WO2025112296A1 - 一种电池失效分析方法、装置、可读存储介质及电子设备 - Google Patents

一种电池失效分析方法、装置、可读存储介质及电子设备 Download PDF

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Publication number
WO2025112296A1
WO2025112296A1 PCT/CN2024/092256 CN2024092256W WO2025112296A1 WO 2025112296 A1 WO2025112296 A1 WO 2025112296A1 CN 2024092256 W CN2024092256 W CN 2024092256W WO 2025112296 A1 WO2025112296 A1 WO 2025112296A1
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Prior art keywords
battery failure
case
failure
factor set
battery
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English (en)
French (fr)
Inventor
孙嘉楠
李睿轩
黄伍德
晋文静
唐伟滨
吴宝晔
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Contemporary Amperex Technology Co Ltd
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Contemporary Amperex Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables

Definitions

  • the present application belongs to the field of battery technology, and in particular relates to a battery failure analysis method, device, computer-readable storage medium and electronic device.
  • embodiments of the present application provide a battery failure analysis method, device, computer-readable storage medium, and electronic device to solve the problem of low accuracy of analysis results in existing battery failure analysis methods.
  • a first aspect of an embodiment of the present application provides a battery failure analysis method, which may include:
  • the battery failure analysis results are determined based on the battery failure factor set.
  • the battery failure case library and the battery failure fault tree can be combined to conduct a comprehensive analysis of the battery failure case, so as to obtain a more comprehensive set of battery failure factors, effectively improving the accuracy of the final battery failure analysis results.
  • the battery failure case is analyzed based on a preset battery failure case library and a preset battery failure fault tree to obtain a battery failure factor set, which may include:
  • the battery failure factor set is determined based on the case library failure factor set and the fault tree failure factor set.
  • a case library failure factor set can be obtained based on the analysis of the battery failure case library, and a fault tree failure factor set can be obtained based on the analysis of the battery failure fault tree.
  • analyzing the battery failure case based on the battery failure case library to obtain the case library failure factor set may include:
  • respectively calculating the case similarity between the battery failure case and each historical case in the battery failure case library may include:
  • Extract the first case features in the battery failure case
  • Extracting the second case feature in the target historical case wherein the target historical case is any historical case in the battery failure case library;
  • the case similarity between the battery failure case and the target historical case is calculated based on the first case feature and the second case feature.
  • calculating the case similarity between the battery failure case and the target historical case according to the first case feature and the second case feature may include:
  • the case similarity between the battery failure case and the target historical case is determined based on the case feature distance.
  • the similarity between cases is characterized by calculating the distance between case features, which can achieve accurate measurement of case similarity.
  • calculating the case feature distance between the first case feature and the second case feature may include:
  • the weighted sum of each sub-feature distance is performed according to the preset sub-feature weight to obtain the case feature distance.
  • corresponding weights can be assigned to each sub-feature according to actual conditions, so that more important sub-features can be strengthened and unimportant sub-features can be weakened, thereby improving the accuracy of the final result.
  • determining a set of failure factors of a case library according to similar cases may include:
  • the root causes of failure are summarized to obtain the set of failure factors in the case library.
  • analyzing a battery failure case based on a battery failure fault tree to obtain a fault tree failure factor set may include:
  • a fault tree analysis is performed on the battery failure case based on the target branch to obtain a set of fault tree failure factors.
  • the corresponding target branch can be determined in the battery failure fault tree according to the failure mode, and the fault tree analysis can be performed based on the target branch, which is more targeted, narrows the analysis scope, and improves the analysis efficiency.
  • determining the battery failure factor set according to the case library failure factor set and the fault tree failure factor set may include:
  • the failure factor set of the case library and the offline factor set of the fault tree are combined to obtain an offline factor set; wherein the offline factor set of the fault tree is an offline factor set in the failure factor set of the fault tree;
  • the offline factor set and the fault tree online factor set are combined to obtain a battery failure factor set; wherein the fault tree online factor set is an online factor set in the fault tree failure factor set.
  • determining a battery failure analysis result according to a battery failure factor set may include:
  • the battery failure analysis results are determined based on the importance of data features.
  • the method may further include:
  • the use of data feature selection in advance can eliminate parameters with poor classification and regression effects, reduce the dimension of the data, and improve the analysis efficiency.
  • the method may further include:
  • the data table is cleaned in advance, a large amount of invalid data can be cleaned out, avoiding its interference with the analysis process, and effectively improving the accuracy of the final battery failure analysis results.
  • the method may further include:
  • the battery failure case library and battery failure fault tree are updated according to the battery failure analysis results.
  • the battery failure case library and battery failure fault tree can be updated according to the analysis results of this time, thereby realizing the closed loop of the analysis process, continuously enriching the battery failure case library and battery failure fault tree, and providing a more complete foundation for subsequent battery failure case analysis.
  • a second aspect of an embodiment of the present application provides a battery failure analysis device, which may include:
  • a battery failure case acquisition module is used to acquire battery failure cases to be analyzed
  • a battery failure analysis module is used to analyze battery failure cases based on a preset battery failure case library and a preset battery failure fault tree to obtain a battery failure factor set;
  • the battery failure analysis result determination module is used to determine the battery failure analysis result according to the battery failure factor set.
  • the battery failure analysis module may include:
  • a battery failure case library analysis submodule is used to analyze battery failure cases based on the battery failure case library to obtain a set of failure factors of the case library;
  • the battery failure fault tree analysis submodule is used to analyze the battery failure case based on the battery failure fault tree and obtain the failure factor set of the fault tree;
  • the battery failure factor set determination submodule is used to determine the battery failure factor set based on the case library failure factor set and the fault tree failure factor set.
  • the battery failure case library analysis submodule may include:
  • a case similarity calculation unit used to respectively calculate the case similarity between the battery failure case and each historical case in the battery failure case library
  • a similar case selection unit used to select historical cases whose case similarity is greater than a preset similarity threshold from the battery failure case library as similar cases of the battery failure case;
  • the case base failure factor set determination unit is used to determine the case base failure factor set according to similar cases.
  • the case similarity calculation unit may include:
  • a first case feature extraction subunit used to extract first case features in a battery failure case
  • a second case feature extraction subunit is used to extract a second case feature in a target historical case; wherein the target historical case is any historical case in a battery failure case library;
  • the case similarity calculation subunit is used to calculate the case similarity between the battery failure case and the target historical case according to the first case feature and the second case feature.
  • the case similarity calculation subunit can be specifically used to: calculate the case feature distance between the first case feature and the second case feature; and determine the case similarity between the battery failure case and the target historical case based on the case feature distance.
  • the case similarity calculation subunit can be specifically used to: respectively calculate the sub-feature distances between each corresponding sub-feature of the first case feature and the second case feature; and perform weighted summation of each sub-feature distance according to a preset sub-feature weight to obtain the case feature distance.
  • the case library failure factor set determination unit may be specifically used to: obtain failure root causes of similar cases; and summarize the failure root causes to obtain the case library failure factor set.
  • the battery failure fault tree analysis submodule can be specifically used to: extract the failure mode in the battery failure case; determine the target branch corresponding to the failure mode in the battery failure fault tree; perform fault tree analysis on the battery failure case based on the target branch to obtain a set of fault tree failure factors.
  • the battery failure factor set determination submodule can be specifically used to: combine the case library failure factor set and the fault tree offline factor set to obtain an offline factor set; wherein the fault tree offline factor set is the offline factor set in the fault tree failure factor set; combine the offline factor set and the fault tree online factor set to obtain a battery failure factor set; wherein the fault tree online factor set is the online factor set in the fault tree failure factor set.
  • the battery failure analysis result determination module may include:
  • a data table construction submodule is used to construct a data table of battery failure cases according to a set of battery failure factors
  • the target model construction submodule is used to construct a machine learning model according to the data table to obtain the constructed target model;
  • a data feature importance ranking submodule is used to determine the importance ranking of data features in a data table according to a target model
  • the battery failure analysis result determination submodule is used to determine the battery failure analysis result according to the importance ranking of data features.
  • the battery failure analysis result determination module may further include:
  • the data feature selection submodule is used to select data features in the data table using a preset data feature selection method to obtain a selected data table.
  • the battery failure analysis result determination module may further include:
  • the data cleaning submodule is used to clean the data in the data table using a preset data cleaning method to obtain a cleaned data table.
  • the battery failure analysis device may further include:
  • the case library and fault tree update module is used to update the battery failure case library and battery failure fault tree according to the battery failure analysis results.
  • a third aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above-mentioned battery failure analysis methods are implemented.
  • a fourth aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above-mentioned battery failure analysis methods when executing the computer program.
