Battery fault detection method and device, electronic equipment and storage medium

Abstract

The invention provides a battery fault detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring voltage data of a single battery; respectively determining a voltage curve and a median voltage curve of the single battery according to the voltage data of the single battery; according to the voltage curve and the median voltage curve of the single battery, determining a deviation value between the voltage curve and the median voltage curve of the single battery; for any battery monomer, acquiring an average normalized value of the deviation values and a mean value of standard scores of the deviation values as characteristic data of the battery monomer; determining a local outlier factor of the single battery according to the characteristic data of the single battery; and when the local outlier factor of the single battery is smaller than a preset value, determining that the single battery does not have a fault. The battery fault detection is realized by combining the standard score related to the battery voltage and the average normalized value, and the information related to the battery voltage can be fully utilized, so that the accuracy of the battery fault detection can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of battery technology, and in particular to a battery fault detection method, device, electronic device and storage medium. Background Art

[0002] With the widespread application of batteries in many fields, especially in electric vehicles, battery fault detection has become crucial. Battery failure may lead to reduced equipment performance and shortened service life, and even endanger the safety of drivers and passengers in electric vehicles. For example, battery failure may cause a sudden drop in the range of electric vehicles and a sudden loss of power. Moreover, due to the complexity of the battery system, faults may be hidden under various operating parameters and conditions. If not detected in time, it may cause more serious problems. Therefore, battery fault detection is a necessary means to ensure the normal use of batteries and the safe and stable operation of related equipment.

[0003] Existing battery fault detection methods generally only use standard scores related to battery voltage to detect battery faults. However, only using standard scores related to battery voltage to detect battery faults cannot effectively utilize complete information related to battery voltage, which in turn leads to low accuracy of battery fault detection. Summary of the invention

[0004] The embodiments of the present application provide a battery fault detection method, device, electronic device and storage medium to solve the problem of low battery fault detection accuracy caused by only performing battery fault detection through a standard score related to the battery voltage in the related art.

[0005] In a first aspect, an embodiment of the present application provides a battery fault detection method, which is applied to a battery, wherein the battery includes a plurality of battery cells, and the method includes:

[0006] Acquiring voltage data of the battery cell;

[0007] Determining the voltage curve and the median voltage curve of the battery cell respectively according to the voltage data of the battery cell;

[0008] Determining a deviation value between the voltage curve of the battery cell and the median voltage curve according to the voltage curve of the battery cell and the median voltage curve;

[0009] For any of the battery cells, obtaining an average normalized value of the deviation value of the battery cell and an average of the standard scores of the deviation value as characteristic data of the battery cell;

[0010] Determining a local outlier factor of the battery cell according to the characteristic data of the battery cell;

[0011] When the local outlier factor of the battery cell is less than a preset value, it is determined that the battery cell has not failed.

[0012] Optionally, determining the deviation value between the voltage curve of the battery cell and the median voltage curve according to the voltage curve of the battery cell and the median voltage curve comprises:

[0013] Acquire a distance between voltage data in the voltage curve and median voltage data in the median voltage curve;

[0014] Determining a minimum distance according to a distance between the voltage data and the median voltage data;

[0015] The minimum distance is taken as a deviation value between the voltage curve and the median voltage curve.

[0016] Optionally, obtaining an average normalized value of the deviation values ​​of the battery cells includes:

[0017] According to all the deviation values, respectively determine the maximum value, the minimum value and the average value of the deviation values;

[0018] An average normalized value of the deviation value is determined according to the deviation value, the maximum value of the deviation value, the minimum value of the deviation value, and the average value of the deviation value.

[0019] Optionally, obtaining the mean of the standard scores of the deviation values ​​of the battery cells includes:

[0020] Obtaining a standard score for the deviation value;

[0021] The mean of the standard scores is determined based on the standard scores of all the deviation values.

[0022] Optionally, obtaining the mean of the standard scores of the deviation values ​​of the battery cells includes:

[0023] Obtaining the number of the deviation values;

[0024] Determine a standard deviation of the deviation value according to an average value of the deviation value, the deviation value, and the number of the deviation values;

[0025] Determining a standard score of the deviation value according to the deviation value, an average value of the deviation value, and a standard deviation of the deviation value;

[0026] The mean of the standard scores is determined based on the standard scores of all the deviation values.

[0027] Optionally, determining the local outlier factor of the battery cell according to the characteristic data of the battery cell includes:

[0028] Acquire the local reachable density of the feature data and the preset distance neighborhood of the feature data, wherein the preset distance neighborhood of the feature data is a set of all the feature data within the preset distance of the feature data;

[0029] A local outlier factor is determined according to the local reachable density of the feature data, the local reachable density of other feature data except the feature data, and a preset distance neighborhood of the feature data.

[0030] Optionally, the obtaining the local reachable density of the feature data includes:

[0031] Acquire the distance between the feature data and other feature data except the feature data and the preset distance of the feature data, wherein the preset distance of the feature data is the distance far from the feature data in the preset sorting position;

[0032] Determining a preset reachable distance of the feature data according to a distance between the feature data and other feature data other than the feature data and a preset distance of the feature data;

[0033] The local reachable density of the feature data is determined according to the preset reachable distance of the feature data and the preset distance neighborhood of the feature data.

[0034] In a second aspect, an embodiment of the present application provides a battery fault detection device, the device comprising:

[0035] A voltage data acquisition module, used to acquire voltage data of the battery cell;

[0036] A voltage curve determination module, used to determine the voltage curve and the median voltage curve of the battery cell according to the voltage data of the battery cell;

[0037] a deviation data determination module, configured to determine a deviation value between the voltage curve of the battery cell and the median voltage curve according to the voltage curve of the battery cell and the median voltage curve;

[0038] A battery cell characteristic data acquisition module, used for acquiring, for any of the battery cells, an average normalized value of the deviation value of the battery cell and an average of the standard scores of the deviation value as characteristic data of the battery cell;

[0039] A local outlier factor determination module, configured to determine a local outlier factor of the battery cell according to the characteristic data of the battery cell;

[0040] The battery fault detection module is used to determine that the battery cell has not failed when the local outlier factor of the battery cell is less than a preset value.

[0041] Optionally, the deviation data determination module includes:

[0042] A distance data acquisition submodule, used to acquire the distance between the voltage data in the voltage curve and the median voltage data in the median voltage curve;

[0043] A minimum distance determination submodule, used to determine a minimum distance according to the distance between the voltage data and the median voltage data;

[0044] The deviation data determination submodule is used for taking the minimum distance as the deviation value between the voltage curve and the median voltage curve.

[0045] Optionally, the battery cell characteristic data acquisition module includes:

[0046] A deviation value calculation submodule, used to determine the maximum value, the minimum value and the average value of the deviation values ​​according to all the deviation values;

[0047] The average normalization operator module is used to determine the average normalized value of the deviation value according to the deviation value, the maximum value of the deviation value, the minimum value of the deviation value and the average value of the deviation value.

[0048] Optionally, the battery cell characteristic data acquisition module includes:

[0049] A standard score acquisition submodule, used to acquire the standard score of the deviation value;

[0050] The first standard score mean determination submodule is used to determine the mean of the standard scores according to the standard scores of all the deviation values.

[0051] Optionally, the battery cell characteristic data acquisition module includes:

[0052] A deviation value quantity acquisition submodule is used to acquire the quantity of the deviation values;

[0053] A deviation value standard deviation determination submodule, used to determine the standard deviation of the deviation value according to the average value of the deviation value, the deviation value and the number of the deviation values;

[0054] A deviation value standard score determination submodule, used to determine the standard score of the deviation value according to the deviation value, the average value of the deviation value and the standard deviation of the deviation value;

[0055] The second standard score mean determination submodule is used to determine the mean of the standard scores according to the standard scores of all the deviation values.

[0056] Optionally, the local outlier factor determination module includes:

[0057] A local reachable density acquisition submodule, used to acquire the local reachable density of the feature data and a preset distance neighborhood of the feature data, wherein the preset distance neighborhood of the feature data is a set of all the feature data within a preset distance of the feature data;

[0058] The local outlier factor determination submodule is used to determine the local outlier factor according to the local reachable density of the feature data, the local reachable density of other feature data except the feature data, and the preset distance neighborhood of the feature data.

[0059] Optionally, the local reachable density acquisition submodule includes:

[0060] a distance acquisition unit, used for acquiring the distance between the feature data and other feature data except the feature data and the preset distance of the feature data, wherein the preset distance of the feature data is the distance far from the feature data in the preset sorting position;

[0061] a reachable distance acquisition unit, configured to determine a preset reachable distance of the feature data according to a distance between the feature data and other feature data other than the feature data and a preset distance of the feature data;

[0062] The local reachable density determining unit is used to determine the local reachable density of the feature data according to the preset reachable distance of the feature data and the preset distance neighborhood of the feature data.

[0063] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor, wherein the processor is configured to execute the instructions to implement any of the battery fault detection methods described above.

[0064] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute any of the battery fault detection methods described above.

