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    A Fault Diagnosis Method for Lithium Batteries Based on Optimal Variational Modal Decomposition and Dimensionless Feature Parameters

    Source: Journal of Electrochemical Energy Conversion and Storage:;2022:;volume( 020 ):;issue: 003::page 31004
    Author:
    Chang, Chun;Tao, Chen;Wang, Shaojin;Zhang, Ruhang;Tian, Aina;Jiang, Jiuchun
    DOI: 10.1115/1.4055536
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Due to the frequent occurrence of electric vehicles safety accidents caused by battery system failures, in order to ensure the normal operation of the vehicle, it is crucial to do a fault diagnosis of the electric vehicle lithium battery. This paper presents a fault diagnosis method for lithium batteries based on optimal variational modal decomposition and dimensionless feature parameters for identifying faulty batteries. The method first preprocesses the voltage signal of a lithium battery by optimal variable mode decomposition to obtain the high and lowfrequency components of the signal and reconstructs the high and lowfrequency components. Then, the dimensionless feature parameters are extracted according to the reconstructed signal, and feature reduction of the dimensionless feature parameters is carried out by a locally linear embedding algorithm. Finally, a local outlier factor algorithm is used to detect faulty batteries. After verified by the operation data before the real electric vehicle's thermal runaway failure, this method can detect the faulty battery timely and accurately.
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      A Fault Diagnosis Method for Lithium Batteries Based on Optimal Variational Modal Decomposition and Dimensionless Feature Parameters

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4288734
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    • Journal of Electrochemical Energy Conversion and Storage

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    contributor authorChang, Chun;Tao, Chen;Wang, Shaojin;Zhang, Ruhang;Tian, Aina;Jiang, Jiuchun
    date accessioned2023-04-06T12:54:16Z
    date available2023-04-06T12:54:16Z
    date copyright10/3/2022 12:00:00 AM
    date issued2022
    identifier issn23816872
    identifier otherjeecs_20_3_031004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288734
    description abstractDue to the frequent occurrence of electric vehicles safety accidents caused by battery system failures, in order to ensure the normal operation of the vehicle, it is crucial to do a fault diagnosis of the electric vehicle lithium battery. This paper presents a fault diagnosis method for lithium batteries based on optimal variational modal decomposition and dimensionless feature parameters for identifying faulty batteries. The method first preprocesses the voltage signal of a lithium battery by optimal variable mode decomposition to obtain the high and lowfrequency components of the signal and reconstructs the high and lowfrequency components. Then, the dimensionless feature parameters are extracted according to the reconstructed signal, and feature reduction of the dimensionless feature parameters is carried out by a locally linear embedding algorithm. Finally, a local outlier factor algorithm is used to detect faulty batteries. After verified by the operation data before the real electric vehicle's thermal runaway failure, this method can detect the faulty battery timely and accurately.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Fault Diagnosis Method for Lithium Batteries Based on Optimal Variational Modal Decomposition and Dimensionless Feature Parameters
    typeJournal Paper
    journal volume20
    journal issue3
    journal titleJournal of Electrochemical Energy Conversion and Storage
    identifier doi10.1115/1.4055536
    journal fristpage31004
    journal lastpage3100410
    page10
    treeJournal of Electrochemical Energy Conversion and Storage:;2022:;volume( 020 ):;issue: 003
    contenttypeFulltext
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