YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Electrochemical Energy Conversion and Storage
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Electrochemical Energy Conversion and Storage
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    State of Health Estimation Method for Lithium-Ion Batteries Based on Multifeature Fusion and BO-BiGRU Model

    Source: Journal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 004::page 41001-1
    Author:
    Zhu, Junchao
    ,
    Zhang, Jun
    ,
    Kang, Jian
    ,
    Liu, ChengZhi
    ,
    Chen, Hua
    ,
    Wu, Tiezhou
    DOI: 10.1115/1.4066872
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The state of health (SOH) of lithium-ion batteries is a crucial parameter for assessing battery degradation. The aim of this study is to solve the problems of single extraction of health features (HFs) and redundancy of information between features in the SOH estimation. This article develops an SOH estimation method for lithium-ion batteries based on multifeature fusion and Bayesian optimization (BO)-bidirectional gated recurrent unit (BiGRU) model. First, a total of eight HFs in three categories, namely, time, energy, and probability, can be extracted from the charging data to accurately describe the aging mechanism of the battery. The Pearson and Spearman analysis method verified the strong correlation between HFs and SOH. Second, the multiple principal components obtained by kernel principal component analysis (KPCA) can eliminate the redundancy of information between HFs. The principal component with the highest correlation with SOH is selected by bicorrelation analysis to be defined as the fused HF. Finally, to improve SOH estimation accuracy, the BO-BiGRU model is proposed. The proposed method is validated using battery datasets from NASA. The results show that the SOH estimation accuracy of the BO-BiGRU model proposed in this article is high, while mean absolute error (MAE) is lower than 1.2%. In addition, the SOH of the lithium battery is estimated using different proportions of test sets, and the results show that the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of the SOH remain within 3%, with high estimation accuracy and robustness.
    • Download: (1.768Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      State of Health Estimation Method for Lithium-Ion Batteries Based on Multifeature Fusion and BO-BiGRU Model

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4305235
    Collections
    • Journal of Electrochemical Energy Conversion and Storage

    Show full item record

    contributor authorZhu, Junchao
    contributor authorZhang, Jun
    contributor authorKang, Jian
    contributor authorLiu, ChengZhi
    contributor authorChen, Hua
    contributor authorWu, Tiezhou
    date accessioned2025-04-21T09:58:43Z
    date available2025-04-21T09:58:43Z
    date copyright10/30/2024 12:00:00 AM
    date issued2024
    identifier issn2381-6872
    identifier otherjeecs_22_4_041001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305235
    description abstractThe state of health (SOH) of lithium-ion batteries is a crucial parameter for assessing battery degradation. The aim of this study is to solve the problems of single extraction of health features (HFs) and redundancy of information between features in the SOH estimation. This article develops an SOH estimation method for lithium-ion batteries based on multifeature fusion and Bayesian optimization (BO)-bidirectional gated recurrent unit (BiGRU) model. First, a total of eight HFs in three categories, namely, time, energy, and probability, can be extracted from the charging data to accurately describe the aging mechanism of the battery. The Pearson and Spearman analysis method verified the strong correlation between HFs and SOH. Second, the multiple principal components obtained by kernel principal component analysis (KPCA) can eliminate the redundancy of information between HFs. The principal component with the highest correlation with SOH is selected by bicorrelation analysis to be defined as the fused HF. Finally, to improve SOH estimation accuracy, the BO-BiGRU model is proposed. The proposed method is validated using battery datasets from NASA. The results show that the SOH estimation accuracy of the BO-BiGRU model proposed in this article is high, while mean absolute error (MAE) is lower than 1.2%. In addition, the SOH of the lithium battery is estimated using different proportions of test sets, and the results show that the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of the SOH remain within 3%, with high estimation accuracy and robustness.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleState of Health Estimation Method for Lithium-Ion Batteries Based on Multifeature Fusion and BO-BiGRU Model
    typeJournal Paper
    journal volume22
    journal issue4
    journal titleJournal of Electrochemical Energy Conversion and Storage
    identifier doi10.1115/1.4066872
    journal fristpage41001-1
    journal lastpage41001-13
    page13
    treeJournal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 004
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian