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.


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    Title :

    Battery SoC Estimation from EIS using Neural Nets


    Contributors:
    Messing, Marvin (author) / Shoa, Tina (author) / Ahmed, Ryan (author) / Habibi, Saeid (author)


    Publication date :

    2020-06-01


    Size :

    359385 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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