Battery fault identification method, device, equipment, medium and program

Abstract

The invention discloses a battery fault identification method, device and equipment, a medium and a program. The method comprises the following steps: acquiring vehicle state data and voltage data of each single battery in a battery pack according to a set period during the running period of a vehicle; according to the voltage data obtained corresponding to each battery monomer in each set period, determining an overall score of the corresponding battery monomer during the vehicle operation period; and determining an abnormal battery cell according to the total score of each battery cell during the operation of the vehicle. According to the technical scheme, comprehensive analysis of all the battery monomers in the battery pack is realized, and the uncertainty of the voltage data of each battery monomer and the importance of the voltage data in the whole data are fully considered, so that the problems of poor reliability and accuracy of identifying the consistency change of the power battery monomers and the abnormal battery monomers in the prior art are solved, and the reliability and accuracy of identifying the abnormal battery monomers are improved. And the reliability and the accuracy of battery fault identification are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery fault detection, and in particular to a battery fault identification method, device, equipment, medium and program. Background Art

[0002] With the widespread adoption of new energy vehicles, the health of power batteries plays a crucial role in vehicle performance and safety. Battery cell consistency is a key factor in determining battery health. Therefore, identifying abnormal battery cells by identifying changes in their consistency is a common method for identifying battery faults.

[0003] Existing methods for identifying abnormal cells based on the consistency of power battery cells can only analyze a single parameter of a single cell. For example, by analyzing the cells with the highest and lowest voltages in the battery pack, the authors can determine whether the cell with the highest or lowest voltage is an abnormal cell.

[0004] However, the above method can only analyze some battery cells and cannot fully capture the consistency change process of each battery cell in the battery pack. It does not fully consider the integrity and inherent correlation of all battery cell data in the battery pack, resulting in poor reliability and accuracy in identifying consistency changes and abnormal battery cells in power battery cells. Summary of the Invention

[0005] The present invention provides a battery fault identification method, device, equipment, medium and program to achieve comprehensive analysis of all battery cells in a battery pack, fully considering the uncertainty of the voltage data of each battery cell and its importance in the overall data, so as to solve the problem of poor reliability and accuracy in identifying consistency changes and abnormal battery cells in the prior art, and improve the reliability and accuracy of battery fault identification.

[0006] According to one aspect of the present invention, a battery fault identification method is provided, characterized by comprising:

[0007] During vehicle operation, vehicle status data and voltage data of each battery cell in the battery pack are obtained according to a set period;

[0008] determining, based on the voltage data corresponding to each battery cell acquired during each set period, an overall score of the corresponding battery cell during operation of the vehicle;

[0009] An abnormal battery cell is determined according to a total score value of each of the battery cells during operation of the vehicle.

[0010] According to a second aspect of the present invention, a battery fault identification device is provided, characterized in that it includes:

[0011] An acquisition module is used to acquire vehicle status data and voltage data of each battery cell in the battery pack according to a set period during vehicle operation;

[0012] A first determination module is configured to determine an overall score of the corresponding battery cell during operation of the vehicle based on the voltage data obtained for each battery cell in each set period;

[0013] The second determining module is configured to determine an abnormal battery cell according to a total score value of each battery cell during operation of the vehicle.

[0014] According to a third aspect of the present invention, there is provided an electronic device, comprising:

[0015] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the battery fault identification method described in any embodiment of the present invention.

[0016] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the battery fault identification method according to any embodiment of the present invention when executed.

[0017] According to a fifth aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the battery fault identification method according to any embodiment of the present invention is implemented.

[0018] The technical solution of the embodiment of the present invention obtains vehicle status data and voltage data of each battery cell in the battery pack according to a set period during vehicle operation, and determines the overall score of the corresponding battery cell during the vehicle operation based on the voltage data corresponding to each battery cell obtained in each set period, and determines the abnormal battery cell based on the total score value of each battery cell during the vehicle operation. During the operation of the vehicle, a comprehensive analysis can be performed on all battery cells in the battery pack, fully considering the uncertainty of the voltage data of each battery cell and its importance in the overall data, thereby solving the problem of poor reliability and accuracy in identifying consistency changes of power battery cells and abnormal battery cells in the prior art, and improving the reliability and accuracy of battery fault identification.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flowchart of a battery fault identification method provided according to the first embodiment of the present invention;

[0022] Figure 2 This is a flowchart of a battery fault identification method provided according to the second embodiment of the present invention;

