight vector;

[0013] Embed each data point in the feature data sequence from the high-dimensional data space to the low-dimensional data space to obtain an embedded data sequence;

[0014] Based on the weight vector and the embedded data sequence, the embedding error of the feature data sequence is calculated. If the embedding error is less than a threshold, the embedded data sequence is used as the electrothermal data feature sequence.

[0015] Furthermore, the process of dividing the normalized feature data sequence into two data point sets includes the following:

[0016] Calculate the average value of all data points in each feature data dimension in the feature data sequence, and construct the average data point based on the average value of all feature data dimensions;

[0017] Calculate the similarity between all data points and the average data point;

[0018] If the similarity between a data point and the average data point is greater than a preset similarity threshold, the data point is divided into the first data point set; otherwise, the data point is divided into the second data point set.

[0019] Furthermore, the similarity between any data point and the average data point is calculated based on the following formula:

[0020]

[0021] Among them, d i Indicates the similarity between the i-th data point and the average data point in the feature data sequence, n represents the feature data dimension, t im Represents the value of the feature data of the i-th data point in the m-th dimension, C m Represents the value of the m-th dimension feature data of the average data point.

[0022] Furthermore, the embedding error is calculated based on the following formula:

[0023]

[0024] Among them, E(T) represents the embedding error, N represents the total number of data points in the feature data sequence, and J i represents the set of k nearest neighbors of the i-th data point, w ij represents the weight between the i-th data point and its j-th nearest neighbor, t i represents the embedded data of the i-th data point, t j Represents the embedded data of the jth nearest neighbor.

[0025] Furthermore, the process of searching two data point sets using multiple query data points to divide each data point set into multiple local data sets includes the following:

[0026] Set the query data point and calculate the similarity between the query data point and the average data point. If the similarity between the two is greater than the preset threshold, the query data point is classified into the first data point set, otherwise it is classified into the second data point set. In the classified data point set, the (k+1) data points with the greatest similarity to the current query data point are screened out, and these (k+1) data points are divided into a local data set, and these (k+1) data points are removed from the current data point set. Then, the query data point is reset and the above process is repeated, and it is iterated continuously until each data point set is divided into multiple local data sets, and each local data set contains (k+1) data points.

[0027] Furthermore, the distance between the electrothermal data feature sequences of any two battery cells is calculated based on the following formula:

[0028]

[0029] Among them, x and y represent the characteristic sequences of the electric and thermal data of the two battery cells, p is a parameter with a value range of [1, 2, ∞], x i and yi Represents the i-th data feature value of the electrothermal data feature sequence of battery cell x and battery cell y, respectively. The superscript T represents transposition, and S -1 () represents the inverse matrix of the covariance matrix.

[0030] In addition, the present invention also provides an electric vehicle battery fault detection system, which adopts the electric vehicle battery fault detection method described above, including:

[0031] The data acquisition and processing module collects the characteristic data of each battery cell in the power battery in real time, constructs a characteristic data sequence of each battery cell, and preprocesses and normalizes the collected characteristic data, wherein the characteristic data includes multiple electrical characteristic data and multiple thermal characteristic data;

[0032] The data dimension reduction processing module is used to perform dimension reduction processing on the normalized feature data sequence of each battery cell to extract the electric and thermal data feature sequence of each battery cell;

[0033] The battery fault diagnosis module is used to calculate the distance between the electrothermal data feature sequences of any two battery cells using an improved Minkowski algorithm. If the distance between any two electrothermal data feature sequences is less than a preset threshold, the battery status is determined to be normal; otherwise, the battery is determined to be faulty.

[0034] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the above method by calling the computer program stored in the memory.

[0035] In addition, the present invention also provides a computer-readable storage medium for storing a computer program for performing electric vehicle battery fault detection, wherein the computer program executes the steps of the above-mentioned method when running on a computer.

[0036] The present invention has the following beneficial effects:

[0037] The electric vehicle battery fault detection method of the present invention first collects the electrical and thermal characteristic data of each battery cell in real time and normalizes them. Then, the normalized characteristic data sequence of each battery cell is subjected to dimensionality reduction processing to extract the electric and thermal data characteristic sequence of each battery cell. Finally, an improved Minkowski algorithm is used to calculate the distance between the electric and thermal data characteristic sequences of any two battery cells. If the distance between any two electric and thermal data characteristic sequences is less than a preset threshold, the battery state is determined to be normal; otherwise, the battery is determined to be faulty. The electric vehicle battery fault detection method of the present invention takes into account the electric and thermal data of multiple dimensions of the battery, which can more comprehensively and accurately reflect the working state of the battery, thereby improving the accuracy of electric vehicle battery fault diagnosis. In addition, by performing dimensionality reduction processing on the collected high-dimensional electric and thermal data, the data processing amount is greatly reduced while retaining the characteristics of the electric and thermal data, thereby improving diagnostic efficiency. At the same time, the improved Minkowski algorithm is used to calculate the distance between the electric and thermal data characteristic sequences, so that the distance metric is not affected by the scale of the data in each dimension, improving the accuracy of the distance calculation result, thereby further improving the accuracy of electric vehicle battery fault diagnosis.

