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.
Battery SoC Estimation from EIS using Neural Nets
2020-06-01
359385 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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