  • a fifth aspect of an embodiment of the present application provides a computer program product.
  • the computer program product When the computer program product is run on an electronic device, the electronic device executes the steps of any one of the above-mentioned battery failure analysis methods.
  • the embodiment of the present application obtains a battery failure case to be analyzed; analyzes the battery failure case based on a preset battery failure case library and a preset battery failure fault tree to obtain a battery failure factor set; and determines the battery failure analysis result based on the battery failure factor set.
  • the battery failure case can be comprehensively analyzed in combination with the battery failure case library and the battery failure fault tree, so that a more comprehensive battery failure factor set can be analyzed, effectively improving the accuracy of the final battery failure analysis result.
  • FIG1 is a flow chart of an embodiment of a battery failure analysis method in an embodiment of the present application.
  • FIG2 is a schematic flow chart of analyzing a battery failure case based on a preset battery failure case library and a preset battery failure fault tree;
  • FIG3 is a schematic flow chart of analyzing battery failure cases based on a battery failure case library
  • FIG4 is a schematic flow chart of determining a battery failure analysis result based on a battery failure factor set
  • FIG5 is a schematic diagram of an output result of an electronic device
  • FIG6 is a structural diagram of an embodiment of a battery failure analysis device in an embodiment of the present application.
  • FIG. 7 is a schematic block diagram of an electronic device in an embodiment of the present application.
  • the term “if” may be interpreted as “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.
  • the phrases “if it is determined” or “if [described condition or event] is detected” may be interpreted as meaning “upon determination” or “in response to determining” or “upon detection of [described condition or event]” or “in response to detecting [described condition or event],” depending on the context.
  • Battery failure can include many aspects. Take battery lithium deposition as an example. When a lithium-ion battery is charged, lithium ions are deintercalated from the positive electrode and embedded in the negative electrode. However, when some abnormal conditions occur, such as insufficient lithium embedding space in the negative electrode, too much resistance to lithium ion embedding in the negative electrode, lithium ions are deintercalated too quickly from the positive electrode but cannot be embedded in the negative electrode in equal amounts, the lithium ions cannot be embedded in the negative electrode. Lithium ions can only obtain electrons on the surface of the negative electrode, thus forming silvery-white metallic lithium, which leads to the phenomenon of lithium deposition in the battery.
  • the embodiments of the present application can combine the battery failure case library and the battery failure fault tree to conduct a comprehensive analysis of the battery failure cases, thereby analyzing and obtaining a more comprehensive set of battery failure factors, effectively improving the accuracy of the final battery failure analysis results.
  • the execution subjects of the embodiments of the present application may include but are not limited to electronic devices such as desktop computers, notebooks, PDAs, servers, etc.
  • An embodiment of a battery failure analysis method in an embodiment of the present application may include:
  • Step S101 Obtain a battery failure case to be analyzed.
  • a user may input a battery failure case to be analyzed into an input interface of an electronic device, and the electronic device may obtain the battery failure case from the input interface and perform subsequent battery failure analysis on it.
  • the user can store the battery failure case to be analyzed in a preset storage device, and the electronic device can obtain the stored battery failure case from the storage device through a pre-established data transmission link, and perform subsequent battery failure analysis on it.
  • the data transmission link can be a data transmission link established based on at least one communication solution such as Wireless Local Area Networks (WLAN) (for example, Wireless Fidelity (WiFi)), Bluetooth, Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), Infrared (IR), Global System for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division-Synchronous Code Division Multiple Access (TD-SCDMA) and Long Term Evolution (LTE).
  • WLAN Wireless Local Area Networks
  • GNSS Global Navigation Satellite System
  • FM Frequency Modulation
  • NFC Near Field Communication
  • IR Infrared
  • GSM Global System for Mobile communications
  • GPRS General Packet Radio Service
  • CDMA Code Division Multiple Access
  • WCDMA Wideband Code Division Multiple Access
  • TD-SCDMA Time Division-Synchronous Code Division Multiple Access
  • LTE Long Term Evolution
  • Battery failure cases may include but are not limited to failure descriptions and failure modes. Taking the failure case of battery lithium deposition as an example, the failure description may include but is not limited to failure nodes, failure rates, failure surfaces, failure locations, lithium deposition morphology, black spot morphology, failure levels and other descriptive dimensions; failure modes may include but are not limited to black spots, large-surface lithium deposition, corner lithium deposition and other different modes.
  • Step S102 Perform battery failure case analysis based on a preset battery failure case library and a preset battery failure fault tree. Analyze and obtain the battery failure factor set.
  • FTA Fault Tree Analysis
  • a fault tree for FTA analysis of battery failure can be pre-constructed, which is recorded as a battery failure fault tree.
  • the specific construction process of the fault tree can refer to any FTA method in the prior art, and the embodiment of the present application does not specifically limit this.
  • a historical case library for analyzing battery failure can be pre-built and recorded as a battery failure case library.
  • the battery failure case library can include various historical cases for which battery failure analysis has been completed, and each historical case is pre-marked with the corresponding failure root cause.
  • the FTA analysis method can be combined with the case library analysis method to conduct a comprehensive analysis of battery failure cases, thereby obtaining a more comprehensive and complete set of battery failure factors.
  • step S102 may specifically include the process shown in FIG2 :
  • Step S1021 Analyze the battery failure cases based on the battery failure case library to obtain a set of failure factors in the case library.
  • Step S1021 can specifically include the process shown in Figure 3:
  • Step S1021a respectively calculating the case similarity between the battery failure case and each historical case in the battery failure case library.
  • the failure description in the battery failure case can be extracted and used as the first case feature, and each description dimension in the failure description is used as one of its sub-features; similarly, the failure description in the target historical case can be extracted and used as the second case feature, and each description dimension in the failure description is used as one of its sub-features.
  • the case similarity between the battery failure case and the target historical case may be calculated based on the first case feature and the second case feature.
  • the case feature distance between the first case feature and the second case feature may be calculated first.
  • the sub-feature distances between the corresponding sub-features of the first case feature and the second case feature may be calculated respectively, and the weighted sum of the sub-feature distances may be performed according to a preset sub-feature weight to obtain the case feature distance.
  • One-Hot encoding can be performed on it first, and then the cosine distance calculation is performed on the encoded data to obtain the corresponding sub-feature distance.
  • the Absolute Percentage Error (APE) can be calculated to obtain the corresponding sub-feature distance.
  • corresponding weights can be assigned in advance according to the actual situation, with more important sub-features being assigned larger weights and less important sub-features being assigned smaller weights.
  • the weighted sum of each sub-feature distance can be performed according to these preset sub-feature weights to obtain the case feature distance. In this way, more important sub-features can be strengthened and unimportant sub-features can be weakened, thereby improving the accuracy of the final result.
  • the case similarity between the battery failure case and the target historical case can be determined based on the case feature distance.
  • case similarity is negatively correlated with case feature distance, that is, the larger the case feature distance between the battery failure case and the target historical case, the smaller the case similarity between the battery failure case and the target historical case; conversely, the smaller the case feature distance between the battery failure case and the target historical case, the greater the case similarity between the battery failure case and the target historical case.
  • the case feature distance can be normalized to the interval range of [0,1], and then the difference between 1 and the case feature distance is calculated and used as the case similarity between the battery failure case and the target historical case.
  • the inverse of the case feature distance can be calculated and used as the case similarity between the battery failure case and the target historical case.
  • Step S1021b selecting historical cases whose case similarity is greater than a preset similarity threshold from the battery failure case library as similar cases to the battery failure case.
  • the specific value of the similarity threshold can be set according to actual conditions.
  • the similarity threshold can be set to 0.8, 0.85, 0.9, 0.95 or other values, which is not specifically limited in the embodiments of the present application.
  • Step S1021c determine a set of failure factors of the case library based on similar cases.
  • Each historical case in the battery failure case library is pre-labeled with the corresponding root cause of failure. After selecting similar cases of battery failure cases from the battery failure case library, the root causes of failure of similar cases can be obtained and summarized to obtain a set of failure factors in the case library.
  • similar cases can be selected from the battery failure case library.
  • the similarity between these similar cases and the battery failure cases is relatively large.
  • the case library failure factor set determined based on these similar cases has a good reference significance for the battery failure cases.
  • the occurrence frequency of the root causes of failures of similar cases can also be counted, and the root causes of failures can be sorted in the case library failure factor set in descending order of occurrence frequency, that is, the higher the occurrence frequency of the root cause of failure, the higher its ranking, and the lower the occurrence frequency of the root cause of failure, the lower its ranking, so as to improve the efficiency of subsequent analysis.