[0038] In addition, the electric vehicle battery fault detection system of the present invention also has the above advantages.

[0039] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 It is a flow chart of an electric vehicle battery fault detection method using a current transformer according to a preferred embodiment of the present application.

[0042] Figure 2 yes Figure 1 Schematic diagram of the sub-process of step S2 in FIG.

[0043] Figure 3 yes Figure 2 Schematic diagram of the sub-process of step S21.

[0044] Figure 4 This is another flow chart of the electric vehicle battery fault detection method using a current transformer according to a preferred embodiment of the present application.

[0045] Figure 5This is another flow chart of the electric vehicle battery fault detection method of the current transformer according to the preferred embodiment of the present application.

[0046] Figure 6 This is a schematic diagram of the module structure of an electric vehicle battery fault detection system using a current transformer according to another embodiment of the present application. DETAILED DESCRIPTION

[0047] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0048] Reference Figure 1 , a preferred embodiment of the present application provides an electric vehicle battery fault detection method, comprising the following contents:

[0049] Step S1: collecting characteristic data of each battery cell in the power battery in real time, constructing a characteristic data sequence of each battery cell, and preprocessing and normalizing the collected characteristic data, wherein the characteristic data includes multiple electrical characteristic data and multiple thermal characteristic data;

[0050] Step S2: performing dimensionality reduction processing on the normalized feature data sequence of each battery cell to extract the electric and thermal data feature sequence of each battery cell;

[0051] Step S3: Using the improved Minkowski algorithm to calculate the distance between the electrothermal data feature sequences of any two battery cells, if the distance between any two electrothermal data feature sequences is less than a preset threshold, the battery is determined to be in normal condition, otherwise it is determined that the battery has a fault.

[0052] It can be understood that the electric vehicle battery fault detection method of the present invention first collects the electrical characteristic data and thermal characteristic data of each battery cell in real time and performs normalization processing, then performs dimensionality reduction processing on the characteristic data sequence after normalization processing of each battery cell, extracts the electric and thermal data characteristic sequence of each battery cell, and finally uses the improved Minkowski algorithm to calculate the distance between the electric and thermal data characteristic sequences of any two battery cells. If the distance between any two electric and thermal data characteristic sequences is less than a preset threshold, the battery state is determined to be normal, otherwise it is determined that the battery has failed. The electric vehicle battery fault detection method of the present invention takes into account the electric and thermal data of multiple dimensions of the battery, which can more comprehensively and accurately reflect the working state of the battery, thereby improving the accuracy of electric vehicle battery fault diagnosis. In addition, by performing dimensionality reduction processing on the collected high-dimensional electric and thermal data, the data processing amount is greatly reduced while retaining the characteristics of the electric and thermal data, and the processing efficiency is improved. At the same time, the use of the improved Minkowski algorithm to calculate the distance between the electric and thermal data characteristic sequences makes the distance metric unaffected by the scale of the data in each dimension, improves the accuracy of the distance calculation result, and further improves the accuracy of electric vehicle battery fault diagnosis.

[0053] It is understood that in step S1, characteristic data of each battery cell in the power battery is collected in real time, wherein the characteristic data includes multiple electrical characteristic data and multiple thermal characteristic data. Specifically, the electrical characteristic data includes voltage, current, peak voltage, and state of charge (SOC), and the thermal characteristic data includes temperature, thermal resistance, thermal distribution uniformity, and state of health (SOH), thereby constructing a characteristic data sequence for each battery cell. In addition, the collected characteristic data needs to be preprocessed, such as cleaning the data, removing noise and outliers, and then normalizing the preprocessed characteristic data to facilitate subsequent data analysis and processing.

