Accurate state-of-health (SOH) estimation is critical for lithium-ion batteries' safe and reliable operation. These batteries are widely used for commercial products, including smartphones, laptops, and electric vehicles. In this paper, we develop a convolutional neural network (CNN) based battery SOH estimation model trained to estimate SOH from constant current charge and discharge data. Aging data from four cells, each charged with a different fifteen-minute fast-charging current profile, is used to train and test the SOH estimation model. The model's accuracy is demonstrated by training with data from one fast-charging aging case and tested using the other three cases, which age at a considerably different rate. The results show that the method is quite robust when the tested cells have more than 80% SOH, with error typically within $\pm \mathbf{2}{\%}$ and not exceeding $\pm \mathbf{3}{\%}$. However, the proposed method has limitations when trying to predict battery health below 80% or when trying to predict battery health from curves with different C-rates. The datasets and the code for the algorithm in this paper are available to download.


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    Titel :

    A Convolutional Neural Network for Estimation of Lithium-Ion Battery State-of-Health during Constant Current Operation


    Beteiligte:
    Chen, Junran (Autor:in) / Manivanan, Manjula (Autor:in) / Duque, Josimar (Autor:in) / Kollmeyer, Phillip (Autor:in) / Panchal, Satyam (Autor:in) / Gross, Oliver (Autor:in) / Emadi, Ali (Autor:in)


    Erscheinungsdatum :

    21.06.2023


    Format / Umfang :

    3697784 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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