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

    Combining Multi-Indirect Features Extraction and Optimized Gaussian Process Regression Algorithm for Online State of Health Estimation of Lithium-Ion Batteries

    Source: Journal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 003::page 31009-1
    Author:
    Lin, Chunsong
    ,
    Tuo, Xianguo
    ,
    Wu, Longxing
    ,
    Zhang, Guiyu
    ,
    Lyu, Zhiqiang
    ,
    Zeng, Xiangling
    DOI: 10.1115/1.4066636
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: With the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitoring of LIBs has emerged as critical to safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial application and computational cost still need to be improved further. In contrast, this article carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature, and other data that can be obtained online. Then, the optimized Gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, by combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum MAPE of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this article is helpful for realizing online SOH estimation in battery management systems.
    • Download: (2.345Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Combining Multi-Indirect Features Extraction and Optimized Gaussian Process Regression Algorithm for Online State of Health Estimation of Lithium-Ion Batteries

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4305599
    Collections
    • Journal of Electrochemical Energy Conversion and Storage

    Show full item record

    contributor authorLin, Chunsong
    contributor authorTuo, Xianguo
    contributor authorWu, Longxing
    contributor authorZhang, Guiyu
    contributor authorLyu, Zhiqiang
    contributor authorZeng, Xiangling
    date accessioned2025-04-21T10:09:05Z
    date available2025-04-21T10:09:05Z
    date copyright10/16/2024 12:00:00 AM
    date issued2024
    identifier issn2381-6872
    identifier otherjeecs_22_3_031009.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305599
    description abstractWith the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitoring of LIBs has emerged as critical to safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial application and computational cost still need to be improved further. In contrast, this article carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature, and other data that can be obtained online. Then, the optimized Gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, by combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum MAPE of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this article is helpful for realizing online SOH estimation in battery management systems.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleCombining Multi-Indirect Features Extraction and Optimized Gaussian Process Regression Algorithm for Online State of Health Estimation of Lithium-Ion Batteries
    typeJournal Paper
    journal volume22
    journal issue3
    journal titleJournal of Electrochemical Energy Conversion and Storage
    identifier doi10.1115/1.4066636
    journal fristpage31009-1
    journal lastpage31009-17
    page17
    treeJournal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 003
    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