[0054] It is understandable that Figure 2 As shown, in step S2, the process of performing dimensionality reduction processing on the normalized feature data sequence of each battery cell to extract the electrothermal data feature sequence of each battery cell includes the following:

[0055] Step S21: Divide all data points in the normalized feature data sequence into two data point sets;

[0056] Step S22: using multiple query data points to search in two data point sets to divide each data point set into multiple local data sets, each local data set including the (k+1) nearest neighbor points of each query data point;

[0057] Step S23: Find the local data set where each data point in the feature data sequence is located, and calculate the weight between the data point and all other data points in the local data set to construct a weight vector;

[0058] Step S24: embedding each data point in the feature data sequence from the high-dimensional data space into the low-dimensional data space to obtain an embedded data sequence;

[0059] Step S25: Based on the weight vector and the embedded data sequence, the embedding error of the feature data sequence is calculated. If the embedding error is less than a threshold, the embedded data sequence is used as the electrothermal data feature sequence.

[0060] Specifically, first, all data points in the normalized feature data sequence of each battery cell are divided into two data point sets based on similarity, and the feature data sequence is preliminarily divided. Figure 3 As shown, in step S21, the process of dividing the normalized feature data sequence into two data point sets includes the following:

[0061] Step S211: Calculate the average value of all data points in the feature data sequence in each feature data dimension, and construct an average data point based on the average values ​​of all feature data dimensions;

[0062] Step S212: Calculate the similarity between all data points and the average data point;

[0063] Step S213: If the similarity between a certain data point and the average data point is greater than a preset similarity threshold, the data point is divided into the first data point set; otherwise, the data point is divided into the second data point set.

[0064] Specifically, for the normalized feature data sequence of each battery cell, the average value of all data points in the feature data sequence in each feature data dimension is first calculated, and then the average data point is constructed based on the average value of all feature data dimensions. Each data point is an eight-dimensional vector. For example, the average values ​​of all data points in the voltage dimension, current dimension, peak voltage dimension, charging state dimension, temperature dimension, thermal resistance dimension, thermal distribution uniformity dimension and health state dimension are first calculated, and then the average data point is constructed based on the eight average values, that is, the average data point includes the average values ​​of the eight feature data dimensions. Then, the similarity between each data point and the average data point is calculated, and the similarity between any data point and the average data point is calculated based on the following formula:

[0065]

[0066] Among them, d irepresents the similarity between the i-th data point and the average data point in the feature data sequence, n represents the feature data dimension, for example, n = 8, t im Represents the value of the feature data of the i-th data point in the m-th dimension, C m Represents the value of the m-th dimension feature data of the average data point.

[0067] Next, the data set is divided and the calculated similarity is compared with a preset similarity threshold. If the similarity is greater than the preset similarity threshold, the data point is divided into the first data point set. If the similarity is not greater than the preset similarity threshold, the data point is divided into the second data point set. This process is repeated until all data points in the feature data sequence are divided into the first data point set and the second data point set.

[0068] Then, the query data point is used to search the two data point sets divided in step S21 to find the (k+1) nearest neighboring data points of each query data point, and these (k+1) data points are constructed into a local data set. The process of searching the two data point sets using multiple query data points to divide each data point set into multiple local data sets includes the following:

[0069] Set the query data point and calculate the similarity between the query data point and the average data point. If the similarity between the two is greater than the preset threshold, the query data point is classified into the first data point set, otherwise it is classified into the second data point set. In the classified data point set, the (k+1) data points with the greatest similarity to the current query data point are screened out, and these (k+1) data points are divided into a local data set, and these (k+1) data points are removed from the current data point set. Then, the query data point is reset and the above process is repeated, and it is iterated continuously until each data point set is divided into multiple local data sets, and each local data set contains (k+1) data points.

[0070] Specifically, the query data point is also an n-dimensional vector, for example, including characteristic data in eight dimensions: voltage, current, peak voltage, charge state, temperature, thermal resistance, thermal distribution uniformity, and health state. The similarity between the query data point and the average data point is calculated using the aforementioned similarity calculation formula. If the similarity between the two is greater than a preset threshold, the query data point is assigned to the first data point set; otherwise, it is assigned to the second data point set. Then, assuming that the query data point is assigned to the first data point set, the similarity between the query data point and all data points in the first data point set is again calculated, and the (k+1) data points with the largest similarity are selected. These (k+1) data points are then divided into a local data set. It can be assumed that the similarity between any two data points in these (k+1) data points is large, and these (k+1) data points are removed from the current data point set. Then, the query data point is reset, and the above process is repeated, iterating continuously until the first data point set is divided into multiple local data sets, each of which contains (k+1) data points. In addition, when some data points still remain in the first data point set after iterative division, they can be supplemented by interpolation to form a local data set, or the remaining data points can be directly eliminated.

[0071] Next, find the local data set where each data point in the feature data sequence is located. Since the first data point set and the second data point set have been divided into multiple local data sets in step S22, the local data set where each data point is located can be directly found. Moreover, since the similarity between the (k+1) data points in the local data set and the query data point is large, the remaining k data points in the local data set can be considered to be the k nearest neighbor points of the data point. Then, the weights between the data point and all the other data points in the local data set are calculated to construct a weight vector, where the weight vector can be expressed as: W i =[w i1 ,w i2 ,...,w ik ],W i Represents the weight vector of the i-th data point, w ik Represents the weight between the i-th data point and its k-th nearest neighbor, and the weight is calculated using a Gaussian function.

[0072] Then, the data dimension of each data point is embedded from the high-dimensional data space to the low-dimensional data space. For example, the data dimension of each data point is embedded from the eight-dimensional data space to the one-dimensional or two-dimensional data space to obtain an embedded data sequence, where the embedded data sequence can be expressed as: T = [t1, t2, ..., t N ], T represents the embedded data sequence, N represents the total number of data points, t Nrepresents the embedded data of the Nth data point. The specific data embedding process belongs to the existing technology and will not be elaborated here. For example, the dimension of the low-dimensional space is first determined, and then the embedding matrix (i.e., the embedded data sequence) is randomly initialized. An optimization algorithm is used to optimize the embedding matrix. For the objective function of the optimization algorithm, the gradient of the embedding matrix needs to be calculated. Then, the value of the embedding matrix is ​​updated using the gradient descent method. Finally, the embedding matrix is ​​obtained through the optimization process.

[0073] Finally, based on the weight vector and the embedded data sequence, the embedding error of the feature data sequence is calculated. If the embedding error is less than the threshold, the embedded data sequence is used as the feature sequence of the electric and thermal data. If the embedding error is not less than the threshold, it is considered that the data embedding does not meet the requirements and the local characteristics of the data are not well preserved. It is necessary to readjust the similarity threshold or adjust the embedding parameters until the embedding error is less than the threshold. The embedding error is calculated based on the following formula:

[0074]

[0075] Among them, E(T) represents the embedding error, N represents the total number of data points in the feature data sequence, and J i represents the set of k nearest neighbors of the i-th data point, w ij represents the weight between the i-th data point and its j-th nearest neighbor, t i represents the embedded data of the i-th data point, t j Represents the embedded data of the jth nearest neighbor.

[0076] It can be understood that the data dimensionality reduction process of the present invention first divides all data points into two data point sets based on the similarity algorithm, realizing the coarse clustering of data points, and then uses the query data point to search in the two data point sets to divide each data point set into multiple local data sets, thereby realizing the fine clustering of data points, so that the k nearest neighbor points of each data point can be quickly found, which greatly improves the dimensionality reduction efficiency, especially when performing data dimensionality reduction on large-scale high-dimensional data, the efficiency improvement is more obvious, and the local characteristics of the feature data sequence are also retained. In addition, the accuracy of data dimensionality reduction is evaluated by constructing an embedding error algorithm, which ensures that the data after dimensionality reduction retains the local characteristics of the original data, which is conducive to improving the accuracy of fault diagnosis. Compared with existing dimensionality reduction algorithms, it has better local structure optimization capabilities, interpretability and robustness, and can better handle nonlinear data. For example, compared with the existing local linear embedding algorithm, the present invention can better process nonlinear data and retain the local structure of the data; compared with the existing T-SNE algorithm, the present invention can generate clearer low-dimensional representations and better display the relationship between data; compared with the existing LLE algorithm, the LLE algorithm is not effective when processing large-scale data and is very sensitive to the number of selected neighboring points. The present invention is more efficient and robust when processing large-scale high-dimensional data by first constructing two data points and then dividing them into different local data sets.

[0077] It can be understood that in the step S3, the improved Minkowski algorithm is used to calculate the distance between the electrothermal data feature sequences of any two battery cells. If the distance between any two electrothermal data feature sequences is less than a preset threshold, the battery state is determined to be normal, otherwise the battery is determined to be faulty. It can be understood that the electrothermal feature data between multiple battery cells in normal operation will show a certain similarity in the data space. If the similarity between the electrothermal feature data of two battery cells exceeds the deviation range, it is determined that the battery has a certain fault or is in an abnormal operating state. Therefore, the present invention uses an improved Minkowski algorithm to calculate the distance between the electrothermal data feature sequences of any two battery cells. If the distance between the electrothermal data feature sequences of any two battery cells is less than a preset threshold, the battery is determined to be normal, otherwise the battery is determined to be faulty. The expression of the improved Minkowski algorithm is:

[0078]

[0079] Among them, x and y represent the characteristic sequences of the electric and thermal data of the two battery cells, p is a parameter with a value range of [1, 2, ∞], x i and y i Represents the i-th data feature value of the electrothermal data feature sequence of battery cell x and battery cell y, respectively. The superscript T represents transposition, and S -1() represents the inverse of the covariance matrix. The value of p varies with the data type. When the data is very sensitive to incremental changes in each dimension, p is 1. When the dimensions are equally important and independent, p is 2. When the data is very sensitive to extreme values, p is ∞. For example, small changes in temperature sometimes indicate that the system is approaching its thermal limit, so p is 1 when calculating the temperature between different battery cells.

