IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i24p8492-d704036.html
   My bibliography  Save this article

A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems

Author

Listed:
  • Chao Li

    (Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8058, USA)

  • Assimina A. Pelegri

    (Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8058, USA)

Abstract

Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.

Suggested Citation

  • Chao Li & Assimina A. Pelegri, 2021. "A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems," Energies, MDPI, vol. 14(24), pages 1-24, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8492-:d:704036
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/24/8492/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/24/8492/
    Download Restriction: no
    ---><---

    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:gam:jeners:v:14:y:2021:i:24:p:8492-:d:704036. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.