[0023] Figure 3 This is a schematic structural diagram of a battery fault identification device provided according to a third embodiment of the present invention;

[0024] Figure 4 It is a structural diagram of an electronic device for implementing the battery fault identification method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Example 1

[0028] Figure 1 A flowchart of a battery fault identification method is provided for the first embodiment of the present invention. This embodiment is applicable to identifying abnormal battery cells in a vehicle's battery pack. The method can be executed by a battery fault identification device, which can be implemented in the form of hardware and / or software and can be configured in the vehicle's control system. Figure 1 As shown, the method includes:

[0029] S101 . During vehicle operation, obtain vehicle status data and voltage data of each battery cell in a battery pack according to a set period.

[0030] Among them, the vehicle operation period may refer to the time period from when the vehicle is started to when the vehicle is turned off. The set period may be a pre-set period for collecting vehicle data and data of each battery cell in the battery pack. The vehicle status data may be various status data of the vehicle during vehicle operation. For example, it may include the vehicle VIN code, GPS timestamp, single cell consistency fault alarm mark, daily mileage and speed record. The battery pack may be composed of multiple battery cells and may be used to provide power for the vehicle. The voltage data of each battery cell in the battery pack may be used to judge the consistency of each battery cell in the battery pack, and may include the maximum single cell voltage, the minimum single cell voltage, the highest single cell voltage single cell number, the lowest single cell voltage single cell number, the battery SOC (state of charge) and the single cell voltage list. It should be noted that a database for storing various vehicle-related data is preset inside the vehicle.

[0031] Exemplarily, the battery fault identification device can preliminarily filter out all vehicle status data during vehicle operation and the voltage data of each battery cell in the battery pack from the vehicle's built-in database. Then, based on a pre-set data collection cycle, the vehicle status data and the voltage data of each battery cell at the corresponding data collection cycle can be filtered. Finally, the filtered data can be further cleaned and eliminated to remove abnormal data recorded in the database, thereby ensuring the accuracy of the data. At the same time, the missing data in the filtered data can also be filled in so that valid vehicle status data and battery cell voltage data exist at each cycle moment to ensure data integrity. For example, when the voltage data of a single battery contains 0, invalid values, or null values, the abnormal values are differenced according to the linear difference method, or when the voltage data of a certain day are all 0, invalid values, or null values, the data for the entire day is removed. This embodiment does not specifically limit the specific methods of cleaning, eliminating, and filling data.

[0032] S102 : Determine an overall score of the corresponding battery cell during operation of the vehicle according to the voltage data corresponding to each battery cell acquired in each set period.

[0033] The overall score of the battery cells during vehicle operation can represent the comprehensive changes in the voltage data of the battery cells during vehicle operation, and can be used to determine the consistency of the voltage data of each battery cell.

[0034] Exemplarily, the battery fault identification device can be based on the voltage data obtained from each battery cell at each cycle moment during vehicle operation, and determine the score value of the voltage data of each battery cell at each cycle moment by analyzing the difference value of the voltage data of each battery cell at each cycle moment, and determine the weight value of the voltage data of each battery cell at each cycle moment according to the importance of the voltage data of the battery cell at each cycle moment, and then perform weighted averaging on the score value of the voltage data of each battery cell at each cycle moment based on the weight value to obtain the overall score of the battery cell.

[0035] S103 : Determine abnormal battery cells according to the total score of each battery cell during the operation of the vehicle.

[0036] The abnormal battery cell may be a battery cell with abnormal voltage data fluctuation, which means that the voltage data of the battery cell is less consistent with the voltage data of other battery cells in the battery pack.

[0037] For example, after determining the overall score of each battery cell, the battery fault identification device can analyze the overall score of each battery cell according to an algorithm such as 3 times the standard deviation, calculate the average score of normal battery cells, and then further identify battery cells with a large deviation from the average score of normal battery cells as abnormal battery cells.

[0038] The above-mentioned technical solution of this embodiment obtains vehicle status data and voltage data of each battery cell in the battery pack according to a set period during vehicle operation, and determines the overall score of the corresponding battery cell during the vehicle operation based on the voltage data corresponding to each battery cell obtained in each set period, and determines the abnormal battery cell based on the total score value of each battery cell during the vehicle operation. During the operation of the vehicle, all battery cells in the battery pack can be comprehensively analyzed, fully considering the uncertainty of the voltage data of each battery cell and its importance in the overall data, thereby solving the problem of poor reliability and accuracy in identifying consistency changes of power battery cells and abnormal battery cells in the prior art, and improving the reliability and accuracy of battery fault identification.

