DocumentCode
3413791
Title
An adaptive algorithm of NiMH battery state of charge estimation for hybrid electric vehicle
Author
Qiang, JiaXi ; Ao, Guoqiang ; He, Jianhui ; Chen, Ziqiang ; Yang, Lin
Author_Institution
Sch. of Mech. Eng., Shanghai Jiaotong Univ., Shanghai
fYear
2008
fDate
June 30 2008-July 2 2008
Firstpage
1556
Lastpage
1561
Abstract
An adaptive algorithm for battery state of charge (SOC) estimation is presented in this paper to solve the critical issue of calculating the remaining energy of battery in hybrid electric vehicle (HEV). To obtain a more accurate SOC estimation value, both coulomb-accumulation and open-circuit voltage contributions are considered in this study. The extended Kalman filter (EKF) theory which has good adaptability is used respectively in these two contributions. The adaptive control effectiveness is achieved in two aspects: one is the application of Kalman filter which can filter the noise of voltage and current measurement and the other is the open-circuit voltage correction when the battery is in steady state to compensate the deficiencies of coulomb-accumulation. The test results show this adaptive algorithm has high robust property, noise-immune ability and accuracy which is suitable for HEV application.
Keywords
Kalman filters; hybrid electric vehicles; nickel; nonlinear filters; secondary cells; system-on-chip; HEV; Ni; adaptive control; coulomb-accumulation; extended Kalman filter; hybrid electric vehicle; noise-immune ability; open-circuit voltage; open-circuit voltage correction; state of charge estimation; voltage-current measurement; Adaptive algorithm; Adaptive control; Adaptive filters; Batteries; Current measurement; Hybrid electric vehicles; State estimation; Steady-state; Testing; Voltage;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics, 2008. ISIE 2008. IEEE International Symposium on
Conference_Location
Cambridge
Print_ISBN
978-1-4244-1665-3
Electronic_ISBN
978-1-4244-1666-0
Type
conf
DOI
10.1109/ISIE.2008.4677229
Filename
4677229
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