[0065] In the embodiment of the present application, the voltage data of the battery cell is obtained; the voltage curve and the median voltage curve of the battery cell are determined according to the voltage data of the battery cell; the deviation value between the voltage curve of the battery cell and the median voltage curve is determined according to the voltage curve and the median voltage curve of the battery cell; for any battery cell, the average normalized value of the deviation value of the battery cell and the average value of the standard score of the deviation value are obtained as the characteristic data of the battery cell; the local outlier factor of the battery cell is determined according to the characteristic data of the battery cell; when the local outlier factor of the battery cell is less than a preset value, it is determined that the battery cell has not failed. The present application realizes battery fault detection by combining the standard score related to the battery voltage and the average normalized value. Compared with the prior art that realizes battery fault detection only by the standard score related to the battery voltage, it can make fuller use of the complete information related to the battery voltage, thereby improving the accuracy of battery fault detection. In addition, the present application utilizes a local outlier factor algorithm to obtain a local outlier factor of characteristic data of a battery cell, and determines whether a battery cell has a fault based on the local outlier factor of the characteristic data of the battery cell. By combining the local outlier factor algorithm, the battery cell can be quickly detected for faults, thereby improving the efficiency of battery fault detection.

[0066] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0068] Figure 1 This is one of the step flow charts of a battery fault detection method provided in an embodiment of the present application;

[0069] Figure 2 This is the second step flow chart of a battery fault detection method provided in an embodiment of the present application;

[0070] Figure 3 This is the third step flow chart of a battery fault detection method provided in an embodiment of the present application;

[0071] Figure 4 This is a fourth step flow chart of a battery fault detection method provided in an embodiment of the present application;

[0072] Figure 5 This is the fifth step flow chart of a battery fault detection method provided in an embodiment of the present application;

[0073] Figure 6 This is the sixth step flow chart of a battery fault detection method provided in an embodiment of the present application;

[0074] Figure 7 This is the seventh step flow chart of a battery fault detection method provided in an embodiment of the present application;

[0075] Figure 8 is a device block diagram of a battery fault detection device provided in an embodiment of the present application;

[0076] Fig. 9 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0077] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0078] The terms "first", "second", etc. in the specification and claims of the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0079] In conjunction with the accompanying drawings, a battery fault detection method, device, storage medium and vehicle provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0080] Figure 1 is a flowchart of a battery fault detection method provided by an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0081] Step 101, obtaining voltage data of a battery cell.

[0082] It should be noted that, in the embodiment of the present application, the battery on the vehicle is composed of a number of battery cells. In order to accurately locate the faulty battery cell, it is first necessary to obtain the voltage data of each battery cell in the battery.

[0083] Optionally, after the voltage data of all battery cells are acquired, data cleaning may be performed on the acquired voltage data of all battery cells, including abnormal value processing, missing value processing, outlier processing, etc.

[0084] Step 102 : determining the voltage curve and the median voltage curve of the battery cell according to the voltage data of the battery cell.

[0085] It should be noted that in the embodiment of the present application, the moving window is a data processing technology that allows a fixed-size window to be slid in the data. Each time the window moves, the data in the window is processed, and the size of the moving window determines the amount of data processed each time.

[0086] After obtaining the voltage data of all battery cells, the number of all the voltage data obtained can be counted, and further, the size of the moving window can be determined according to the number of all the voltage data obtained, wherein the determined moving window size must ensure that the amount of data in the moving window is sufficient for effective calculation and analysis, while also avoiding calculation delays or excessive resource consumption caused by the moving window being too large.

[0087] Based on the determined size of the moving window, a voltage curve of the battery cell is determined according to the voltage data of the battery cell in the current moving window, and a median voltage curve of the battery cell is determined according to the voltage data of the battery cell in the current moving window.

[0088] The voltage curve of the battery cell is determined according to the voltage data of the battery cell in the current moving window. Specifically, for the voltage data of any battery cell, the voltage data of the battery cell is plotted into a voltage curve to represent the voltage change of the battery cell in the current moving window.

[0089] The median voltage curve of the battery cell is determined according to the voltage data of the battery cell in the current moving window. Specifically, for the voltage data of any battery cell, the median voltage data of the battery cell is determined from all the voltage data of the battery cell; the median voltage data of all the battery cells are plotted into a median voltage curve.

[0090] Step 103 : determining a deviation value between the voltage curve of the battery cell and the median voltage curve according to the voltage curve of the battery cell and the median voltage curve.

[0091] It should be noted that, in the embodiment of the present application, for the voltage curve of any battery cell, the deviation value between the voltage curve of the battery cell and the median voltage curve is determined according to the voltage curve of the battery cell and the median voltage curve.

[0092] Step 104 : for any battery cell, obtain the average normalized value of the deviation value of the battery cell and the average of the standard scores of the deviation value as characteristic data of the battery cell.

[0093] It should be noted that in the embodiment of the present application, for any battery cell, according to the deviation value between the voltage curve of the battery cell and the median voltage curve, the average normalized value of the deviation value of the battery cell and the average of the standard fraction of the deviation value are obtained, and the average normalized value of the deviation value and the average of the standard fraction of the deviation value are used as the characteristic data of the battery cell. In other words, any battery cell corresponds to a set of characteristic data, wherein a set of characteristic data includes the average normalized value of the deviation value of the battery cell and the average of the standard fraction of the deviation value of the battery cell.

[0094] Step 105 : determining the local outlier factor of the battery cell according to the characteristic data of the battery cell.

[0095] It should be noted that, in the embodiment of the present application, for any battery cell, a series of operations are performed on the characteristic data of the battery cell using a LOF (Local Outlier Factor) algorithm to obtain the local outlier factor of the battery cell.

[0096] Step 106 : When the local outlier factor of the battery cell is less than a preset value, determine that the battery cell has not failed.

[0097] It should be noted that in the embodiment of the present application, the preset value may be 1. For any battery cell, when the local outlier factor of the battery cell is obtained, it is determined whether the local outlier factor of the battery cell is less than 1. If the local outlier factor of the battery cell is less than 1, it means that the greater the density of the battery cell, the more normal the battery cell is, and it is determined that the battery cell has not failed. If the local outlier factor of the battery cell is greater than 1, it means that the smaller the density of the battery cell, the more abnormal the battery cell is, and it is determined that the battery cell has failed.

[0098] The present application realizes battery fault detection by combining the standard score related to the battery voltage and the average normalized value. Compared with the prior art that only realizes battery fault detection by using the standard score related to the battery voltage, the present application can make full use of the complete information related to the battery voltage, thereby improving the accuracy of battery fault detection. In addition, the present application uses the local outlier factor algorithm to obtain the local outlier factor of the characteristic data of the battery cell, and judges whether the battery cell has a fault according to the local outlier factor of the characteristic data of the battery cell. By combining the local outlier factor algorithm, the battery cell can be quickly detected for faults, thereby improving the efficiency of battery fault detection.

[0099] Further, in the embodiments of the present application, Figure 2 As shown, step 103 may further include the following steps:

[0100] Step 201 : obtaining the distance between voltage data in the voltage curve and median voltage data in the median voltage curve.

[0101] It should be noted that, in the embodiment of the present application, for the voltage curve of any battery cell, the distance between the voltage data in the voltage curve and the median voltage data in the median voltage curve can be obtained according to formula (1).

[0102]

[0103] Among them, d(x i ,y i ) is the voltage data x in the voltage curve i and the median voltage data y in the median voltage curve i The distance between i is the voltage data in the voltage curve, y i is the median voltage data in the median voltage curve.

[0104] Step 202: Determine a minimum distance according to the distance between the voltage data and the median voltage data.

[0105] Step 203: taking the minimum distance as the deviation value between the voltage curve and the median voltage curve.

[0106] After obtaining the distance between the voltage data in the voltage curve and the median voltage data in the median voltage curve, the minimum distance between the voltage curve and the median voltage curve can be determined according to formula (2).

[0107] S(i,j)=d(x i ,y i )+min(S(i-1,j),S(i,j-1),S(i-1,j-1)) Formula (2)

[0108] Among them, S(i,j) is the distance between the i-th voltage data of the voltage curve and the j-th median voltage data of the median voltage curve, that is, the minimum distance between the voltage curve and the median voltage curve, d(x i ,y i ) is the voltage data x in the voltage curve i and the median voltage data y in the median voltage curve i S(i-1,j) is the distance between the i-1th voltage data of the voltage curve and the j-1th median voltage data of the median voltage curve, S(i,j-1) is the distance between the i-1th voltage data of the voltage curve and the j-1th median voltage data of the median voltage curve, and S(i-1,j-1) is the distance between the i-1th voltage data of the voltage curve and the j-1th median voltage data of the median voltage curve.

[0109] For the voltage curve of any battery cell, after determining the minimum distance between the voltage curve and the median voltage curve, the minimum distance can be used as the deviation value between the voltage curve and the median voltage curve, wherein the smaller the deviation value is, the higher the similarity between the voltage curve and the median voltage curve is; the larger the deviation value is, the lower the similarity between the voltage curve and the median voltage curve is.