[0039] Based on the above embodiment, this embodiment also provides an optional embodiment, which can further optimize the above embodiment, including:

[0040] After acquiring the vehicle status data and the voltage data of each battery cell in the battery pack according to the set period, the method may further include:

[0041] Abnormal processing is performed on the voltage data of each single battery in the battery pack according to the vehicle status data.

[0042] For example, the voltage data of each battery cell that has been screened out can be sorted and stored in the form of an array list, which is recorded as [N1, N2, ..., N m ], where N is the voltage data value of each battery cell, m is the number of voltage data of the battery cell, and the total number of battery cells in the battery pack is recorded as M. Then, based on the comparison between the total number M of battery cells in the battery pack and the number m of voltage data of the battery cells recorded in the vehicle status data, the voltage data with inconsistent numbers in the array list are removed to ensure data consistency. And / or, the voltage data of battery cells with a daily mileage of less than 10 km or a total number of data less than 100 during vehicle operation are removed to avoid misjudgment due to insufficient data volume. And / or, the voltage data of battery cells whose voltage data is 0 or null for a long time are removed to avoid erroneous anomalies caused by deviation from the overall value due to the voltage data of a certain battery cell being 0 for a long time.

[0043] Based on the above embodiment, this embodiment further provides an optional embodiment, which can further optimize step S103 of the above embodiment, determining abnormal battery cells according to the total score of each battery cell during the operation of the vehicle, and can include:

[0044] By using the density-based clustering algorithm, a target battery cell whose total score deviates from the average score of all cells is identified, and the target battery cell is recorded as an abnormal cell.

[0045] Among them, a density-based clustering algorithm (DBSCAN clustering method) can be used to identify battery cells that deviate from the score values of the majority of battery cells.

[0046] For example, after determining the total score of each battery cell during vehicle operation, a cluster analysis can be performed on the total score of each battery cell based on the DBSCAN clustering method, dividing the total scores of the battery cells into clusters and noise points, and marking the noise points as outliers to locate the corresponding abnormal cells. A cluster can be formed by aggregating the total scores of a large portion of concentrated and stable voltage data, while a noise point can represent a total score that is discrete from the total score of the cluster.

[0047] The advantage of this setting is that the DBSCAN clustering algorithm has good noise resistance and the ability to recognize clusters of different shapes. It can effectively distinguish normal monomers from abnormal monomers, avoid misjudgments caused by data noise or local anomalies, improve the accuracy and reliability of fault identification, and reduce the misjudgment rate.

[0048] Based on the above embodiment, this embodiment further provides an optional embodiment, which can further optimize the above embodiment. The method may further include:

[0049] Record the cumulative number of abnormalities of each battery cell within a preset time period;

[0050] Set the fault threshold based on the cumulative number of abnormalities when faults occur frequently;

[0051] When the cumulative number of abnormalities detected in a battery cell is greater than the fault threshold, a battery fault warning is issued.

[0052] The preset time period can refer to the total vehicle operating time per day from the time the vehicle rolls off the production line to the present. The cumulative number of abnormalities can refer to the total number of times a battery cell has been identified as an abnormal battery cell. Frequent faults can refer to the time period when the vehicle frequently reports battery cell consistency faults. The fault threshold can be used to determine whether a consistency fault alarm is required for an abnormal battery cell.

[0053] For example, starting from the moment the vehicle rolls off the production line, the voltage data of each battery cell in the battery pack during all vehicle operating hours can be recorded, thereby calculating the total score value of the voltage data of each battery cell every day, and using a clustering method to mark each battery cell as an abnormal battery cell or a normal battery cell. Then, the cumulative number of abnormalities of each battery cell can be recorded over time.

[0054] At the same time, the abnormality threshold can be determined based on the relationship between the time of reported battery cell consistency failures and the cumulative number of abnormal battery cell failures in historical records. For example, if a vehicle frequently reports battery cell consistency failures after May 8, 2024, and before the failure report, the battery cell frequently reports abnormalities, with a cumulative number of abnormalities reaching Q times, and if the battery cells of most vehicles frequently report battery cell consistency failures when the cumulative number of abnormalities reaches Q, then Q can be set as the battery cell consistency failure threshold. Where Q is a positive integer greater than 1.