[0080] It can be understood that the covariance structure of the data is introduced into the improved Minkowski algorithm of the present invention, so that the distance metric is not affected by the scale of the characteristic data in each dimension, can better reflect the intrinsic structure of the data, and improve the accuracy of the distance calculation results, thereby further improving the accuracy of electric vehicle battery fault diagnosis.

[0081] Optionally, considering that the data characteristics of the battery in the charging state and the discharging state are quite different, in order to further improve the accuracy of battery fault diagnosis, the present invention performs the above-mentioned data collection, dimensionality reduction, and distance calculation processes in the battery charging state and the battery discharging state respectively. Since the distance measurement judgment thresholds in the charging stage and the discharging stage are different, a new threshold is obtained by assigning weights to the two thresholds, and then the new threshold is used to determine whether a fault occurs.

[0082] Alternatively, as Figure 4 As shown, the electric vehicle battery fault detection method also includes the following contents:

[0083] Step S4: Evaluate the fault detection result.

[0084] Specifically, the F1-score method is used to evaluate the detection results to verify the effectiveness and accuracy of battery fault detection. The F1-score method includes four indicators: accuracy, precision, recall rate and F1 score. A represents accuracy, P represents precision, R represents recall, F1 represents F1 score, TT represents the diagnosis of fault and the actual fault, TF represents the diagnosis of fault and the actual normal, FT represents the diagnosis of normal and the actual fault, and FF represents the diagnosis of normal and the actual normal. Then, the effectiveness and accuracy of the fault detection results are evaluated based on the four indicators. The specific evaluation logic belongs to the existing technology and will not be repeated here. In addition, when P is high but R is low, a normal battery may be mistakenly diagnosed as a faulty battery, which proves that the probability of misjudging a normal battery as a fault is high, which means that the distance threshold is set low and the threshold should be increased. If R is high but P is low, it means that the detection is too conservative and cannot identify certain battery faults, which means that the distance threshold is set high and the threshold should be lowered.

[0085] Alternatively, as Figure 5As shown, the electric vehicle battery fault detection method also includes the following contents:

[0086] Step S5: constructing a correlation analysis model based on the collected electrothermal characteristic data, and using the improved Pearson correlation coefficient to analyze the correlation between the characteristic data, and determining the fault type according to the correlation results.

[0087] Specifically, for short-circuit faults, the correlation analysis model constructed is: R1 = [F(V1′, V2′)F(V2′, V3′)...F(V N ′, V1′)], V1′ represents the voltage sum of the first battery cell and the second battery cell, V i ′ represents the voltage sum of the i-th battery cell and the i+1-th battery cell, and F() represents the correlation calculated using the improved Pearson correlation coefficient. When the i-th battery cell has a short circuit fault, the correlation coefficient F(V i ' -2 ,V i ' -1 ) and F(V i ′,V i ' +1 ) will be abnormal, and F(V i ' -1 ,V i ′) is still normal, where the abnormal correlation coefficient means that it deviates from the preset value range, and normal means that it is within the preset value range. Therefore, by performing correlation analysis on the voltage data of the battery cell, short circuit faults can be accurately diagnosed. Furthermore, for connection faults, the constructed correlation analysis model is: R2=[F(ΔV 1,2 +m,I) F(ΔV 2,3 +m,I) ... F(ΔV N,1 +m,I)], where ΔV 1,2 =|V1′-V′2|, m represents the correction value, I represents the battery current. When the connection between the i-th battery cell and the i+1-th battery cell fails, the correlation coefficient F(V i-2 ,V i-1 ) and F(V i ,V i+1 ) will be swapped and will not approach 0, and F(ΔV i-1，i +m,I) and F(ΔV i,i+1+m,I) will be close to 1. Therefore, based on the correlation analysis of the voltages of different battery cells, the correlation analysis of the voltage difference and current of different battery cells can be performed to diagnose connection faults. In addition, the voltage sensor fault is also based on the correlation analysis of the voltages of different battery cells. By performing the correlation analysis of the voltage difference and current of different batteries, when the voltage sensor corresponding to the i-th battery cell fails, the correlation coefficient F(V i-2 ,V i-1 ) and F(V i ,V i+1 ) will be swapped, and no large correlation value will appear in R2, that is, all correlation coefficients in R2 are smaller than the preset threshold.