[0110] This application calculates the minimum distance between the voltage curve and the median voltage curve and uses it as the deviation value between the voltage curve and the median voltage curve. This can not only more comprehensively evaluate the health status of the battery cells, but also detect faults in real time during battery operation, discover potential problems in a timely manner, and ensure the normal use of the battery and the safe and stable operation of related equipment.

[0111] Further, in the embodiments of the present application, Figure 3 As shown, step 104 may further include the following steps:

[0112] Step 301, determining the maximum value, the minimum value and the average value of the deviation values ​​according to all the deviation values.

[0113] Step 302: Determine an average normalized value of the deviation value according to the deviation value, the maximum value of the deviation value, the minimum value of the deviation value, and the average value of the deviation value.

[0114] It should be noted that in the embodiment of the present application, the deviation values ​​between the voltage curves of all battery cells and the median voltage curve are sorted in order from large to small to obtain a sorting result. The deviation value with the largest value in the sorting result is taken as the maximum deviation value, and the deviation value with the smallest value in the sorting result is taken as the minimum deviation value.

[0115] The average value of the deviation values ​​is determined according to formula (3).

[0116]

[0117] in, is the average value of the deviation, N is the number of deviations, S i is the deviation between the voltage curve of the ith battery cell and the median voltage curve.

[0118] Furthermore, the average normalized value of the deviation value is determined according to formula (4).

[0119]

[0120] Among them, X” is the average normalized value of the deviation value, S i is the deviation between the voltage curve of the ith battery cell and the median voltage curve, is the average value of the deviation, S max is the maximum value of the deviation, S min is the minimum value of the deviation.

[0121] The present application determines the average normalized value of the deviation value based on the deviation value, the maximum value of the deviation value, the minimum value of the deviation value and the average value of the deviation value, thereby providing a more comprehensive data basis for subsequent battery fault detection, thereby improving the accuracy of battery fault detection.

[0122] Further, in the embodiments of the present application, Figure 4 As shown, step 104 may further include the following steps:

[0123] Step 401, obtaining the standard score of the deviation value.

[0124] Step 402, determining the mean of the standard scores according to the standard scores of all deviation values.

[0125] It should be noted that in the embodiment of the present application, for the deviation value between the voltage curve of any battery cell and the median voltage curve, the standard score of the deviation value is obtained according to steps 501-503. After the standard scores of the deviation values ​​of all battery cells are obtained, the average value is calculated according to the standard scores of the deviation values ​​of all battery cells and the number of standard scores of the deviation values, and the obtained average value is the mean of the standard scores.

[0126] The present application obtains the standard score of the deviation value and determines the mean of the standard score according to the standard scores of all the deviation values, thereby providing a more comprehensive data basis for subsequent battery fault detection, thereby improving the accuracy of battery fault detection.

[0127] Further, in the embodiments of the present application, Figure 5As shown, step 104 may further include the following steps:

[0128] Step 501, obtaining the number of deviation values.

[0129] Step 502, determining the standard deviation of the deviation values ​​according to the average value of the deviation values, the deviation values, and the number of the deviation values.

[0130] Step 503, determining a standard score of the deviation value according to the deviation value, the average value of the deviation value and the standard deviation of the deviation value.

[0131] Step 504, determining the mean of the standard scores according to the standard scores of all deviation values.

[0132] It should be noted that, in the embodiment of the present application, the specific implementation process of obtaining the standard score of the deviation value is: obtaining the number of deviation values ​​based on all deviation values; determining the standard deviation of the deviation value according to the following formula (5); and determining the standard score of the deviation value according to the following formula (6).

[0133]

[0134] Where σ is the standard score of the deviation value, N is the number of deviation values, S i is the deviation between the voltage curve of the ith battery cell and the median voltage curve, is the average value of the deviations.

[0135]

[0136] Where X' is the standard score of the deviation value, S i is the deviation between the voltage curve of the ith battery cell and the median voltage curve, is the mean value of the deviation, and σ is the standard score of the deviation.

[0137] After the standard scores of the deviation values ​​of all battery cells are obtained, an average value is calculated according to the standard scores of the deviation values ​​of all battery cells and the number of standard scores of the deviation values, and the obtained average value is the mean of the standard scores.

[0138] The present application determines the standard score of the deviation value according to the deviation value, the average value of the deviation value and the standard deviation of the deviation value, and then determines the mean value of the standard score according to the standard score, so as to further detect the fault status of the battery cell more comprehensively.

