Title :
Online SoC estimation of lithium ion battery for EV/BEV using Kalman filter with fading memory
Author :
Lim, K.C. ; Bastawrous, H.A. ; Duong, V.H. ; See, K.W. ; Zhang, P. ; Dou, S.X.
Author_Institution :
Inst. for Supercond. & Electron. Mater., Univ. of Wollongong, Wollongong, NSW, Australia
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.
Keywords :
Kalman filters; battery powered vehicles; estimation theory; parameter estimation; secondary cells; EV-BEV; battery electric model parameter identification; electric vehicle; fading Kalman filter memory; lithium ion battery; online SoC estimation; open circuit voltage estimation; state of charge; Batteries; Estimation; Fading; Integrated circuit modeling; Kalman filters; System-on-chip; Voltage measurement;
Conference_Titel :
Consumer Electronics (GCCE), 2014 IEEE 3rd Global Conference on
Conference_Location :
Tokyo
DOI :
10.1109/GCCE.2014.7031205