[0055] On this basis, when the cumulative number of abnormal battery cells recorded by the vehicle over time is greater than the set fault threshold Q, a single cell consistency fault warning can be issued to remind the user to perform battery balancing or consistency maintenance in a timely manner to avoid vehicle downtime due to sudden failures.

[0056] Example 2

[0057] Figure 2 This is a flowchart of a battery fault identification method provided in the second embodiment of the present invention. This embodiment can further optimize step S102 of the above embodiment, and determine the overall score of the corresponding battery cell during the operation of the vehicle according to the voltage data obtained for each battery cell under each set period. It can include: for each battery cell, according to the voltage data corresponding to each set period, determining the information entropy weight of the battery cell under the corresponding set period; according to the information entropy weights corresponding to the battery cell, determining the total score value of the battery cell during the operation of the vehicle. Figure 2 As shown, the method includes:

[0058] S201 . During vehicle operation, obtain vehicle status data and voltage data of each battery cell in a battery pack according to a set period.

[0059] S202 : For each battery cell, determine the information entropy weight of the battery cell in the corresponding set period according to the voltage data corresponding to each set period.

[0060] The information entropy weight can be used to quantify the uncertainty of the voltage data of a battery cell at a specific time in a cycle. It can be understood that the information entropy value of the voltage data can be used to measure the degree of dispersion of the voltage data. The smaller the entropy value, the more concentrated the voltage data and the higher the stability. The larger the entropy value, the more discrete the voltage data and the lower the stability.

[0061] It should be noted that for each battery cell in the battery pack, voltage data can be collected at multiple times during the vehicle's operation at a set interval. For example, if the vehicle runs for one hour, voltage data can be collected at a set interval of 10 seconds. In this case, voltage data can be collected for each battery cell in the battery pack at the 10th, 20th, 30th, and 360th seconds after the vehicle has been running.

[0062] For example, for each battery cell in the battery pack, the voltage data corresponding to the battery cell at the first cycle moment, the second cycle moment, the third cycle moment ... the tth cycle moment can be obtained respectively, which is recorded as N: Where t is the number of cycles, and m is the number of battery cell voltage data. Furthermore, based on the voltage data corresponding to each battery cell during each of the set cycles, the information entropy values corresponding to the voltage data of each battery cell at the first cycle moment, the second cycle moment, the third cycle moment, and so on, are calculated to further determine the information entropy weight of each battery cell at each moment. It is understood that a greater information entropy value corresponds to a greater corresponding information entropy weight. In this embodiment, the method for determining the information entropy value of a battery cell during each set cycle based on the voltage data corresponding to each battery cell during the corresponding set cycle can be any existing method for calculating information entropy.

[0063] Optionally, determining the information entropy weight of the battery cell in the corresponding set period according to the voltage data corresponding to each set period may include:

[0064] For each set period, determining a probability distribution of the voltage data corresponding to the set period falling into each preset interval;

[0065] According to the probability distribution, the information entropy weight of the battery cell in the set period is determined.

[0066] The preset interval can be a voltage range set based on the different SOC ranges of the battery cells, or a voltage range that can represent the degree of dispersion of the battery cell voltage. The probability distribution can refer to the probability that the voltage data corresponding to each battery cell in each set cycle falls within the preset interval.

[0067] Exemplarily, when determining to obtain the voltage data N corresponding to each battery cell at the 1st cycle moment, the 2nd cycle moment, the 3rd cycle moment ... the tth cycle moment: After that, each data value can be standardized. The standardization formula can be Among them, N i,j is the data of row i and column j in the above voltage data, min(N i ) is the minimum voltage data among the voltage data at the cycle time corresponding to the i-th row, max(N i ) is the maximum voltage data among the voltage data at the cycle time corresponding to the i-th row, min(N j ) is the minimum voltage data among the voltage data of the battery cell corresponding to the jth column, X i,j is the data N for row i and column j i,j The corresponding standard value is obtained after standardization. The standardized data can be recorded as X: Wherein, t is the number of cycles, and m is the number of voltage data of battery cells.

[0068] Furthermore, for each cycle moment, the mean value μ of the voltage data of the battery cells at each cycle moment can be determined based on the voltage data of the battery cells at each cycle moment. t and standard deviation σ t , and then, based on the mean value μ of the voltage data of the battery cell at each cycle t and standard deviation σ t , multiple preset intervals corresponding to each periodic moment can be set to obtain the preset interval matrix B: in, Finally, the probability P of the voltage data of each battery cell corresponding to each set period falling within the preset interval range corresponding to each set period can be determined based on the preset interval matrix B for setting multiple preset intervals corresponding to each period: in Among them, p i,j It can be the probability value corresponding to the i-th row and j-th column in the matrix P. i,j It can be the interval range value corresponding to the i-th row and j-th column in the preset interval matrix B.