  • Step S1022 Analyze the battery failure case based on the battery failure fault tree to obtain a set of fault tree failure factors.
  • any FTA method in the prior art can be used to analyze battery failure cases according to specific circumstances, and no specific limitation is made here.
  • the battery failure fault tree may include branches corresponding to each failure mode.
  • the battery failure fault tree may include but is not limited to branches corresponding to black spot failure modes, branches corresponding to large-surface lithium deposition failure modes, branches corresponding to corner lithium deposition failure modes, etc.
  • the failure mode in the battery failure case can be extracted, and the branch corresponding to the failure mode can be determined in the battery failure fault tree and recorded as the target branch.
  • the target branch there is no need to analyze the battery failure case based on the entire battery failure fault tree, but only the target branch, so as to obtain the set of fault tree failure factors. This method is more targeted, narrows the scope of analysis, and effectively improves the analysis efficiency.
  • Step S1023 Determine a battery failure factor set according to the case library failure factor set and the fault tree failure factor set.
  • the failure factors included in the database can be recorded as online factors, and the failure factors not included in the database can be recorded as offline factors.
  • the failure factors in the case library failure factor set are all offline factors, while the failure factors in the fault tree failure factor set include online factors and offline factors.
  • the offline factor set in the fault tree failure factor set can be recorded as the fault tree offline factor set
  • the online factor set in the fault tree failure factor set can be recorded as the fault tree online factor set.
  • the analysis index of each failure factor can be calculated separately, where the analysis index may include but is not limited to distribution difference, equipment concentration, film roll concentration, time concentration and other indicators.
  • an offline factor set By combining the case library failure factor set and the fault tree offline factor set, an offline factor set can be obtained. Similar to the online factors, the analysis index of each failure factor in the offline factor set can be calculated separately, where the analysis index can include but is not limited to distribution difference, equipment concentration, film roll concentration, time concentration and other indicators.
  • the offline factor set and the fault tree online factor set may be combined to obtain a battery failure factor set.
  • a case library failure factor set can be obtained based on the analysis of the battery failure case library, and a fault tree failure factor set can be obtained based on the analysis of the battery failure fault tree.
  • Step S103 determining a battery failure analysis result according to the battery failure factor set.
  • Step S103 can specifically include the process shown in FIG4:
  • Step S1031 construct a data table of battery failure cases according to the battery failure factor set.
  • the online factors and offline factors in the battery failure factor set can be combined according to certain rules to construct a data table of the battery failure case.
  • the specific rules can be set according to the actual situation, for example, they can include but are not limited to the rules of cell barcode matching, core roll (JR) matching, film roll matching, etc.
  • a preset data cleaning method may be used to perform data cleaning in the data table to obtain a cleaned data table.
  • the specific data cleaning method can be set according to the actual situation, and the embodiment of the present application does not specifically limit this.
  • it can include but is not limited to: removing parameters with no differences in the data table, removing parameters in the data table where the data missing exceeds a set threshold, removing rows or columns containing missing values (i.e., dropna), and other cleaning operations.
  • a preset data feature selection method can also be used to select data features in a data table, thereby obtaining a selected data table.
  • the specific data feature selection method can be set according to the actual situation, and the embodiment of the present application does not specifically limit this.
  • it can include but is not limited to: recursive feature elimination method, Select from model and other feature selection methods.
  • Step S1032 construct a machine learning model according to the data table to obtain a constructed target model.
  • the data in the data table can be input into the initial machine learning model for classification or regression modeling to obtain the final target model.
  • the specific machine learning model to be used can be set according to the actual situation, and the embodiment of the present application does not specifically limit this.
  • it can include but is not limited to: decision tree, random forest, XGBoost and other models.
  • Step S1033 Determine the importance ranking of data features in the data table according to the target model.
  • the target model can be used to rank the importance of each feature (ie, failure factor) in the data table to determine the degree of its influence on the final result.
  • the importance of features can be evaluated based on the number of node splits or information gain of the feature in the decision tree; for random forests, the out-of-bag (OOB) error can be used to evaluate the importance of features; for XGBoost, the average number of feature splits on each weak learner or the loss reduction brought by the feature to the model can be used to evaluate the importance of features.
  • OOB out-of-bag
  • Step S1034 Determine the battery failure analysis result according to the order of importance of the data features.
  • the electronic device after determining the data feature importance ranking, can output the data feature importance ranking through a preset human-computer interaction interface. In addition, it can also be displayed in combination with indicators such as distribution differences, equipment concentration, film roll concentration, and time concentration.
  • FIG5 is a schematic diagram of the output result of the electronic device. As shown in the figure, the importance of data features is ranked as follows: failure factor A, failure factor B, failure factor C, failure factor D, failure factor E, failure factor F, ... After the user verifies through manual inspection, the failure factor B can be determined as the final battery failure analysis result, and the battery failure analysis result can be input into the electronic device through the human-computer interaction interface.
  • the electronic device may also update the battery failure case library and the battery failure fault tree according to the battery failure analysis result.
  • the current battery failure case can be added to the battery failure case library as a new historical case, and its corresponding root cause of failure can be marked according to the battery failure analysis results; the missing failure factors in the battery failure fault tree can also be fed back and completed according to the battery failure analysis results, thereby realizing a closed loop of the analysis process, continuously enriching the battery failure case library and the battery failure fault tree, and providing a more complete foundation for subsequent battery failure case analysis.
  • the embodiment of the present application obtains a battery failure case to be analyzed; analyzes the battery failure case based on a preset battery failure case library and a preset battery failure fault tree to obtain a battery failure factor set; and determines the battery failure analysis result based on the battery failure factor set.
  • the effective fault tree is used to conduct a comprehensive analysis of battery failure cases, so that a more comprehensive set of battery failure factors can be analyzed, effectively improving the accuracy of the final battery failure analysis results.
  • FIG6 shows a structural diagram of an embodiment of a battery failure analysis device provided in the embodiment of the present application.
  • a battery failure analysis device may include:
  • a battery failure case acquisition module 601 is used to acquire a battery failure case to be analyzed
  • a battery failure analysis module 602 is used to analyze the battery failure case based on a preset battery failure case library and a preset battery failure fault tree to obtain a battery failure factor set;
  • the battery failure analysis result determination module 603 is used to determine the battery failure analysis result according to the battery failure factor set.
  • the battery failure analysis module may include:
  • a battery failure case library analysis submodule is used to analyze battery failure cases based on the battery failure case library to obtain a set of failure factors of the case library;
  • the battery failure fault tree analysis submodule is used to analyze the battery failure case based on the battery failure fault tree and obtain the failure factor set of the fault tree;
  • the battery failure factor set determination submodule is used to determine the battery failure factor set based on the case library failure factor set and the fault tree failure factor set.
  • the battery failure case library analysis submodule may include:
  • a case similarity calculation unit used to respectively calculate the case similarity between the battery failure case and each historical case in the battery failure case library
  • a similar case selection unit used to select historical cases whose case similarity is greater than a preset similarity threshold from the battery failure case library as similar cases of the battery failure case;
  • the case base failure factor set determination unit is used to determine the case base failure factor set according to similar cases.
  • the case similarity calculation unit may include:
  • a first case feature extraction subunit used to extract first case features in a battery failure case
  • a second case feature extraction subunit is used to extract a second case feature in a target historical case; wherein the target historical case is any historical case in a battery failure case library;
  • the case similarity calculation subunit is used to calculate the case similarity between the battery failure case and the target historical case according to the first case feature and the second case feature.
  • the case similarity calculation subunit can be specifically used to: calculate The case feature distance between the first case feature and the second case feature; determining the case similarity between the battery failure case and the target historical case according to the case feature distance.
  • the case similarity calculation subunit can be specifically used to: respectively calculate the sub-feature distances between each corresponding sub-feature of the first case feature and the second case feature; perform weighted summation of each sub-feature distance according to a preset sub-feature weight to obtain the case feature distance.
  • the case library failure factor set determination unit can be specifically used to: obtain failure root causes of similar cases; summarize the failure root causes to obtain the case library failure factor set.
  • the battery failure fault tree analysis submodule can be specifically used to: extract the failure mode in the battery failure case; determine the target branch corresponding to the failure mode in the battery failure fault tree; perform fault tree analysis on the battery failure case based on the target branch to obtain a set of fault tree failure factors.
  • the battery failure factor set determination submodule can be specifically used to: combine the case library failure factor set and the fault tree offline factor set to obtain an offline factor set; wherein the fault tree offline factor set is the offline factor set in the fault tree failure factor set; combine the offline factor set and the fault tree online factor set to obtain a battery failure factor set; wherein the fault tree online factor set is the online factor set in the fault tree failure factor set.