[0139] Further, in the embodiments of the present application, Figure 6 As shown, step 105 may also include the following steps:

[0140] Step 601 : obtaining the local reachable density of the feature data and the preset distance neighborhood of the feature data, wherein the preset distance neighborhood of the feature data is a set of all feature data within a preset distance of the feature data.

[0141] It should be noted that in the embodiment of the present application, a preset distance neighborhood is also called the kth distance neighborhood, wherein the kth distance neighborhood refers to the set of all feature data within the kth distance of the feature data, including the feature data at the kth distance.

[0142] For the characteristic data of any battery cell, the local reachable density of the characteristic data of the battery cell, the k-th distance neighborhood of the characteristic data of the battery cell, and the local reachable density of the characteristic data of other battery cells except the characteristic data of the battery cell are obtained.

[0143] Step 602: Determine a local outlier factor according to the local reachable density of the feature data, the local reachable density of other feature data except the feature data, and a preset distance neighborhood of the feature data.

[0144] It should be noted that in the embodiment of the present application, after obtaining the local reachable density of the characteristic data of the battery cell, the k-th distance neighborhood of the characteristic data of the battery cell, and the local reachable density of the characteristic data of other battery cells except the characteristic data of the battery cell, the local outlier factor of the characteristic data of the battery cell can be determined according to formula (7).

[0145]

[0146] Among them, LOF k (p) is the local outlier factor of the characteristic data of the battery cell, N k (p) is the kth distance neighborhood of the characteristic data of the battery cell, lrd k (o) is the local reachable density of the characteristic data of other battery cells except the characteristic data of the battery cell, lrd k (p) is the local reachable density of the characteristic data of the battery cell.

[0147] The present application determines the local outlier factor based on the local reachable density of the feature data, the local reachable density of other feature data except the feature data, and the preset distance neighborhood of the feature data, that is, uses the local outlier factor algorithm to obtain the local outlier factor of the feature data of the battery cell, thereby realizing the judgment of whether the battery cell has a fault based on the local outlier factor of the feature data of the battery cell. By combining the local outlier factor algorithm, the battery cell fault detection can be quickly carried out, thereby improving the efficiency of battery fault detection.

[0148] Further, in the embodiments of the present application, Figure 7As shown, step 601 may also include the following steps:

[0149] Step 701 : obtaining the distance between the feature data and other feature data except the feature data and the preset distance of the feature data, wherein the preset distance of the feature data is the distance far from the feature data in the preset sorting position.

[0150] It should be noted that, in the embodiment of the present application, the preset distance, that is, the Kth distance, refers to the distance value that is k-th farthest from the characteristic data of the battery cell, that is, the distance between the characteristic data of the battery cell and the kth neighbor.

[0151] For the characteristic data of any battery cell, if the characteristic data of the battery cell is represented by p and the characteristic data other than the characteristic data of the battery cell is represented by o, then the Euclidean distance between the characteristic data of the battery cell and the characteristic data other than the characteristic data of the battery cell is calculated, that is, the distance, represented by dist(p,o).

[0152] At the same time, the Kth distance of the characteristic data of the battery cell is also required. The Kth distance of the characteristic data of the battery cell can be obtained by dist k (p) indicates.

[0153] Step 702: Determine a preset reachable distance of the feature data according to the distance between the feature data and other feature data other than the feature data and the preset distance of the feature data.

[0154] It should be noted that in the embodiment of the present application, the preset reachable distance, that is, the Kth reachable distance, is defined as the larger of the kth distance of the characteristic data of the battery cell and the distance between the characteristic data of the battery cell and other characteristic data except the characteristic data of the battery cell.

[0155] After obtaining the distance between the characteristic data of the battery cell and other characteristic data except the characteristic data of the battery cell and the Kth distance of the characteristic data of the battery cell, the preset reachable distance of the characteristic data can be determined according to formula (8).

[0156] reach_dist k (p,,o)=max{dist k (p), dist(p,o)} Formula (8)

[0157] Among them, reach_dist k (p,o) is the Kth reachable distance of the characteristic data of the battery cell, dist k(p) is the Kth distance of the characteristic data of the battery cell, and dist(p,o) is the distance between the characteristic data of the battery cell and other characteristic data except the characteristic data of the battery cell.

[0158] Step 703: determining the local reachability density of the feature data according to the preset reachable distance of the feature data and the preset distance neighborhood of the feature data.

[0159] It should be noted that, in the embodiment of the present application, specifically, the local reachable density of the characteristic data of the battery cell can be determined according to formula (9).

[0160]

[0161] Among them, lrd k (p) is the local reachable density of the characteristic data of the battery cell, N k (p) is the kth distance neighborhood of the characteristic data of the battery cell, reach_dist k (p,o) is the Kth reachable distance of the characteristic data of the battery cell.

[0162] The present application determines the local reachable density of feature data according to the preset reachable distance of the feature data and the preset distance neighborhood of the feature data, thereby providing a data basis for the subsequent acquisition of the local outlier factor. The local outlier factor can further be combined to determine whether a battery cell has a fault, thereby improving the efficiency of battery fault detection.

[0163] Corresponding to the method provided in the above-mentioned battery fault detection method embodiment of the present application, see Figure 8 The present application also provides a device block diagram of a battery fault detection device. In this embodiment, the device includes:

[0164] The voltage data acquisition module 801 is used to acquire the voltage data of the battery cell;

[0165] A voltage curve determination module 802 is used to determine the voltage curve and the median voltage curve of the battery cell according to the voltage data of the battery cell;

[0166] The deviation data determination module 803 is used to determine the deviation value between the voltage curve of the battery cell and the median voltage curve according to the voltage curve of the battery cell and the median voltage curve;

[0167] A battery cell characteristic data acquisition module 804 is used to acquire, for any battery cell, an average normalized value of the deviation value of the battery cell and an average value of the standard score of the deviation value as characteristic data of the battery cell;

[0168] A local outlier factor determination module 805, configured to determine a local outlier factor of a battery cell according to characteristic data of the battery cell;

[0169] The battery fault detection module 806 is used to determine that the battery cell has not failed when the local outlier factor of the battery cell is less than a preset value.

[0170] Optionally, the deviation data determination module includes:

[0171] A distance data acquisition submodule, used to acquire the distance between the voltage data in the voltage curve and the median voltage data in the median voltage curve;

[0172] A minimum distance determination submodule, used to determine the minimum distance according to the distance between the voltage data and the median voltage data;

[0173] The deviation data determination submodule is used for the minimum distance as the deviation value between the voltage curve and the median voltage curve.

[0174] Optionally, the battery cell characteristic data acquisition module includes:

[0175] The deviation value calculation submodule is used to determine the maximum value, the minimum value and the average value of the deviation values ​​according to all the deviation values;

[0176] The average normalization operator module is used to determine an average normalized value of the deviation value according to the deviation value, the maximum value of the deviation value, the minimum value of the deviation value and the average value of the deviation value.

[0177] Optionally, the battery cell characteristic data acquisition module includes:

[0178] A standard score acquisition submodule is used to obtain the standard score of the deviation value;

[0179] The first standard score mean determination submodule is used to determine the mean of the standard scores according to the standard scores of all deviation values.

[0180] Optionally, the battery cell characteristic data acquisition module includes:

[0181] The deviation value quantity acquisition submodule is used to obtain the quantity of deviation values;

[0182] A deviation value standard deviation determination submodule is used to determine the standard deviation of the deviation value according to the average value of the deviation value, the deviation value and the number of the deviation values;

[0183] A deviation value standard score determination submodule is used to determine the standard score of the deviation value according to the deviation value, the average value of the deviation value and the standard deviation of the deviation value;

[0184] The second standard score mean determination submodule is used to determine the mean of the standard scores according to the standard scores of all the deviation values.

[0185] Optionally, the local outlier factor determination module includes:

[0186] A local reachable density acquisition submodule, used to acquire the local reachable density of feature data and a preset distance neighborhood of the feature data, wherein the preset distance neighborhood of the feature data is a set of all feature data within a preset distance of the feature data;

[0187] The local outlier factor determination submodule is used to determine the local outlier factor according to the local reachable density of the feature data, the local reachable density of other feature data except the feature data, and the preset distance neighborhood of the feature data.

[0188] Optionally, the local reachable density acquisition submodule includes:

[0189] A distance acquisition unit, used to acquire the distance between the feature data and other feature data other than the feature data and the preset distance of the feature data, wherein the preset distance of the feature data is the distance far from the feature data in the preset sorting position;

[0190] A reachable distance acquisition unit, used to determine a preset reachable distance of the feature data according to a distance between the feature data and other feature data other than the feature data and a preset distance of the feature data;

[0191] The local reachable density determining unit is used to determine the local reachable density of the feature data according to a preset reachable distance of the feature data and a preset distance neighborhood of the feature data.

[0192] Fig. 9 This is a structural diagram of an electronic device M00 provided in an embodiment of the present application. In the figure, the electronic device M00 includes a processor M01 and a memory M02. The memory M02 stores programs or instructions that can be run on the processor M01. When the program or instruction is executed by the processor M01, the various steps of the above-mentioned battery fault detection method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0193] In an embodiment of the present application, the memory M02 may be used to store software programs and various data. The memory M02 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory M02 may include a volatile memory or a non-volatile memory, or the memory M02 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory M02 in the embodiment of the present application includes but is not limited to these and any other suitable types of memories.