[0069] Furthermore, based on the probability P that the voltage data of each battery cell corresponding to each set period falls within the preset interval range corresponding to each set period: The information entropy value corresponding to each battery cell at each set cycle moment can be determined. The formula for determining the information entropy value can be: e j is the information entropy value of the battery cell at each set cycle moment, p ij is the probability value corresponding to the i-th row and j-th column in the matrix P.

[0070] Finally, based on the information entropy value e corresponding to each battery cell at each set cycle time j , the information entropy weight w of the battery cell under the set cycle can be determined:

[0071] S203 : Determine a total score value of the battery cell during the operation of the vehicle according to each of the information entropy weights corresponding to the battery cell.

[0072] For example, when determining the information entropy weight w corresponding to each battery cell, j Then, based on the information entropy weights wj corresponding to each battery cell, the sum of the probability distributions of each battery cell at all set periodic moments during the vehicle operation can be determined as the total score value of the battery cell during the vehicle operation. For example, the total score value s of the battery cell during the vehicle operation is i It can be: Among them, wj is the information entropy weight of a battery cell at the jth set cycle moment, p ij It may be the probability value corresponding to the i-th row and j-th column in the probability matrix P that the voltage data corresponding to each battery cell in each set period falls within the preset interval range corresponding to each set period.

[0073] S204 : Determine abnormal battery cells according to the total score of each battery cell during the operation of the vehicle.

[0074] Accordingly, when determining the total score value s of the battery cell during vehicle operation, i After that, the average score of all battery cells can be determined by the mean calculation method. Thus, the total score s of each battery cell is used i The average score of all battery cells The difference is calculated and its absolute value is taken to obtain Δs. Δs represents the degree to which the total score of a battery cell deviates from the total score of the majority of battery cells and is used to identify abnormal battery cells. For example, if the degree to which the total score of a battery cell during vehicle operation deviates from the total score of the majority of battery cells by Δs is greater than a preset threshold, the battery cell can be determined to be an abnormal battery cell.

[0075] The above technical solution in this embodiment determines the information entropy weight of each battery cell in the corresponding set period according to the voltage data corresponding to each set period, determines the total score value of the battery cell during the operation of the vehicle according to each information entropy weight corresponding to the battery cell, and then determines the abnormal battery cell according to the total score value of each battery cell during the operation of the vehicle, and provides a power battery cell consistency fault identification method based on information entropy weight. The information entropy weight fully considers the uncertainty of the voltage data of each battery cell and its importance in the overall data, can quantify the fluctuation of the voltage data of the battery cell, and can also highlight the impact of key data with larger information entropy weight on the overall consistency, thereby more accurately reflecting the true state of the battery cell consistency, and further improving the accuracy of power battery cell consistency identification.

[0076] Example 3

[0077] Figure 3 This is a schematic diagram of the structure of a battery fault identification device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes:

[0078] The acquisition module 31 may be used to acquire vehicle status data and voltage data of each battery cell in the battery pack according to a set period during vehicle operation;

[0079] The first determination module 32 may be configured to determine an overall score of the corresponding battery cell during operation of the vehicle based on the voltage data obtained for each battery cell in each set period;

[0080] The second determining module 33 may be configured to determine an abnormal battery cell according to the total score value of each battery cell during the operation of the vehicle.

[0081] A battery fault identification device provided in this embodiment can obtain vehicle status data and voltage data of each battery cell in a battery pack according to a set period during vehicle operation, and determine the overall score of the corresponding battery cell during the vehicle operation based on the voltage data corresponding to each battery cell obtained in each set period, and determine abnormal battery cells based on the total score value of each battery cell during the vehicle operation. During the operation of the vehicle, a comprehensive analysis can be performed on all battery cells in the battery pack, fully considering the uncertainty of the voltage data of each battery cell and its importance in the overall data, thereby solving the problem of poor reliability and accuracy in identifying consistency changes of power battery cells and abnormal battery cells in the prior art, and improving the reliability and accuracy of battery fault identification.

[0082] Optionally, the device may further include: a processing module.