  • the battery failure analysis result determination module may include:
  • a data table construction submodule is used to construct a data table of battery failure cases according to a set of battery failure factors
  • the target model construction submodule is used to construct a machine learning model according to the data table to obtain the constructed target model;
  • a data feature importance ranking submodule is used to determine the importance ranking of data features in a data table according to a target model
  • the battery failure analysis result determination submodule is used to determine the battery failure analysis result according to the importance ranking of data features.
  • the battery failure analysis result determination module may also include:
  • the data feature selection submodule is used to select data features in the data table using a preset data feature selection method to obtain a selected data table.
  • the battery failure analysis result determination module may also include:
  • the data cleaning submodule is used to clean the data in the data table using a preset data cleaning method to obtain a cleaned data table.
  • the battery failure analysis device may further include:
  • the case library and fault tree update module is used to update the battery failure case library and battery failure fault tree according to the battery failure analysis results.
  • FIG7 shows a schematic block diagram of an electronic device provided in an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown.
  • the electronic device 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70.
  • the processor 70 executes the computer program 72, the steps in the above-mentioned various battery failure analysis method embodiments are implemented, such as steps S101 to S103 shown in FIG1 .
  • the processor 70 executes the computer program 72, the functions of the modules/units in the above-mentioned various device embodiments are implemented, such as the functions of modules 601 to 603 shown in FIG6 .
  • the computer program 72 may be divided into one or more modules/units, which are stored in the memory 71 and executed by the processor 70 to complete the present application.
  • the one or more modules/units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 72 in the electronic device 7.
  • the electronic device 7 may be a computing device such as a desktop computer, a notebook, a palm computer, a server, etc.
  • FIG. 7 is merely an example of the electronic device 7 and does not constitute a limitation on the electronic device 7.
  • the electronic device 7 may include more or fewer components than shown in the figure, or may combine certain components, or different components.
  • the electronic device 7 may also include an input/output device, a network access device, a bus, etc.
  • the processor 70 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor may be a microprocessor or any conventional processor, etc.
  • the memory 71 may be an internal storage unit of the electronic device 7, such as a hard disk or memory of the electronic device 7.
  • the memory 71 may also be an external storage device of the electronic device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), etc. equipped on the electronic device 7.
  • the memory 71 may also include both an internal storage unit of the electronic device 7 and an external storage device.
  • the memory 71 is used to store the computer program and other programs and data required by the electronic device 7.
  • the memory 71 may also be used to temporarily store data that has been output or is to be output.
  • the disclosed devices/electronic devices and methods can be implemented in other ways.
  • the device/electronic device embodiments described above are merely schematic.
  • the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
  • Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
  • the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program.
  • the computer program can be stored in a computer-readable storage medium.
  • the computer program is executed by the processor, it can implement the above-mentioned
  • the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form.
  • the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

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Abstract

本申请属于电池技术领域，尤其涉及一种电池失效分析方法、装置、计算机可读存储介质及电子设备。所述方法包括：获取待分析的电池失效案例；基于预设的电池失效案例库和预设的电池失效故障树对所述电池失效案例进行分析，得到电池失效因子集合；根据所述电池失效因子集合确定电池失效分析结果。通过本申请，可以结合电池失效案例库和电池失效故障树对电池失效案例进行综合分析，从而可以分析得到更加全面的电池失效因子集合，有效提高了最终的电池失效分析结果的准确度。

