Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v202y2024ics1364032124003733.html
   My bibliography  Save this article

Machine learning for full lifecycle management of lithium-ion batteries

Author

Listed:
  • Zhai, Qiangxiang
  • Jiang, Hongmin
  • Long, Nengbing
  • Kang, Qiaoling
  • Meng, Xianhe
  • Zhou, Mingjiong
  • Yan, Lijing
  • Ma, Tingli
Abstract
Developing advanced battery materials, monitoring and predicting the health status of batteries, and effectively managing retired batteries are crucial for accelerating the closure of the whole industrial chain of power lithium-ion batteries for electric vehicles. Machine learning technology plays a vital role in the research, production, service, and retirement of lithium-ion batteries due to its robust learning and predictive capabilities. While there have been detailed and valuable reviews on this topic, a comprehensive summary of machine learning progress from the perspective of full lifecycle management of lithium-ion batteries is still lacking. This review divides the full lifecycle of lithium-ion batteries into three stages: pre-prediction, mid-prediction, and late prediction phases, and summarizes recent advances in different machine learning methods categorized as materials screening, life prediction, and cascade utilization. It also emphasizes the implementation and evaluation of hybrid machine learning models. Finally, potential research opportunities and future directions are presented, mainly from two aspects of battery databases and the application of large-scale time-series models. This review provides valuable guidance and reference for researchers and practitioners to broaden the scope of machine learning for its application in lithium-ion batteries.

Suggested Citation

  • Zhai, Qiangxiang & Jiang, Hongmin & Long, Nengbing & Kang, Qiaoling & Meng, Xianhe & Zhou, Mingjiong & Yan, Lijing & Ma, Tingli, 2024. "Machine learning for full lifecycle management of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:rensus:v:202:y:2024:i:c:s1364032124003733
    DOI: 10.1016/j.rser.2024.114647
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032124003733
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2024.114647?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:202:y:2024:i:c:s1364032124003733. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.