[0083] The processing module can be used to obtain vehicle status data and voltage data of each battery cell in the battery pack according to a set period, and then perform abnormal processing on the voltage data of each single battery in the battery pack according to the vehicle status data.

[0084] Optionally, the first determination module 32 may further include: a weight module and a scoring module.

[0085] The weight module can be used to determine, for each battery cell, the information entropy weight of the battery cell in the corresponding set period according to the voltage data corresponding to each set period;

[0086] The scoring module may be configured to determine a total score value of the battery cells during operation of the vehicle according to the information entropy weights corresponding to the battery cells.

[0087] Optionally, the weight module may be specifically configured to determine, for each set period, a probability distribution of the voltage data corresponding to the set period falling within each preset interval;

[0088] According to the probability distribution, the information entropy weight of the battery cell in the set period is determined.

[0089] Optionally, the second determining module 33 may be specifically configured to identify a target battery cell whose total score deviates from the average score of all cells by using the called density-based clustering algorithm, and mark the target battery cell as an abnormal cell.

[0090] Optionally, the device may further include: an early warning module.

[0091] The early warning module can be used to record the cumulative number of abnormalities of each battery cell within a preset time period;

[0092] Set the fault threshold based on the cumulative number of abnormalities when faults occur frequently;

[0093] When the cumulative number of abnormalities detected in a battery cell is greater than the fault threshold, a battery fault warning is issued.

[0094] The battery fault identification device provided in the embodiment of the present invention can execute the battery fault identification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0095] Example 4

[0096] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0097] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. Various programs and data required for the operation of the electronic device 40 can also be stored in the RAM 43. The processor 41, ROM 42 and RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0098] Multiple components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0099] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 41 executes the various methods and processes described above, such as the battery fault identification method.

[0100] In some embodiments, the battery fault identification method can be implemented as a computer program that is tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the battery fault identification method described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the battery fault identification method in any other suitable manner (e.g., by means of firmware).

[0101] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0103] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0105] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0106] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0107] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0108] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A battery fault identification method, characterized in that: include: During vehicle operation, vehicle status data and voltage data of each battery cell in the battery pack are obtained according to a set period; determining, based on the voltage data corresponding to each battery cell acquired during each set period, an overall score of the corresponding battery cell during operation of the vehicle; An abnormal battery cell is determined according to a total score value of each of the battery cells during operation of the vehicle.

2. The method according to claim 1, characterized in that After acquiring the vehicle status data and the voltage data of each battery cell in the battery pack according to the set period, the method further includes: Abnormal processing is performed on the voltage data of each single battery in the battery pack according to the vehicle status data.

3. The method according to claim 1, characterized in that Determining the overall score of the corresponding battery cell during the operation of the vehicle according to the voltage data corresponding to each battery cell obtained in each set period includes: For each battery cell, determining the information entropy weight of the battery cell in the corresponding set period according to the voltage data corresponding to each set period; A total score value of the battery cell during the operation of the vehicle is determined according to each of the information entropy weights corresponding to the battery cell.

4. The method according to claim 3, characterized in that The determining, based on the voltage data corresponding to each of the set cycles, the information entropy weight of the battery cell in the corresponding set cycle includes: For each set period, determining a probability distribution of the voltage data corresponding to the set period falling within each preset interval; According to the probability distribution, the information entropy weight of the battery cell in the set period is determined.

5. The method according to claim 1, wherein The determining of abnormal battery cells according to the total score of each battery cell during the operation of the vehicle includes: By using the density-based clustering algorithm, a target battery cell whose total score deviates from the average score of all cells is identified, and the target battery cell is recorded as an abnormal cell.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Record the cumulative number of abnormalities of each battery cell within a preset time period; Set the fault threshold based on the cumulative number of abnormalities when faults occur frequently; When the cumulative number of abnormalities detected in a battery cell is greater than the fault threshold, a battery fault warning is issued.

7. A battery fault identification device, characterized in that: include: An acquisition module is used to acquire vehicle status data and voltage data of each battery cell in the battery pack according to a set period during vehicle operation; A first determination module is configured to determine an overall score of the corresponding battery cell during operation of the vehicle based on the voltage data obtained for each battery cell in each set period; The second determining module is configured to determine an abnormal battery cell according to a total score value of each battery cell during operation of the vehicle.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so as to enable the at least one processor to execute the battery fault identification method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the battery fault identification method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the battery fault identification method according to any one of claims 1 to 6.

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