Description

一种电池失效分析方法、装置、可读存储介质及电子设备
本申请要求于2023年11月27日提交国家知识产权局、申请号为202311597778.0、申请名称为“一种电池失效分析方法、装置、可读存储介质及电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于电池技术领域，尤其涉及一种电池失效分析方法、装置、计算机可读存储介质及电子设备。
背景技术
随着可再生能源和电动汽车的快速普及，锂离子电池等电池的需求正在急剧增长。然而，由于其生产过程中的复杂制造流程和物理化学反应，电池的失效问题(如电池析锂等)一直是制约其性能和可靠性的主要因素。
在现有的电池失效分析方法中，需要依靠大量的生产经验，配合物理和化学实验来识别和解决问题，虽然有时也能得到一些有用的分析结果，但整体而言分析不全面，分析结果的准确度较低，无法应对大规模生产中的挑战。
技术问题
有鉴于此，本申请实施例提供了一种电池失效分析方法、装置、计算机可读存储介质及电子设备，以解决现有的电池失效分析方法存在的分析结果的准确度较低的问题。
技术解决方案
本申请实施例的第一方面提供了一种电池失效分析方法，可以包括：
获取待分析的电池失效案例；
基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行分析，得到电池失效因子集合；
根据电池失效因子集合确定电池失效分析结果。
通过上述方案，可以结合电池失效案例库和电池失效故障树对电池失效案例进行综合分析，从而可以分析得到更加全面的电池失效因子集合，有效提高了最终的电池失效分析结果的准确度。
在第一方面的一种具体实现方式中，基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行分析，得到电池失效因子集合，可以包括：
基于电池失效案例库对电池失效案例进行分析，得到案例库失效因子集合；
基于电池失效故障树对电池失效案例进行分析，得到故障树失效因子集合；
根据案例库失效因子集合和故障树失效因子集合，确定电池失效因子集合。
通过上述方案，可以基于电池失效案例库的分析得到案例库失效因子集合，基于电池失效故障树的分析得到故障树失效因子集合，通过对这两者的综合考虑，得到更加全面的电池失效因子集合。
在第一方面的一种具体实现方式中，基于电池失效案例库对电池失效案例进行分析，得到案例库失效因子集合，可以包括：
分别计算电池失效案例与电池失效案例库中的各个历史案例之间的案例相似度；
从电池失效案例库中选取案例相似度大于预设的相似度阈值的历史案例作为电池失效案例的相似案例；
根据相似案例确定案例库失效因子集合。
通过上述方案，可以从电池失效案例库中选取相似案例，这些相似案例与电池失效案例之间的相似度较大，根据这些相似案例确定的案例库失效因子集合对于电池失效案例具有较好的参考意义。
在第一方面的一种具体实现方式中，分别计算电池失效案例与电池失效案例库中的各个历史案例之间的案例相似度，可以包括：
提取电池失效案例中的第一案例特征；
提取目标历史案例中的第二案例特征；其中，目标历史案例为电池失效案例库中的任意一个历史案例；
根据第一案例特征和第二案例特征，计算电池失效案例和目标历史案例之间的案例相似度。
在第一方面的一种具体实现方式中，根据第一案例特征和第二案例特征，计算电池失效案例和目标历史案例之间的案例相似度，可以包括：
计算第一案例特征和第二案例特征之间的案例特征距离；
根据案例特征距离确定电池失效案例和目标历史案例之间的案例相似度。
通过上述方案，通过计算案例特征之间的距离来表征案例之间的相似度，可以实现对案例相似度的精确度量。
在第一方面的一种具体实现方式中，计算第一案例特征和第二案例特征之间的案例特征距离，可以包括：
分别计算第一案例特征和第二案例特征的各个对应的子特征之间的子特征距离；
根据预设的子特征权重对各个子特征距离进行加权求和，得到案例特征距离。
通过上述方案，可以根据实际情况为各个子特征赋予相对应的权重，强化较重要的子特征，弱化不重要的子特征，提高了最终结果的准确度。
在第一方面的一种具体实现方式中，根据相似案例确定案例库失效因子集合，可以包括：
获取相似案例的失效根因；
对失效根因进行汇总，得到案例库失效因子集合。
在第一方面的一种具体实现方式中，基于电池失效故障树对电池失效案例进行分析，得到故障树失效因子集合，可以包括：
提取电池失效案例中的失效模式；
在电池失效故障树中确定与失效模式对应的目标分支；
基于目标分支对电池失效案例进行故障树分析，得到故障树失效因子集合。
通过上述方案，可以根据失效模式在电池失效故障树中确定对应的目标分支，并基于该目标分支进行故障树分析，具有更强的针对性，缩小了分析范围，提高了分析效率。
在第一方面的一种具体实现方式中，根据案例库失效因子集合和故障树失效因子集合，确定电池失效因子集合，可以包括：
对案例库失效因子集合和故障树线下因子集合进行组合，得到线下因子集合；其中，故障树线下因子集合为故障树失效因子集合中的线下因子集合；
对线下因子集合和故障树线上因子集合进行组合，得到电池失效因子集合；其中，故障树线上因子集合为故障树失效因子集合中的线上因子集合。
在第一方面的一种具体实现方式中，根据电池失效因子集合确定电池失效分析结果，可以包括：
按照电池失效因子集合构建电池失效案例的数据表；
根据数据表进行机器学习模型构建，得到构建后的目标模型；
根据目标模型确定数据表中的数据特征重要性排序；
根据数据特征重要性排序确定电池失效分析结果。
通过上述方案，采用机器学习的方式来进行电池失效分析，有效提高了整体的分析效率。
在第一方面的一种具体实现方式中，在根据数据表进行机器学习模型构建之前，还可以包括：
使用预设的数据特征选型方法在数据表中进行数据特征选型，得到选型后的数据表。
通过上述方案，预先使用数据特征选型可以剔除掉对分类、回归效果较差的参数，降低数据的维度，提高了分析效率。
在第一方面的一种具体实现方式中，在根据数据表进行机器学习模型构建之前，还可以包括：
使用预设的数据清洗方法在数据表中进行数据清洗，得到清洗后的数据表。
通过上述方案，预先对数据表进行数据清洗，可以清洗掉大量的无效数据，避免了其对分析过程的干扰，有效提高了最终的电池失效分析结果的准确度。
在第一方面的一种具体实现方式中，在根据电池失效因子集合确定电池失效分析结果之后，还可以包括：
根据电池失效分析结果对电池失效案例库和电池失效故障树进行更新。
通过上述方案，可以根据本次的分析结果对电池失效案例库和电池失效故障树进行更新，从而实现了分析过程的闭环，不断丰富电池失效案例库和电池失效故障树，为后续的电池失效案例分析提供了更加完备的基础。
本申请实施例的第二方面提供了一种电池失效分析装置，可以包括：
电池失效案例获取模块，用于获取待分析的电池失效案例；
电池失效分析模块，用于基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行分析，得到电池失效因子集合；
电池失效分析结果确定模块，用于根据电池失效因子集合确定电池失效分析结果。
在第二方面的一种具体实现方式中，电池失效分析模块可以包括：
电池失效案例库分析子模块，用于基于电池失效案例库对电池失效案例进行分析，得到案例库失效因子集合；
电池失效故障树分析子模块，用于基于电池失效故障树对电池失效案例进行分析，得到故障树失效因子集合；
电池失效因子集合确定子模块，用于根据案例库失效因子集合和故障树失效因子集合，确定电池失效因子集合。
在第二方面的一种具体实现方式中，电池失效案例库分析子模块可以包括：
案例相似度计算单元，用于分别计算电池失效案例与电池失效案例库中的各个历史案例之间的案例相似度；
相似案例选取单元，用于从电池失效案例库中选取案例相似度大于预设的相似度阈值的历史案例作为电池失效案例的相似案例；
案例库失效因子集合确定单元，用于根据相似案例确定案例库失效因子集合。
在第二方面的一种具体实现方式中，案例相似度计算单元可以包括：
第一案例特征提取子单元，用于提取电池失效案例中的第一案例特征；
第二案例特征提取子单元，用于提取目标历史案例中的第二案例特征；其中，目标历史案例为电池失效案例库中的任意一个历史案例；
案例相似度计算子单元，用于根据第一案例特征和第二案例特征，计算电池失效案例和目标历史案例之间的案例相似度。
在第二方面的一种具体实现方式中，案例相似度计算子单元可以具体用于：计算第一案例特征和第二案例特征之间的案例特征距离；根据案例特征距离确定电池失效案例和目标历史案例之间的案例相似度。
在第二方面的一种具体实现方式中，案例相似度计算子单元可以具体用于：分别计算第一案例特征和第二案例特征的各个对应的子特征之间的子特征距离；根据预设的子特征权重对各个子特征距离进行加权求和，得到案例特征距离。
在第二方面的一种具体实现方式中，案例库失效因子集合确定单元可以具体用于：获取相似案例的失效根因；对失效根因进行汇总，得到案例库失效因子集合。
在第二方面的一种具体实现方式中，电池失效故障树分析子模块可以具体用于：提取电池失效案例中的失效模式；在电池失效故障树中确定与失效模式对应的目标分支；基于目标分支对电池失效案例进行故障树分析，得到故障树失效因子集合。
在第二方面的一种具体实现方式中，电池失效因子集合确定子模块可以具体用于：对案例库失效因子集合和故障树线下因子集合进行组合，得到线下因子集合；其中，故障树线下因子集合为故障树失效因子集合中的线下因子集合；对线下因子集合和故障树线上因子集合进行组合，得到电池失效因子集合；其中，故障树线上因子集合为故障树失效因子集合中的线上因子集合。
在第二方面的一种具体实现方式中，电池失效分析结果确定模块可以包括：
数据表构建子模块，用于按照电池失效因子集合构建电池失效案例的数据表；
目标模型构建子模块，用于根据数据表进行机器学习模型构建，得到构建后的目标模型；
数据特征重要性排序子模块，用于根据目标模型确定数据表中的数据特征重要性排序；
电池失效分析结果确定子模块，用于根据数据特征重要性排序确定电池失效分析结果。
在第二方面的一种具体实现方式中，电池失效分析结果确定模块还可以包括：
数据特征选型子模块，用于使用预设的数据特征选型方法在数据表中进行数据特征选型，得到选型后的数据表。
在第二方面的一种具体实现方式中，电池失效分析结果确定模块还可以包括：
数据清洗子模块，用于使用预设的数据清洗方法在数据表中进行数据清洗，得到清洗后的数据表。
在第二方面的一种具体实现方式中，电池失效分析装置还可以包括：
案例库及故障树更新模块，用于根据电池失效分析结果对电池失效案例库和电池失效故障树进行更新。
本申请实施例的第三方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述任一种电池失效分析方法的步骤。
