Battery SoC Estimation from EIS using Neural Nets

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.