[0194] The processor M01 may include one or more processing units; optionally, the processor M01 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor M01.

[0195] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned battery fault detection method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0196] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0197] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned battery fault detection method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0198] It should be understood that the chip involved in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0199] An embodiment of the present application provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned battery fault detection method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0200] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A battery failure detection method, characterized in that: Applied to a battery, wherein the battery comprises a plurality of battery cells, the method comprising: Acquiring voltage data of the battery cell; Determining the voltage curve and the median voltage curve of the battery cell respectively according to the voltage data of the battery cell; Determining a deviation value between the voltage curve of the battery cell and the median voltage curve according to the voltage curve of the battery cell and the median voltage curve; For any of the battery cells, obtaining an average normalized value of the deviation value of the battery cell and an average of the standard scores of the deviation value as characteristic data of the battery cell; Determining a local outlier factor of the battery cell according to the characteristic data of the battery cell; When the local outlier factor of the battery cell is less than a preset value, it is determined that the battery cell has not failed.

2. The method according to claim 1, characterized in that Determining the deviation value between the voltage curve of the battery cell and the median voltage curve according to the voltage curve of the battery cell and the median voltage curve comprises: Acquire a distance between voltage data in the voltage curve and median voltage data in the median voltage curve; Determining a minimum distance according to a distance between the voltage data and the median voltage data; The minimum distance is taken as a deviation value between the voltage curve and the median voltage curve.

3. The method according to claim 1, characterized in that The step of obtaining the average normalized value of the deviation value of the battery cell comprises: According to all the deviation values, respectively determine the maximum value, the minimum value and the average value of the deviation values; An average normalized value of the deviation value is determined according to the deviation value, the maximum value of the deviation value, the minimum value of the deviation value, and the average value of the deviation value.

4. The method according to claim 1, characterized in that: The step of obtaining the mean of the standard scores of the deviation values ​​of the battery cells comprises: Obtaining a standard score for the deviation value; The mean of the standard scores is determined based on the standard scores of all the deviation values.

5. The method according to claim 3, characterized in that: The step of obtaining the mean of the standard scores of the deviation values ​​of the battery cells comprises: Obtaining the number of the deviation values; Determine a standard deviation of the deviation value according to an average value of the deviation value, the deviation value, and the number of the deviation values; Determining a standard score of the deviation value according to the deviation value, an average value of the deviation value, and a standard deviation of the deviation value; The mean of the standard scores is determined based on the standard scores of all the deviation values.

6. The method according to claim 1, characterized in that The determining the local outlier factor of the battery cell according to the characteristic data of the battery cell comprises: Acquire the local reachable density of the feature data and the preset distance neighborhood of the feature data, wherein the preset distance neighborhood of the feature data is a set of all the feature data within the preset distance of the feature data; A local outlier factor is determined according to the local reachable density of the feature data, the local reachable density of other feature data except the feature data, and a preset distance neighborhood of the feature data.

7. The method according to claim 6, characterized in that The obtaining of the local reachable density of the feature data comprises: Acquire the distance between the feature data and other feature data except the feature data and the preset distance of the feature data, wherein the preset distance of the feature data is the distance far from the feature data in the preset sorting position; Determining a preset reachable distance of the feature data according to a distance between the feature data and other feature data other than the feature data and a preset distance of the feature data; The local reachable density of the feature data is determined according to the preset reachable distance of the feature data and the preset distance neighborhood of the feature data.

8. A battery fault detection device, characterized in that: The device comprises: A voltage data acquisition module, used to acquire voltage data of the battery cell; A voltage curve determination module, used to determine the voltage curve and the median voltage curve of the battery cell according to the voltage data of the battery cell; a deviation data determination module, configured to determine a deviation value between the voltage curve of the battery cell and the median voltage curve according to the voltage curve of the battery cell and the median voltage curve; A battery cell characteristic data acquisition module, used for acquiring, for any of the battery cells, an average normalized value of the deviation value of the battery cell and an average of the standard scores of the deviation value as characteristic data of the battery cell; A local outlier factor determination module, configured to determine a local outlier factor of the battery cell according to the characteristic data of the battery cell; The battery fault detection module is used to determine that the battery cell has not failed when the local outlier factor of the battery cell is less than a preset value.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the battery fault detection method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the battery failure detection method according to any one of claims 1 to 7.

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