IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v402y2025ipas0306261925015405.html

Real-time lithium plating onset detection for lithium-ion batteries via dynamic impedance spectra analysis

Author

Listed:
  • Du, Xinghao
  • Meng, Jinhao
  • Amirat, Yassine
  • Gao, Fei
  • Benbouzid, Mohamed

Abstract

Real-time lithium plating detection is essential for ensuring lithium-ion batteries’ safety and longevity. While impedance analysis offers valuable insights into the lithium plating process, the plating detection efficacy is often limited by overlapping electrochemical-thermal phenomena, complicating the extraction of plating-specific impedance features. To overcome this challenge, this work proposes a dynamic impedance tracking framework based on a specifically designed equivalent circuit model (ECM), enabling real-time observation of interfacial electrochemical dynamics. An adaptive lithium plating detection framework further enhances accuracy by employing statistical thresholding to distinguish plating-induced impedance variations from normal operational fluctuations. A consistency metric is formulated to quantitatively assess the proposed method’s performance across diverse charging rates and thermal conditions. Experimental validation demonstrates the proposed method’s superior sensitivity and robustness compared to three conventional impedance-based plating indicators.

Suggested Citation

  • Du, Xinghao & Meng, Jinhao & Amirat, Yassine & Gao, Fei & Benbouzid, Mohamed, 2025. "Real-time lithium plating onset detection for lithium-ion batteries via dynamic impedance spectra analysis," Applied Energy, Elsevier, vol. 402(PA).
  • Handle: RePEc:eee:appene:v:402:y:2025:i:pa:s0306261925015405
    DOI: 10.1016/j.apenergy.2025.126810
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925015405
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2025.126810?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Xie, Yi & Guo, Weijie & Zhou, Tingshun & Li, Wei & Fan, Yining & Yang, Rui & Zhang, Yangjun, 2025. "An impedance-based electro-thermal model integrated with in-situ lithium-plating criterion for AC heating at low temperatures," Applied Energy, Elsevier, vol. 391(C).
    2. Shen, Yudong & Wang, Xueyuan & Jiang, Zhao & Luo, Bingyin & Chen, Daidai & Wei, Xuezhe & Dai, Haifeng, 2024. "Online detection of lithium plating onset during constant and multistage constant current fast charging for lithium-ion batteries," Applied Energy, Elsevier, vol. 370(C).
    3. Wang, Peng & Xiong, Rui & Shen, Weixiang & Sun, Fengchun, 2025. "Aging-induced, rate-independent Lithium plating: A complete mechanism analysis throughout the battery lifecycle," Applied Energy, Elsevier, vol. 393(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chang, Chun & Li, Xinqi & Sun, Yuhui & Jiang, Jiuchun & Tian, Aina & Lv, Lu & Gao, Yang, 2025. "Force-signal driven real-time lithium plating detection in mechanically constrained LiFePO4 pouch cells," Energy, Elsevier, vol. 323(C).
    2. Zhang, Zhihang & Wang, Hewu & Lu, Languang & Li, Yalun & Xu, Wenqiang & Liu, Haoran & Li, Desheng & Ouyang, Minggao, 2025. "State-of-charge and capacity estimation for MWh-scale LiFePO4 peak-shaving battery energy storage stations based on real-world operating data," Energy, Elsevier, vol. 339(C).
    3. Yuan, Yuebo & Wang, Hewu & Sun, Yukun & Han, Xuebing & Zhu, Cheng & Ouyang, Minggao, 2025. "The influence of local lithium plating on battery safety and a novel detection method," Energy, Elsevier, vol. 321(C).
    4. Xiong, Xin & Wang, Yujie & Jiang, Cong & Sun, Zhendong & Chen, Zonghai, 2025. "Multi-physics data and model feature fusion for lithium-ion battery capacity estimation by transformer-based deep learning," Energy, Elsevier, vol. 335(C).

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:appene:v:402:y:2025:i:pa:s0306261925015405. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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/405891/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.