IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v286y2024ics036054422303075x.html
   My bibliography  Save this article

Enhancing real-time degradation prediction of lithium-ion battery: A digital twin framework with CNN-LSTM-attention model

Author

Listed:
  • Li, Wei
  • Li, Yongsheng
  • Garg, Akhil
  • Gao, Liang

Abstract

Lithium-ion batteries (LIBs) have gained widespread usage in electric vehicles (EVs) due to their high energy density, long cycle life, and environmental friendliness. However, as LIBs undergo repeated charging and discharging cycles, they experience performance degradation. When the rated capacity of LIBs drops to approximately 80 %, retirement becomes necessary. Therefore, accurately determining real-time battery degradation is of paramount importance. This study presents a digital twin framework for analyzing and predicting LIB degradation performance. Within this framework, the back propagation neural network (BPNN) is employed to predict and complete the partial discharge voltage curve of the actual battery cycle. Building upon this, in conjunction with the battery's state of charge (SOC), the convolutional neural networks-long short term memory-attention (CNN-LSTM-Attention) model is utilized to real-time forecast the maximum available capacity of LIBs and reveal the battery's degradation state. Experimental results demonstrate a 99.6 % accuracy in completing the partial discharge voltage. Moreover, the prediction accuracy for maximum available capacity surpasses 99 % with a maximum error of less than 3 mAh. Thus, this research substantiates the efficacy and practical applicability of the proposed approach.

Suggested Citation

  • Li, Wei & Li, Yongsheng & Garg, Akhil & Gao, Liang, 2024. "Enhancing real-time degradation prediction of lithium-ion battery: A digital twin framework with CNN-LSTM-attention model," Energy, Elsevier, vol. 286(C).
  • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s036054422303075x
    DOI: 10.1016/j.energy.2023.129681
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422303075X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.129681?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    2. Huang, Xiaojia & Wang, Chen & Zhang, Shenghui, 2024. "Research and application of a Model selection forecasting system for wind speed and theoretical power generation in wind farms based on classification and wind conversion," Energy, Elsevier, vol. 293(C).

    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:s036054422303075x. 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.