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    A Fault Diagnosis Method for Electric Vehicle Lithium Power Batteries Based on Dual-Feature Extraction From the Time and Frequency Domains

    Source: Journal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 003::page 31008-1
    Author:
    Chang, Chun
    ,
    Dai, Jiuhe
    ,
    Pan, Yaliang
    ,
    Lv, Lu
    ,
    Gao, Yang
    ,
    Jiang, Jiuchun
    DOI: 10.1115/1.4066479
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This study focuses on the safety and reliability issues of lithium-ion batteries, proposing a fault diagnosis strategy that leverages dual-feature extraction from both the time and frequency domains. Additionally, by modifying the traditional autoencoder, the study proposes a feature-guided autoencoder as an unsupervised model for extracting features in the time domain. Initially, wavelet packet decomposition and its energy-denoising treatment are employed to refine fault information within battery voltage signals. Subsequently, the reconstruction error outputted by the Feature-Guided Autoencoder is utilized as the time-domain fault feature, while the cosine similarity of the energy of signals in various frequency bands obtained after wavelet packet decomposition serves as the frequency-domain fault feature. Ultimately, this article selects the Isolation Forest algorithm for two-dimensional outlier detection of time and frequency features. Experimental results demonstrate that the feature-guided autoencoder proposed in this study not only enhances the sensitivity of time-domain fault features compared to traditional autoencoders and their variants but also optimizes issues related to training time and computational load. The effectiveness of the proposed dual-feature fault diagnosis method in both the time and frequency domains is validated through data from two actual vehicles, showing superior early fault detection capability relative to single-feature fault diagnosis methods.
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      A Fault Diagnosis Method for Electric Vehicle Lithium Power Batteries Based on Dual-Feature Extraction From the Time and Frequency Domains

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4305369
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    • Journal of Electrochemical Energy Conversion and Storage

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    contributor authorChang, Chun
    contributor authorDai, Jiuhe
    contributor authorPan, Yaliang
    contributor authorLv, Lu
    contributor authorGao, Yang
    contributor authorJiang, Jiuchun
    date accessioned2025-04-21T10:02:25Z
    date available2025-04-21T10:02:25Z
    date copyright10/7/2024 12:00:00 AM
    date issued2024
    identifier issn2381-6872
    identifier otherjeecs_22_3_031008.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305369
    description abstractThis study focuses on the safety and reliability issues of lithium-ion batteries, proposing a fault diagnosis strategy that leverages dual-feature extraction from both the time and frequency domains. Additionally, by modifying the traditional autoencoder, the study proposes a feature-guided autoencoder as an unsupervised model for extracting features in the time domain. Initially, wavelet packet decomposition and its energy-denoising treatment are employed to refine fault information within battery voltage signals. Subsequently, the reconstruction error outputted by the Feature-Guided Autoencoder is utilized as the time-domain fault feature, while the cosine similarity of the energy of signals in various frequency bands obtained after wavelet packet decomposition serves as the frequency-domain fault feature. Ultimately, this article selects the Isolation Forest algorithm for two-dimensional outlier detection of time and frequency features. Experimental results demonstrate that the feature-guided autoencoder proposed in this study not only enhances the sensitivity of time-domain fault features compared to traditional autoencoders and their variants but also optimizes issues related to training time and computational load. The effectiveness of the proposed dual-feature fault diagnosis method in both the time and frequency domains is validated through data from two actual vehicles, showing superior early fault detection capability relative to single-feature fault diagnosis methods.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Fault Diagnosis Method for Electric Vehicle Lithium Power Batteries Based on Dual-Feature Extraction From the Time and Frequency Domains
    typeJournal Paper
    journal volume22
    journal issue3
    journal titleJournal of Electrochemical Energy Conversion and Storage
    identifier doi10.1115/1.4066479
    journal fristpage31008-1
    journal lastpage31008-14
    page14
    treeJournal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 003
    contenttypeFulltext
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