Accurately predicting the SoC of the battery is the main function of any Battery Management System (BMS). BMS must carefully read the battery parameters, to safe guard the battery, to predict accurately the battery's state, so as to improve the battery's performance and to alert to users/external devices. The objective of this study is to offer a comprehensive overview of Machine Learning (ML) algorithms for predicting the State-of-Charge (SoC) of Lithium-ion batteries for enabling an accurate and online estimation, mandatory for ensuring battery as well as user safety and also aims to provide efficient BMS for the advancing the development of EVs (Electric Vehicles) and HEVs (Hybrid Electric Vehicles).


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    Titel :

    Machine Learning Algorithms for Estimation of State-of-Charge of Li-Ion Batteries


    Beteiligte:
    Pushpavanam, B. (Autor:in) / Akilan, T. (Autor:in) / Kalyani, S. (Autor:in) / Swedheetha, C. (Autor:in) / Naveen, P. (Autor:in) / Manikandan, P. (Autor:in)


    Erscheinungsdatum :

    22.11.2023


    Format / Umfang :

    989491 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch