Online SoC estimation of lithium ion battery for EV/BEV using Kalman filter with fading memory

Authors

Kai Chin Lim, University of WollongongFollow
Hany A. Bastawrous, University of WollongongFollow
Van Huan Duong, University of WollongongFollow
Khay Wai W. See, University of WollongongFollow
Peng Zhang, University of WollongongFollow
S X. Dou, University of WollongongFollow

RIS ID

99048

Publication Details

Lim, K. C., Bastawrous, H. A., Duong, V. H., See, K. W., Zhang, P. & Dou, S. X. (2014). Online SoC estimation of lithium ion battery for EV/BEV using Kalman filter with fading memory. 2014 IEEE 3rd Global Conference on Consumer Electronics (pp. 476-477). United States: IEEE.

Abstract

A novel algorithm based on fading Kalman filter to estimate the state of charge (SoC) of Li-ion battery used in electric vehicles is proposed and validated in this paper. Online identification of battery's electric model parameters followed by open circuit voltage estimation by fading Kalman filter resulted in accurate SoC estimation. The experimental results obtained from actual driving cycle in real-time reveal the robust performance of the proposed algorithm.