本申请实施例的第四方面提供了一种电子设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述任一种电池失效分析方法的步骤。
本申请实施例的第五方面提供了一种计算机程序产品，当计算机程序产品在电子设备上运行时，使得电子设备执行上述任一种电池失效分析方法的步骤。
有益效果
本申请实施例获取待分析的电池失效案例；基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行分析，得到电池失效因子集合；根据电池失效因子集合确定电池失效分析结果。通过本申请实施例，可以结合电池失效案例库和电池失效故障树对电池失效案例进行综合分析，从而可以分析得到更加全面的电池失效因子集合，有效提高了最终的电池失效分析结果的准确度。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为本申请实施例中一种电池失效分析方法的一个实施例流程图；
图2为基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行分析的示意流程图；
图3为基于电池失效案例库对电池失效案例进行分析的示意流程图；
图4为根据电池失效因子集合确定电池失效分析结果的示意流程图；
图5为电子设备的输出结果的示意图；
图6为本申请实施例中一种电池失效分析装置的一个实施例结构图；
图7为本申请实施例中一种电子设备的示意框图。
本发明的实施方式
为使得本申请的发明目的、特征、优点能够更加的明显和易懂，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，下面所描述的实施例仅仅是本申请一部分实施例，而非全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本申请保护的范围。
应当理解，当在本说明书和所附权利要求书中使用时，术语“包括”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。
还应当理解，在此本申请说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本申请。如在本申请说明书和所附权利要求书中所使用的那样，除非上下文清楚地指明其它情况，否则单数形式的“一”、“一个”及“该”意在包括复数形式。
还应当进一步理解，在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合。
如在本说明书和所附权利要求书中所使用的那样，术语“如果”可以依据上下文被解释为“当...时”或“一旦”或“响应于确定”或“响应于检测到”。类似地，短语“如果确定”或“如果检测到[所描述条件或事件]”可以依据上下文被解释为意指“一旦确定”或“响应于确定”或“一旦检测到[所描述条件或事件]”或“响应于检测到[所描述条件或事件]”。
另外，在本申请的描述中，术语“第一”、“第二”、“第三”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
随着可再生能源和电动汽车的快速普及，锂离子电池等电池的需求正在急剧增长。然而，由于其生产过程中的复杂制造流程和物理化学反应，电池的失效问题一直是制约其性能和可靠性的主要因素。
电池失效可以包括很多方面，以其中的电池析锂为例，锂离子电池在充电时，锂离子从正极脱嵌并嵌入负极；但是当一些异常情况：如负极嵌锂空间不足、锂离子嵌入负极阻力太大、锂离子过快的从正极脱嵌但无法等量的嵌入负极等异常发生时，无法嵌入负极的 锂离子只能在负极表面得电子，从而形成银白色的金属锂单质，从而产生电池析锂的现象。
在现有的电池失效分析方法中，需要依靠大量的生产经验，配合物理和化学实验来识别和解决问题，虽然有时也能得到一些有用的分析结果，但整体而言分析不全面，分析结果的准确度较低，无法应对大规模生产中的挑战。
针对这一问题，本申请实施例可以结合电池失效案例库和电池失效故障树对电池失效案例进行综合分析，从而可以分析得到更加全面的电池失效因子集合，有效提高了最终的电池失效分析结果的准确度。
本申请实施例的执行主体可以包括但限于桌上型计算机、笔记本、掌上电脑、服务器等电子设备。
请参阅图1，本申请实施例中一种电池失效分析方法的一个实施例可以包括：
步骤S101、获取待分析的电池失效案例。
在本申请实施例的一种具体实现方式中，用户可以将待分析的电池失效案例输入至电子设备中的输入接口，电子设备可以从输入接口中获取电池失效案例，并对其进行后续的电池失效分析。
在本申请实施例的另一种具体实现方式中，用户可以将待分析的电池失效案例存储在预设的存储设备中，电子设备可以通过预先建立的数据传输链路从存储设备处获取其存储的电池失效案例，并对其进行后续的电池失效分析。
其中，该数据传输链路可以是基于无线局域网(Wireless Local Area Networks，WLAN)(例如，无线保真(Wireless Fidelity，WiFi))、蓝牙(bluetooth)、全球导航卫星系统(Global Navigation Satellite System，GNSS)、调频(Frequency Modulation，FM)、近距离无线通信技术(Near Field Communication，NFC)、红外技术(infrared，IR)、全球移动通讯系统(Global System for Mobile communications，GSM)，通用分组无线服务(General Packet Radio Service，GPRS)、码分多址接入(Code Division Multiple Access，CDMA)、宽带码分多址(Wideband Code Division Multiple Access，WCDMA)、时分码分多址(Time Division-Synchronous Code Division Multiple Access，TD-SCDMA)以及长期演进(Long Term Evolution，LTE)等至少一种通信解决方案建立起来的数据传输链路。
电池失效案例中可以包括但不限于失效描述和失效模式，以电池析锂的失效案例为例，失效描述可以包括但不限于失效节点、失效率、失效面、失效位置、析锂形貌、黑斑形貌、失效等级等描述维度；失效模式可以包括但不限于黑斑、大面析锂、拐角析锂等不同模式。
步骤S102、基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行 分析，得到电池失效因子集合。
故障树分析(Fault Tree Analysis，FTA)是一种逻辑演绎的分析工具，用描绘事故因果关系的有向逻辑树，分析事故的现象、原因及结果，从而找出预防事故的措施。FTA是系统安全工程中的重要的分析方法之一，可以对各种系统的危险性进行识别评价，既适用于定性分析，又能进行定量分析，具有简明、形象化的特点，体现了以系统工程方法研究安全问题的系统性、准确性和预测性。
在本申请实施例中，可以预先构建用于对电池失效进行FTA分析的故障树，将其记为电池失效故障树。其中，故障树的具体构建过程可以参照现有技术中的任意一种FTA方法，本申请实施例对此不作具体限定。
在本申请实施例中，可以预先构建用于对电池失效进行分析的历史案例库，将其记为电池失效案例库。其中，电池失效案例库中可以包括各个已完成电池失效分析的历史案例，每个历史案例预先标注了对应的失效根因。
为了提高电池失效分析的准确度，可以将FTA分析方法与案例库分析方法进行结合，对电池失效案例进行综合分析，从而得到更加全面完整的电池失效因子集合。
在本申请实施例的一种具体实现方式中，步骤S102具体可以包括如图2所示的过程：
步骤S1021、基于电池失效案例库对电池失效案例进行分析，得到案例库失效因子集合。
在本申请实施例的一种具体实现方式中，可以基于案例之间的相似度来从电池失效案例库中选取电池失效案例的相似案例，并根据相似案例来确定案例库失效因子集合。步骤S1021具体可以包括如图3所示的过程：
步骤S1021a、分别计算电池失效案例与电池失效案例库中的各个历史案例之间的案例相似度。
以电池失效案例库中的任意一个历史案例为例，将其记为目标历史案例，在本申请实施例的一种具体实现方式中，可以提取电池失效案例中的失效描述，将其作为第一案例特征，失效描述中的每个描述维度均作为其中的一个子特征；类似地，可以提取目标历史案例中的失效描述，将其作为第二案例特征，失效描述中的每个描述维度均作为其中的一个子特征。
在得到第一案例特征和第二案例特征之后，则可以根据第一案例特征和第二案例特征，计算电池失效案例和目标历史案例之间的案例相似度。
在本申请实施例的一种具体实现方式中，可以首先计算第一案例特征和第二案例特征之间的案例特征距离。
具体地，可以分别计算第一案例特征和第二案例特征的各个对应的子特征之间的子特征距离，并根据预设的子特征权重对各个子特征距离进行加权求和，得到案例特征距离。
其中，对于名义型的子特征，可以先对其进行One-Hot编码，再对编码后的数据进行余弦距离计算，从而得到相应的子特征距离。
对于数值型的子特征，可以对其进行绝对百分比误差(Absolute Percentage Error，APE)计算，从而得到相应的子特征距离。
需要说明的是，以上的距离计算方法仅为示例，在实际应用中，可以根据具体情况采用现有技术中的任意一种距离计算方法，本申请实施例对此不作具体限定。
对于不同的子特征，可以根据实际情况预先为其赋予相对应的权重，为较重要的子特征赋予较大的权重，为不重要的子特征赋予较小的权重。在得到各个子特征距离之后，可以根据这些预设的子特征权重对各个子特征距离进行加权求和，得到案例特征距离。通过这样的方式，可以强化较重要的子特征，弱化不重要的子特征，从而提高最终结果的准确度。
在得到案例特征距离之后，可以根据案例特征距离确定电池失效案例和目标历史案例之间的案例相似度。
其中，案例相似度与案例特征距离负相关，即电池失效案例和目标历史案例之间的案例特征距离越大，则电池失效案例和目标历史案例之间的案例相似度越小，反之，电池失效案例和目标历史案例之间的案例特征距离越小，则电池失效案例和目标历史案例之间的案例相似度越大。
在本申请实施例的一种具体实现方式中，可以将案例特征距离归一化至[0,1]的区间范围内，然后计算1与案例特征距离之差，将其作为电池失效案例和目标历史案例之间的案例相似度。
在本申请实施例的另一种具体实现方式中，可以计算案例特征距离的倒数，将其作为电池失效案例和目标历史案例之间的案例相似度。
通过计算案例特征之间的距离来表征案例之间的相似度，可以实现对案例相似度的精确度量。
