For the challenge of intricate the aging mechanism and the difficulty in measuring battery life, in-situ non-destructive diagnostic tests combined with Distribution of Relaxation Times (DRT) analysis capture the electrochemical process characteristics associated with time constants during battery cycle life. Subsequently, life prediction algorithm based on the Extreme Gradient Boosting (XGBoost) is developed. The prediction accuracy of dQ-dV, ECM (Equivalent Circuit Model), and DRT features is compared under dynamic conditions for two different batteries. The results indicate that the DRT analysis offers more precise mechanism characteristics than ECM and dQ-dV. The XGBoost-based estimation algorithm enhances the robustness of life prediction under dynamic conditions and improves the generalization across battery types.


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    Titel :

    Remaining Useful Life Prediction Based on Electrochemical Impedance Characteristic Distribution


    Beteiligte:
    He, Rong (Autor:in) / Guo, Bin (Autor:in)


    Erscheinungsdatum :

    10.10.2024


    Format / Umfang :

    947630 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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