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Battery SoC Estimation from EIS using Neural Nets
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Battery SoC Estimation from EIS using Neural Nets

Abstract

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.

Authors

Messing M; Shoa T; Ahmed R; Habibi S

Volume

00

Pagination

pp. 588-593

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication Date

June 26, 2020

DOI

10.1109/itec48692.2020.9161523

Name of conference

2020 IEEE Transportation Electrification Conference & Expo (ITEC)
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