步骤S1021b、从电池失效案例库中选取案例相似度大于预设的相似度阈值的历史案例作为电池失效案例的相似案例。
其中，相似度阈值的具体取值可以根据实际情况进行设置，例如，可以将相似度阈值设置为0.8、0.85、0.9、0.95或者其它取值，本申请实施例对此不作具体限定。
步骤S1021c、根据相似案例确定案例库失效因子集合。
电池失效案例库中的每个历史案例均预先标注了对应的失效根因，在从电池失效案例库中选取出电池失效案例的相似案例之后，可以获取相似案例的失效根因，并对失效根因进行汇总，从而得到案例库失效因子集合。
通过图3所示的过程，可以从电池失效案例库中选取相似案例，这些相似案例与电池失效案例之间的相似度较大，根据这些相似案例确定的案例库失效因子集合对于电池失效案例具有较好的参考意义。
在本申请实施例的一种具体实现方式中，还可以统计相似案例的失效根因的发生频率，并在案例库失效因子集合中将失效根因按照发生频率由高到低的顺序进行排序，即发生频率越高的失效根因，其排序越靠前，发生频率越低的失效根因，其排序越靠后，以提高后续的分析效率。
步骤S1022、基于电池失效故障树对电池失效案例进行分析，得到故障树失效因子集合。
在实际应用中，可以根据具体情况采用现有技术中的任意一种FTA方法来对电池失效案例进行分析，此处对此不作具体限定。
在本申请实施例的一种具体实现方式中，电池失效故障树中可以包括与每种失效模式分别对应的分支，例如，电池失效故障树可以包括但不限于与黑斑失效模式对应的分支、与大面析锂失效模式对应的分支、与拐角析锂失效模式对应的分支等。
在基于电池失效故障树对电池失效案例进行分析时，可以提取电池失效案例中的失效模式，并在电池失效故障树中确定与失效模式对应的分支，将其记为目标分支。后续则无需基于整个电池失效故障树，而只需基于目标分支对电池失效案例进行故障树分析，从而得到故障树失效因子集合。这样的方式具有更强的针对性，缩小了分析范围，有效提高了分析效率。
步骤S1023、根据案例库失效因子集合和故障树失效因子集合，确定电池失效因子集合。
由于导致电池失效的失效因子较多，可能有数百个以上，因此只会将其中较为重要的失效因子收录在预设的数据库中，为了便于区分，此处可以将已收录在数据库中的失效因子记为线上因子，将未收录在数据库中的失效因子记为线下因子。
案例库失效因子集合中的失效因子均为线下因子，故障树失效因子集合中的失效因子则包括线上因子和线下因子。为了便于区分，此处可以将故障树失效因子集合中的线下因子集合记为故障树线下因子集合，将故障树失效因子集合中的线上因子集合记为故障树线上因子集合。
对于故障树线上因子集合，可以分别计算其中每个失效因子的分析指标，其中，分析指标可以包括但不限于分布差异性、设备集中性、膜卷集中性、时间集中性等指标。
对案例库失效因子集合和故障树线下因子集合进行组合，可以得到线下因子集合。与线上因子类似，可以分别计算线下因子集合中每个失效因子的分析指标，其中，分析指标可以包括但不限于分布差异性、设备集中性、膜卷集中性、时间集中性等指标。
在确定出线下因子集合和故障树线上因子集合之后，可以对线下因子集合和故障树线上因子集合进行组合，从而得到电池失效因子集合。
通过图2所示的过程，可以基于电池失效案例库的分析得到案例库失效因子集合，基于电池失效故障树的分析得到故障树失效因子集合，通过对这两者的综合考虑，得到更加全面的电池失效因子集合。
步骤S103、根据电池失效因子集合确定电池失效分析结果。
在本申请实施例的一种具体实现方式中，可以采用机器学习的方式来进行电池失效分析，以提高整体的分析效率。步骤S103具体可以包括如图4所示的过程：
步骤S1031、按照电池失效因子集合构建电池失效案例的数据表。
对于电池失效案例中的所有数据，可以根据电池失效因子集合中的线上因子和线下因子按照一定的规则进行组合，从而构建得到电池失效案例的数据表。其中，具体的规则可以根据实际情况进行设置，例如，可以包括但不限于电芯条形码(barcode)匹配的规则、芯卷(JR)匹配的规则、膜卷匹配的规则等。
在本申请实施例的一种具体实现方式中，在构建出数据表之后，还可以使用预设的数据清洗方法在数据表中进行数据清洗，从而得到清洗后的数据表。
具体的数据清洗方法可以根据实际情况进行设置，本申请实施例对此不作具体限定。例如，可以包括但不限于：剔除数据表中没有差异的参数、剔除数据表中数据缺失超过设定阈值的参数、剔除包含缺失值的行或列(即dropna)等清洗操作。
通过预先对数据表进行数据清洗，可以清洗掉大量的无效数据，避免了其对分析过程的干扰，有效提高了最终的电池失效分析结果的准确度。
在本申请实施例的一种具体实现方式中，还可以使用预设的数据特征选型方法在数据表中进行数据特征选型，从而得到选型后的数据表。
具体的数据特征选型方法可以根据实际情况进行设置，本申请实施例对此不作具体限定。例如，可以包括但不限于：递归特征消除法、Selectfrommodel等特征选型方法。
通过预先使用数据特征选型，可以剔除掉对分类、回归效果较差的参数，降低数据的维度，从而提高分析效率。
步骤S1032、根据数据表进行机器学习模型构建，得到构建后的目标模型。
在得到数据表之后，可以将数据表中的数据输入至初始的机器学习模型中，进行分类或者回归的建模，从而得到最终的目标模型。
具体采用何种机器学习模型可以根据实际情况进行设置，本申请实施例对此不作具体限定。例如，可以包括但不限于：决策树、随机森林、XGBoost等模型。
步骤S1033、根据目标模型确定数据表中的数据特征重要性排序。
在得到目标模型之后，可以使用目标模型对数据表中的各个特征(即失效因子)进行重要性排序，以确定其对最终结果的影响程度。
具体地，对于决策树而言，可以根据特征在决策树中的节点分裂次数或信息增益来评估特征的重要性；对于随机森林而言，可以使用袋外数据(Out-of-Bag，OOB)误差来评估特征的重要性；对于XGBoost而言，可以使用特征在各个弱学习器上的平均分裂次数或特征对模型带来的损失减少量来评估特征的重要性。
步骤S1034、根据数据特征重要性排序确定电池失效分析结果。
在本申请实施例的一种具体实现方式中，电子设备在确定数据特征重要性排序之后，可以通过预设的人机交互界面将数据特征重要性排序进行输出，此外还可以结合分布差异性、设备集中性、膜卷集中性、时间集中性等指标进行展示。
用户可以基于电子设备的输出结果，通过人工检验的方式进行验证，修正得到最终的电池失效分析结果，并提出相对应的解决方案。
图5所示为电子设备的输出结果的示意图，如图所示，数据特征重要性排序为：失效因子A、失效因子B、失效因子C、失效因子D、失效因子E、失效因子F、……，用户通过人工检验的方式进行验证后，可以将其中的失效因子B确定为最终的电池失效分析结果，并将该电池失效分析结果通过人机交互界面输入至电子设备中。
在本申请实施例的一种具体实现方式中，电子设备在得到最终的电池失效分析结果之后，还可以根据电池失效分析结果对电池失效案例库和电池失效故障树进行更新。
具体地，可以将当前的电池失效案例作为新的历史案例添加入电池失效案例库，根据电池失效分析结果标注其对应的失效根因；还可以根据电池失效分析结果将电池失效故障树中缺失的失效因子进行反馈和补全，从而实现了分析过程的闭环，不断丰富电池失效案例库和电池失效故障树，为后续的电池失效案例分析提供了更加完备的基础。
综上所述，本申请实施例获取待分析的电池失效案例；基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行分析，得到电池失效因子集合；根据电池失效因子集合确定电池失效分析结果。通过本申请实施例，可以结合电池失效案例库和电池失 效故障树对电池失效案例进行综合分析，从而可以分析得到更加全面的电池失效因子集合，有效提高了最终的电池失效分析结果的准确度。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
对应于上文实施例所述的一种电池失效分析方法，图6示出了本申请实施例提供的一种电池失效分析装置的一个实施例结构图。
本实施例中，一种电池失效分析装置可以包括：
电池失效案例获取模块601，用于获取待分析的电池失效案例；
电池失效分析模块602，用于基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行分析，得到电池失效因子集合；
电池失效分析结果确定模块603，用于根据电池失效因子集合确定电池失效分析结果。
在本申请实施例的一种具体实现方式中，电池失效分析模块可以包括：
电池失效案例库分析子模块，用于基于电池失效案例库对电池失效案例进行分析，得到案例库失效因子集合；
电池失效故障树分析子模块，用于基于电池失效故障树对电池失效案例进行分析，得到故障树失效因子集合；
电池失效因子集合确定子模块，用于根据案例库失效因子集合和故障树失效因子集合，确定电池失效因子集合。
在本申请实施例的一种具体实现方式中，电池失效案例库分析子模块可以包括：
案例相似度计算单元，用于分别计算电池失效案例与电池失效案例库中的各个历史案例之间的案例相似度；
相似案例选取单元，用于从电池失效案例库中选取案例相似度大于预设的相似度阈值的历史案例作为电池失效案例的相似案例；
案例库失效因子集合确定单元，用于根据相似案例确定案例库失效因子集合。
在本申请实施例的一种具体实现方式中，案例相似度计算单元可以包括：
第一案例特征提取子单元，用于提取电池失效案例中的第一案例特征；
第二案例特征提取子单元，用于提取目标历史案例中的第二案例特征；其中，目标历史案例为电池失效案例库中的任意一个历史案例；
案例相似度计算子单元，用于根据第一案例特征和第二案例特征，计算电池失效案例和目标历史案例之间的案例相似度。
在本申请实施例的一种具体实现方式中，案例相似度计算子单元可以具体用于：计算 第一案例特征和第二案例特征之间的案例特征距离；根据案例特征距离确定电池失效案例和目标历史案例之间的案例相似度。
在本申请实施例的一种具体实现方式中，案例相似度计算子单元可以具体用于：分别计算第一案例特征和第二案例特征的各个对应的子特征之间的子特征距离；根据预设的子特征权重对各个子特征距离进行加权求和，得到案例特征距离。
在本申请实施例的一种具体实现方式中，案例库失效因子集合确定单元可以具体用于：获取相似案例的失效根因；对失效根因进行汇总，得到案例库失效因子集合。
在本申请实施例的一种具体实现方式中，电池失效故障树分析子模块可以具体用于：提取电池失效案例中的失效模式；在电池失效故障树中确定与失效模式对应的目标分支；基于目标分支对电池失效案例进行故障树分析，得到故障树失效因子集合。
在本申请实施例的一种具体实现方式中，电池失效因子集合确定子模块可以具体用于：对案例库失效因子集合和故障树线下因子集合进行组合，得到线下因子集合；其中，故障树线下因子集合为故障树失效因子集合中的线下因子集合；对线下因子集合和故障树线上因子集合进行组合，得到电池失效因子集合；其中，故障树线上因子集合为故障树失效因子集合中的线上因子集合。
在本申请实施例的一种具体实现方式中，电池失效分析结果确定模块可以包括：
数据表构建子模块，用于按照电池失效因子集合构建电池失效案例的数据表；
目标模型构建子模块，用于根据数据表进行机器学习模型构建，得到构建后的目标模型；
数据特征重要性排序子模块，用于根据目标模型确定数据表中的数据特征重要性排序；
电池失效分析结果确定子模块，用于根据数据特征重要性排序确定电池失效分析结果。
在本申请实施例的一种具体实现方式中，电池失效分析结果确定模块还可以包括：
数据特征选型子模块，用于使用预设的数据特征选型方法在数据表中进行数据特征选型，得到选型后的数据表。
在本申请实施例的一种具体实现方式中，电池失效分析结果确定模块还可以包括：
数据清洗子模块，用于使用预设的数据清洗方法在数据表中进行数据清洗，得到清洗后的数据表。
在本申请实施例的一种具体实现方式中，电池失效分析装置还可以包括：
案例库及故障树更新模块，用于根据电池失效分析结果对电池失效案例库和电池失效故障树进行更新。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的装置，模 块和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
图7示出了本申请实施例提供的一种电子设备的示意框图，为了便于说明，仅示出了与本申请实施例相关的部分。
如图7所示，该实施例的电子设备7包括：处理器70、存储器71以及存储在所述存储器71中并可在所述处理器70上运行的计算机程序72。所述处理器70执行所述计算机程序72时实现上述各个电池失效分析方法实施例中的步骤，例如图1所示的步骤S101至步骤S103。或者，所述处理器70执行所述计算机程序72时实现上述各装置实施例中各模块/单元的功能，例如图6所示模块601至模块603的功能。
示例性的，所述计算机程序72可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器71中，并由所述处理器70执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序72在所述电子设备7中的执行过程。
所述电子设备7可以是桌上型计算机、笔记本、掌上电脑、服务器等计算设备。本领域技术人员可以理解，图7仅仅是电子设备7的示例，并不构成对电子设备7的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述电子设备7还可以包括输入输出设备、网络接入设备、总线等。
所述处理器70可以是中央处理单元(Central Processing Unit，CPU)，还可以是其它通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其它可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器71可以是所述电子设备7的内部存储单元，例如电子设备7的硬盘或内存。所述存储器71也可以是所述电子设备7的外部存储设备，例如所述电子设备7上配备的插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。进一步地，所述存储器71还可以既包括所述电子设备7的内部存储单元也包括外部存储设备。所述存储器71用于存储所述计算机程序以及所述电子设备7所需的其它程序和数据。所述存储器71还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单 元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/电子设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/电子设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各 个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读存储介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读存储介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读存储介质不包括电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (16)

  1. 一种电池失效分析方法，其中，包括：
    获取待分析的电池失效案例；
    基于预设的电池失效案例库和预设的电池失效故障树对所述电池失效案例进行分析，得到电池失效因子集合；
    根据所述电池失效因子集合确定电池失效分析结果。
  2. 根据权利要求1所述的电池失效分析方法，其中，所述基于预设的电池失效案例库和预设的电池失效故障树对所述电池失效案例进行分析，得到电池失效因子集合，包括：
    基于所述电池失效案例库对所述电池失效案例进行分析，得到案例库失效因子集合；
    基于所述电池失效故障树对所述电池失效案例进行分析，得到故障树失效因子集合；
    根据所述案例库失效因子集合和所述故障树失效因子集合，确定所述电池失效因子集合。
  3. 根据权利要求2所述的电池失效分析方法，其中，所述基于所述电池失效案例库对所述电池失效案例进行分析，得到案例库失效因子集合，包括：
    分别计算所述电池失效案例与所述电池失效案例库中的各个历史案例之间的案例相似度；
    从所述电池失效案例库中选取案例相似度大于预设的相似度阈值的历史案例作为所述电池失效案例的相似案例；
    根据所述相似案例确定所述案例库失效因子集合。
  4. 根据权利要求3所述的电池失效分析方法，其中，所述分别计算所述电池失效案例与所述电池失效案例库中的各个历史案例之间的案例相似度，包括：
    提取所述电池失效案例中的第一案例特征；
    提取目标历史案例中的第二案例特征；其中，所述目标历史案例为所述电池失效案例库中的任意一个历史案例；
    根据所述第一案例特征和所述第二案例特征，计算所述电池失效案例和所述目标历史案例之间的案例相似度。
  5. 根据权利要求4所述的电池失效分析方法，其中，所述根据所述第一案例特征和所述第二案例特征，计算所述电池失效案例和所述目标历史案例之间的案例相似度，包括：
    计算所述第一案例特征和所述第二案例特征之间的案例特征距离；
    根据所述案例特征距离确定所述电池失效案例和所述目标历史案例之间的案例相似 度。
  6. 根据权利要求5所述的电池失效分析方法，其中，所述计算所述第一案例特征和所述第二案例特征之间的案例特征距离，包括：
    分别计算所述第一案例特征和所述第二案例特征的各个对应的子特征之间的子特征距离；
    根据预设的子特征权重对各个子特征距离进行加权求和，得到所述案例特征距离。
  7. 根据权利要求3至6中任一项所述的电池失效分析方法，其中，所述根据所述相似案例确定所述案例库失效因子集合，包括：
    获取所述相似案例的失效根因；
    对所述失效根因进行汇总，得到所述案例库失效因子集合。
  8. 根据权利要求2至7中任一项所述的电池失效分析方法，其中，所述基于所述电池失效故障树对所述电池失效案例进行分析，得到故障树失效因子集合，包括：
    提取所述电池失效案例中的失效模式；
    在所述电池失效故障树中确定与所述失效模式对应的目标分支；
    基于所述目标分支对所述电池失效案例进行故障树分析，得到所述故障树失效因子集合。
  9. 根据权利要求2至8中任一项所述的电池失效分析方法，其中，所述根据所述案例库失效因子集合和所述故障树失效因子集合，确定所述电池失效因子集合，包括：
    对所述案例库失效因子集合和故障树线下因子集合进行组合，得到线下因子集合；其中，所述故障树线下因子集合为所述故障树失效因子集合中的线下因子集合；
    对所述线下因子集合和所述故障树线上因子集合进行组合，得到所述电池失效因子集合；其中，所述故障树线上因子集合为所述故障树失效因子集合中的线上因子集合。
  10. 根据权利要求1至9中任一项所述的电池失效分析方法，其中，所述根据所述电池失效因子集合确定电池失效分析结果，包括：
    按照所述电池失效因子集合构建所述电池失效案例的数据表；
    根据所述数据表进行机器学习模型构建，得到构建后的目标模型；
    根据所述目标模型确定所述数据表中的数据特征重要性排序；
    根据所述数据特征重要性排序确定所述电池失效分析结果。
  11. 根据权利要求10所述的电池失效分析方法，其中，在根据所述数据表进行机器学习模型构建之前，还包括：
    使用预设的数据特征选型方法在所述数据表中进行数据特征选型，得到选型后的所述 数据表。
  12. 根据权利要求10或11所述的电池失效分析方法，其中，在根据所述数据表进行机器学习模型构建之前，还包括：
    使用预设的数据清洗方法在所述数据表中进行数据清洗，得到清洗后的所述数据表。
  13. 根据权利要求1至12中任一项所述的电池失效分析方法，其中，在根据所述电池失效因子集合确定电池失效分析结果之后，还包括：
    根据所述电池失效分析结果对所述电池失效案例库和所述电池失效故障树进行更新。
  14. 一种电池失效分析装置，其中，包括：
    电池失效案例获取模块，用于获取待分析的电池失效案例；
    电池失效分析模块，用于基于预设的电池失效案例库和预设的电池失效故障树对电池失效案例进行分析，得到电池失效因子集合；
    电池失效分析结果确定模块，用于根据电池失效因子集合确定电池失效分析结果。
  15. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其中，所述计算机程序被处理器执行时实现如权利要求1至13中任一项所述的电池失效分析方法的步骤。
  16. 一种电子设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现如权利要求1至13中任一项所述的电池失效分析方法的步骤。
PCT/CN2024/092256 2023-11-27 2024-05-10 一种电池失效分析方法、装置、可读存储介质及电子设备 Pending WO2025112296A1 (zh)

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