IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v389y2025ics0306261925004659.html
   My bibliography  Save this article

Investigation of lithium-ion battery degradation by corrected differential voltage analysis based on reference electrode

Author

Listed:
  • Xu, Wentao
  • Zhu, Jiangong
  • Zhang, Jie
  • Tian, Mengshu
  • Cai, Jixiang
  • Wu, Hang
  • Wei, Gang
  • Chen, Tingfeng
  • Wei, Xuezhe
  • Dai, Haifeng

Abstract

Differential voltage analysis (DVA) is a non-destructive method for analyzing the degradation mechanisms of lithium-ion batteries and is widely used. The capacity variations between the peaks on the differential voltage curves are considered to reflect different degradation mechanisms of the batteries. However, DVA of batteries is the coupling of cathodes and anodes, and is based on idealized assumptions that still require experimental validation and correction. Due to the challenges in fabricating long-term and reliable reference electrodes for commercial batteries, it is hard to decouple the cathode and anode over the battery entire lifespan. In this study, four types of three-electrode batteries are fabricated. The cycling aging experiments are conducted on the three-electrode batteries, decoupling the voltages of the cathode and anode over the entire battery lifespan. The DVA method is validated and corrected. The corrected DVA, combined with electrochemical impedance spectroscopy based on reference electrodes, is used to analyze and quantify the battery degradation mechanisms. The degradation mechanisms of 36.3 % loss of lithium inventory, 7.8 % loss of active material (LAM) for anode, and severe LAM for cathode are identified. Finally, the degradation mechanisms are validated by post-mortem analysis. This study provides valuable insights and guidance for the use of DVA and the future commercial three-electrode batteries in battery diagnosis and prognosis.

Suggested Citation

  • Xu, Wentao & Zhu, Jiangong & Zhang, Jie & Tian, Mengshu & Cai, Jixiang & Wu, Hang & Wei, Gang & Chen, Tingfeng & Wei, Xuezhe & Dai, Haifeng, 2025. "Investigation of lithium-ion battery degradation by corrected differential voltage analysis based on reference electrode," Applied Energy, Elsevier, vol. 389(C).
    • Handle: RePEc:eee:appene:v:389:y:2025:i:c:s0306261925004659
    DOI: 10.1016/j.apenergy.2025.125735
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925004659
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125735?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. Cai, Jixiang & Wei, Xuezhe & Wang, Xueyuan & Zhu, Jiangong & Jiang, Bo & Tao, Zhe & Tian, Mengshu & Dai, Haifeng, 2025. "Revealing effects of pouch Li-ion battery structure on fast charging ability through numerical simulation," Applied Energy, Elsevier, vol. 377(PA).
    2. Sun, Tao & Chen, Jianguo & Wang, Shaoqing & Chen, Quanwei & Han, Xuebing & Zheng, Yuejiu, 2023. "Aging mechanism analysis and capacity estimation of lithium - ion battery pack based on electric vehicle charging data," Energy, Elsevier, vol. 283(C).
    3. Wang, Ruixi & Zhou, Xing & Wang, Yu & Xiao, Yukang & Shi, Zhichao & Liu, Yajie & Zhang, Tao, 2024. "Degradation analysis of lithium-ion batteries under ultrahigh-rate discharge profile," Applied Energy, Elsevier, vol. 376(PA).
    4. Yang, Minxing & Sun, Xiaofei & Liu, Rui & Wang, Lingzhi & Zhao, Fei & Mei, Xuesong, 2024. "Predict the lifetime of lithium-ion batteries using early cycles: A review," Applied Energy, Elsevier, vol. 376(PA).
    5. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    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. Chen, Jianguo & Han, Xuebing & Sun, Tao & Zheng, Yuejiu, 2024. "Analysis and prediction of battery aging modes based on transfer learning," Applied Energy, Elsevier, vol. 356(C).
    2. Liu, Chenghao & Deng, Zhongwei & Zhang, Xiaohong & Bao, Huanhuan & Cheng, Duanqian, 2024. "Battery state of health estimation across electrochemistry and working conditions based on domain adaptation," Energy, Elsevier, vol. 297(C).
    3. Joselyn Stephane Menye & Mamadou-Baïlo Camara & Brayima Dakyo, 2025. "Lithium Battery Degradation and Failure Mechanisms: A State-of-the-Art Review," Energies, MDPI, vol. 18(2), pages 1-43, January.
    4. Mohammed, Abubakar Gambo & Elfeky, Karem Elsayed & Wang, Qiuwang, 2022. "Recent advancement and enhanced battery performance using phase change materials based hybrid battery thermal management for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    5. Chang, Chun & Wu, Yutong & Jiang, Jiuchun & Jiang, Yan & Tian, Aina & Li, Taiyu & Gao, Yang, 2022. "Prognostics of the state of health for lithium-ion battery packs in energy storage applications," Energy, Elsevier, vol. 239(PB).
    6. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    7. Wang, Zhenpo & Zhang, Dayu & Liu, Peng & Lin, Ni & Zhang, Zhaosheng & She, Chengqi, 2024. "An online inconsistency evaluation and abnormal cell identification method for real-world electric vehicles," Energy, Elsevier, vol. 307(C).
    8. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    9. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    10. Haris, Muhammad & Hasan, Muhammad Noman & Qin, Shiyin, 2021. "Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network," Applied Energy, Elsevier, vol. 286(C).
    11. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    12. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    13. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    14. Ouyang, Tiancheng & Gong, Yubin & Ye, Jinlu & Deng, Qiaoyang & Su, Yingying, 2025. "State co-estimation for lithium-ion batteries based on multi-innovations online identification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    15. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
    16. Wang, Shunli & Wu, Yingyang & Zhou, Heng & Zhang, Qin & Fernandez, Carlos & Blaabjerg, Frede, 2025. "Improved particle swarm optimization-adaptive dual extended Kalman filtering for accurate battery state of charge and state of energy joint estimation with efficient core factor feedback correction," Energy, Elsevier, vol. 322(C).
    17. Zhao, Jingyuan & Wang, Zhenghong & Wu, Yuyan & Burke, Andrew F., 2025. "Predictive pretrained transformer (PPT) for real-time battery health diagnostics," Applied Energy, Elsevier, vol. 377(PD).
    18. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network," Energy, Elsevier, vol. 295(C).
    19. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    20. Zhao, Yijian & Zheng, Menglian, 2025. "Battery management system for zinc-based flow batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 215(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:389:y:2025:i:c:s0306261925004659. 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.