IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i11p5147-d1671299.html
   My bibliography  Save this article

A Connectivity-Based Outlier Factor Method for Rapid Battery Internal Short-Circuit Diagnosis

Author

Listed:
  • Zhiguo Dong

    (National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China)

  • Gongqiang Li

    (National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China)

  • Fengxiang Xie

    (National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China)

  • Shiwen Zhao

    (School of Control Science and Engineering, Shandong University, Jinan 250100, China)

  • Xiaofan Ji

    (National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China)

  • Mofan Tian

    (National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China)

  • Kailong Liu

    (School of Control Science and Engineering, Shandong University, Jinan 250100, China)

Abstract

Internal short-circuit (ISC) is a critical failure mode in lithium-ion (Li-ion) batteries that can trigger thermal runaway and pose serious risks to both environmental and human safety. Early-stage ISC faults are particularly challenging to detect due to their subtle characteristics and the masking effects of voltage fluctuations. To address these challenges, this study proposes a rapid and accurate ISC diagnosis method based on the connectivity-based outlier factor (COF) algorithm. The key innovation lies in the preprocessing of terminal voltage to amplify fault signatures and suppress natural fluctuations, thereby enhancing sensitivity to early anomalies. The COF algorithm is then applied to identify ISC faults in real time. Validation under urban-dynamometer driving schedule (UDDS) conditions demonstrates the method’s effectiveness: it successfully detects early ISC faults with an equivalent resistance as high as 100 Ω within 207 s of onset. This unsupervised, data-driven approach improves fault detection speed and accuracy, contributing to the advancement of safe, reliable, and sustainable LIB deployment in clean energy and transportation systems.

Suggested Citation

  • Zhiguo Dong & Gongqiang Li & Fengxiang Xie & Shiwen Zhao & Xiaofan Ji & Mofan Tian & Kailong Liu, 2025. "A Connectivity-Based Outlier Factor Method for Rapid Battery Internal Short-Circuit Diagnosis," Sustainability, MDPI, vol. 17(11), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5147-:d:1671299
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/11/5147/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/11/5147/
    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:jsusta:v:17:y:2025:i:11:p:5147-:d:1671299. 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.