IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v286y2024ics0360544223028566.html
   My bibliography  Save this article

An intelligent fusion estimation method for state of charge estimation of lithium-ion batteries

Author

Listed:
  • Cheng, Xingqun
  • Liu, Xiaolong
  • Li, Xinxin
  • Yu, Quanqing

Abstract

The state of charge estimation of lithium-ion batteries in electric vehicles is critical in battery management systems. Using a single model and algorithm for state of charge estimation has some limitations, therefore fusion algorithms are investigated by researchers. However, among many fusion methods, there are no basis for judgement concerning which models and algorithms to be chosen for fusion in different discharge intervals in order to obtain the desired results. To address this problem, this study proposes a three-interval fusion method for state of charge estimation based on fitness values. Firstly, three different types of equivalent circuit models and six filtering algorithms are built in this study. Next, the whole state of charge is divided into three intervals in discharging process and the estimation error characteristics of each interval are extracted. Then, assessment of model-algorithm fits through fitness values. Finally, the combinations of better fitness values in each interval are selected and the three-interval fusion method for estimation of state of charge is achieved by adaptive weighted average. The results show that the maximum absolute error of the three-interval fusion method is less than 0.5 % and the accuracy is improved by 40 % compared to other fusion methods.

Suggested Citation

  • Cheng, Xingqun & Liu, Xiaolong & Li, Xinxin & Yu, Quanqing, 2024. "An intelligent fusion estimation method for state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223028566
    DOI: 10.1016/j.energy.2023.129462
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223028566
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129462?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:energy:v:286:y:2024:i:c:s0360544223028566. 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.journals.elsevier.com/energy .

    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.