[0088] In addition, for the heat dissipation coefficient failure, the correlation analysis model constructed is: R3=[F(T1,θ1) F(T2,θ2) ...F(T N ,θ N )], R4=[F(T1′,T′2) F(T′2,T′3) ... F(T′ N ,T′1)], T1 and θ1 represent the temperature and thermal resistance of the first battery cell respectively, and T1′ represents the temperature sum of the first and second battery cells. When the cooling system of the i-th battery cell fails, the correlation coefficient F(T i ,θ i ), F(T′ i-2 ,T′ i-1 ), F(T′ i ,T′ i+1 ) is abnormal, and F(T′ i-1 ,T′ i Therefore, by analyzing the correlation between the thermal resistance and temperature of each battery cell, as well as the correlation between the temperatures of different battery cells, it is possible to diagnose heat dissipation system failures.

[0089] In addition, for thermal runaway faults, the correlation analysis model constructed is: R5=[F(V1,T1)F(V2,T2)...F(V N ,T N )], R6=[F(I1,T1)F(I2,T2)...F(I N ,T N )], V1, I1 and T1 represent the voltage, current and temperature of the first battery cell respectively. When the i-th battery cell has a thermal runaway fault, its temperature will rise sharply, its voltage will drop sharply, and its current will rise sharply. The correlation coefficient F(V i ,T i ) will decrease abnormally, and F(I i ,T iTherefore, thermal runaway faults can be diagnosed by analyzing the correlation between the voltage and temperature, and the current and temperature of each battery cell.

[0090] In addition, for battery insulation failure, the correlation analysis model constructed is: R7=[F(θ1,θ2) F(θ2,θ3) ...F(θ N ,θ1)], when the insulation failure occurs in the i-th battery cell, its thermal resistance will decrease, and the thermal resistance of different battery cells will increase inconsistently, and the correlation coefficient F(θ i-1 ,θ i ), F(θ i ,θ i+1 ) will cause an exception.

[0091] In addition, for low capacity and low SOC faults, the constructed correlation analysis model is: It represents the ratio of the voltage of the i-th battery cell to the average battery voltage of the normal battery pack. If the voltage ratio fluctuates significantly and is accompanied by an abnormal temperature increase, then when the i-th battery cell has a low capacity and low SOC fault, An exception will occur.

[0092] In addition, for internal resistance fault, the correlation analysis model is constructed as follows: R9=[F(V 1,max ,SOC1) F(V 2,max ,SOC2) ... F(V N,max ,SOC 1N )],V 1,max and SOC1 represent the peak voltage and SOC state of the first battery cell respectively. When an internal resistance fault occurs, the peak voltage of the battery drops abnormally, and the SOC state and peak voltage will show a nonlinear state. Therefore, when an internal resistance fault occurs in the i-th battery cell, F(V i,max ,SOC i ) will cause an exception.

[0093] In addition, for different correlation analysis models, the process of analyzing the correlation between feature data using the improved Pearson correlation coefficient is as follows:

[0094] First, construct the input data matrix X based on the collected feature data, X∈R N×T×F , N represents the number of samples, T represents the time step, and F represents the number of features, that is, the feature dimension, where the features include the voltage V of each battery cell i Current I i , peak voltage V i，max 、Thermal resistance θ i and temperature T i , and the voltage of two adjacent battery cells and Vi ′、voltage difference ΔV i-1,i , battery current I, the ratio of the battery cell voltage to the average battery voltage of a normal battery pack Let y be the label vector, where y∈{0,1} N , 0 means normal, 1 means fault.

[0095] Then, using X as input, we use f1 filters of size k1 to get the feature map C1: C1 = ReLU(X*W1+b1), where * represents the convolution operation, W1 and b1 are the weight and bias of the convolution kernel respectively, ReLU is the activation function, and ReLU(x) = max(0,x).

[0096] Next, perform maximum pooling on C1 to obtain P1, which is the output of the feature extraction layer. Then use f2 filters of size k2 to obtain the feature map C2: C2 = ReLU (P1*W2+b2)

[0097] Then perform maximum pooling on C2 to obtain P2, which is the output of the feature optimization layer. Then flatten P2 into a feature vector F to obtain the improved Pearson correlation coefficient, which is expressed as:

[0098]

[0099] Among them, ρ i Represents the Pearson correlation coefficient vector of the i-th dimension feature, that is, including multiple F(), f i,j represents the eigenvalue of the jth sample on the i-th dimension feature, f i,j ∈F, and Represents the i-th dimension feature f i and the mean of the label vector y, represents the weight coefficient of the i-th dimension feature, Represents the weight coefficient of the i-th label.

