In this paper, a battery state of charge (SoC) estimation strategy with deep neural networks (DNN) and Electrochemical Impedance Spectroscopy (EIS) is proposed. EIS data was obtained for a range of conditions and was used as inputs to a DNN. Additionally, a battery model was fit to the data, and the model parameters were used as inputs to a second DNN. The Root Mean Square Error (RMSE) of both networks was found to be less than 5% for SoC above 30%. The dataset used in this study included batteries of different States of Health (SoH) as well as EIS measured at various rest times after different discharge pulses.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Battery SoC Estimation from EIS using Neural Nets


    Beteiligte:
    Messing, Marvin (Autor:in) / Shoa, Tina (Autor:in) / Ahmed, Ryan (Autor:in) / Habibi, Saeid (Autor:in)


    Erscheinungsdatum :

    2020-06-01


    Format / Umfang :

    359385 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Unstructured to Structured Error Correction Using Neural Nets

    Al-Mashouq, K. A. / Al Jabri, A. K. / IEEE; Hong Kong Chapter of Signal Processing | British Library Conference Proceedings | 1994


    Neural Network Development Tool (NETS)

    Baffes, Paul T. | NTRS | 1990


    Developing Fuzzy Route Choice Models Using Neural Nets

    Hawas, Y. E. / INRIA / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2003