[0100] It can be understood that the present invention extracts nonlinear features by introducing some CNN mechanisms to better capture the complex relationships between variables, making the entire network including feature extraction and correlation calculation trainable, and optimizing the problems of traditional person coefficients that cannot be updated and data real-time.

[0101] In addition, if Figure 6 As shown, another embodiment of the present invention further provides an electric vehicle battery fault detection system, preferably using the electric vehicle battery fault detection method as described above, comprising:

[0102] The data acquisition and processing module collects the characteristic data of each battery cell in the power battery in real time, constructs a characteristic data sequence of each battery cell, and preprocesses and normalizes the collected characteristic data, wherein the characteristic data includes multiple electrical characteristic data and multiple thermal characteristic data;

[0103] The data dimension reduction processing module is used to perform dimension reduction processing on the normalized feature data sequence of each battery cell to extract the electric and thermal data feature sequence of each battery cell;

[0104] The battery fault diagnosis module is used to calculate the distance between the electrothermal data feature sequences of any two battery cells using an improved Minkowski algorithm. If the distance between any two electrothermal data feature sequences is less than a preset threshold, the battery status is determined to be normal; otherwise, the battery is determined to be faulty.

[0105] It can be understood that the electric vehicle battery fault detection system of the present invention first collects the electrical and thermal characteristic data of each battery cell in real time and performs normalization processing, then performs dimensionality reduction processing on the characteristic data sequence after normalization processing of each battery cell to extract the electrothermal data characteristic sequence of each battery cell, and finally uses the improved Minkowski algorithm to calculate the distance between the electrothermal data characteristic sequences of any two battery cells. If the distance between any two electrothermal data characteristic sequences is less than a preset threshold, the battery state is determined to be normal, otherwise it is determined that the battery has failed. The electric vehicle battery fault detection method of the present invention takes into account the electrothermal data of multiple dimensions of the battery, which can more comprehensively and accurately reflect the working state of the battery, thereby improving the accuracy of electric vehicle battery fault diagnosis. In addition, by performing dimensionality reduction processing on the collected high-dimensional electrothermal data, the data processing amount is greatly reduced while retaining the characteristics of the electrothermal data, thereby improving processing efficiency. At the same time, the use of the improved Minkowski algorithm to calculate the distance between the electrothermal data characteristic sequences makes the distance metric unaffected by the scale of the data in each dimension, improves the accuracy of the distance calculation result, and further improves the accuracy of electric vehicle battery fault diagnosis.

[0106] In addition, the electric vehicle battery fault detection system further includes:

[0107] The fault detection result evaluation module is used to evaluate the fault detection result.

[0108] In addition, the electric vehicle battery fault detection system further includes:

[0109] The fault type diagnosis and identification module is used to build a correlation analysis model based on the collected electrothermal characteristic data, and use the improved Pearson correlation coefficient to analyze the correlation between the characteristic data, and determine the fault type according to the correlation results.

[0110] Among them, each module of the system embodiment corresponds to each step of the above method embodiment, so the specific working process and principle of each module will not be repeated here, and the corresponding reference is made to the above method embodiment.

[0111] In addition, another embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the above method by calling the computer program stored in the memory.

[0112] In addition, another embodiment of the present invention further provides a computer-readable storage medium for storing a computer program for performing electric vehicle battery fault detection, wherein the computer program executes the steps of the above-described method when running on a computer.

[0113] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical medium with a pattern of holes, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash-erasable programmable read-only memory (FLASH-EPROM), any other memory chip or cartridge, or any other medium that can be read by a computer. Instructions can further be transmitted or received via a transmission medium. The term transmission medium may include any tangible or intangible medium that can be used to store, encode, or carry instructions for execution by a machine, and includes digital or analog communication signals or other intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires of a bus used to transmit a computer data signal.

[0114] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0118] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0119] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

[0120] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for detecting battery failure in an electric vehicle, characterized in that: Includes the following: Collecting characteristic data of each battery cell in the power battery in real time, constructing a characteristic data sequence of each battery cell, and preprocessing and normalizing the collected characteristic data, wherein the characteristic data includes multiple electrical characteristic data and multiple thermal characteristic data; Perform dimensionality reduction processing on the normalized feature data sequence of each battery cell to extract the electric and thermal data feature sequence of each battery cell; The improved Minkowski algorithm is used to calculate the distance between the electrothermal data feature sequences of any two battery cells. If the distance between any two electrothermal data feature sequences is less than a preset threshold, the battery is judged to be in normal condition; otherwise, the battery is judged to be faulty. The distance between the electrothermal data feature sequences of any two battery cells is calculated based on the following formula: Among them, x and y represent the characteristic sequences of the electric and thermal data of the two battery cells, p is a parameter with a value range of [1, 2, ∞], x i and y i Represents the i-th data feature value of the electrothermal data feature sequence of battery cell x and battery cell y, respectively. The superscript T represents transposition, and S -1 () represents the inverse matrix of the covariance matrix, and n represents the dimension of the feature data.

2. The electric vehicle battery fault detection method according to claim 1, characterized in that: The process of performing dimensionality reduction processing on the normalized feature data sequence of each battery cell to extract the electric and thermal data feature sequence of each battery cell includes the following: Divide all data points in the normalized feature data sequence into two data point sets; Using multiple query data points to search between two data point sets, each data point set is divided into multiple local data sets, each local data set includes the (k+1) nearest neighbor points of each query data point; Find the local data set where each data point in the feature data sequence is located, and calculate the weight between this data point and all other data points in the local data set to construct a weight vector; Embed each data point in the feature data sequence from the high-dimensional data space to the low-dimensional data space to obtain an embedded data sequence; Based on the weight vector and the embedded data sequence, the embedding error of the feature data sequence is calculated. If the embedding error is less than a threshold, the embedded data sequence is used as the electrothermal data feature sequence.

3. The electric vehicle battery fault detection method according to claim 2, characterized in that: The process of dividing the normalized feature data sequence into two data point sets includes the following: Calculate the average value of all data points in each feature data dimension in the feature data sequence, and construct the average data point based on the average value of all feature data dimensions; Calculate the similarity between all data points and the average data point; If the similarity between a data point and the average data point is greater than a preset similarity threshold, the data point is divided into the first data point set; otherwise, the data point is divided into the second data point set.

4. The electric vehicle battery fault detection method according to claim 3, characterized in that: The similarity between any data point and the average data point is calculated based on the following formula: Among them, d i Indicates the similarity between the i-th data point and the average data point in the feature data sequence, n represents the feature data dimension, t im Represents the value of the feature data of the i-th data point in the m-th dimension, C m Represents the value of the m-th dimension feature data of the average data point.

5. The electric vehicle battery fault detection method according to claim 4, characterized in that: The embedding error is calculated based on the following formula: Among them, E(T) represents the embedding error, N represents the total number of data points in the feature data sequence, and J i represents the set of k nearest neighbors of the i-th data point, w ij represents the weight between the i-th data point and its j-th nearest neighbor, t i represents the embedded data of the i-th data point, t j Represents the embedded data of the jth nearest neighbor.

6. The electric vehicle battery fault detection method according to claim 3, characterized in that: The process of searching two data point sets using multiple query data points to divide each data point set into multiple local data sets includes the following: Set the query data point and calculate the similarity between the query data point and the average data point. If the similarity between the two is greater than the preset threshold, the query data point is classified into the first data point set, otherwise it is classified into the second data point set. In the classified data point set, the (k+1) data points with the greatest similarity to the current query data point are screened out, and these (k+1) data points are divided into a local data set, and these (k+1) data points are removed from the current data point set. Then, the query data point is reset and the above process is repeated, and it is iterated continuously until each data point set is divided into multiple local data sets, and each local data set contains (k+1) data points.

7. An electric vehicle battery fault detection system, using the electric vehicle battery fault detection method according to any one of claims 1 to 6, characterized in that: include: The data acquisition and processing module collects the characteristic data of each battery cell in the power battery in real time, constructs a characteristic data sequence of each battery cell, and preprocesses and normalizes the collected characteristic data, wherein the characteristic data includes multiple electrical characteristic data and multiple thermal characteristic data; The data dimension reduction processing module is used to perform dimension reduction processing on the normalized feature data sequence of each battery cell to extract the electric and thermal data feature sequence of each battery cell; The battery fault diagnosis module is used to calculate the distance between the electrothermal data feature sequences of any two battery cells using an improved Minkowski algorithm. If the distance between any two electrothermal data feature sequences is less than a preset threshold, the battery status is determined to be normal; otherwise, the battery is determined to be faulty.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the method according to any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium for storing a computer program for detecting battery failures in electric vehicles, characterized in that: When the computer program is run on a computer, the steps of the method according to any one of claims 1 to 6 are executed.

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