DocumentCode :
574307
Title :
Real time battery power capability estimation
Author :
Anderson, R. Dyche ; Yanan Zhao ; Xu Wang ; Xiao Guang Yang ; Yonghua Li
Author_Institution :
Vehicle & Battery Controls Dept., Ford Motor Co., Dearborn, MI, USA
fYear :
2012
fDate :
27-29 June 2012
Firstpage :
592
Lastpage :
597
Abstract :
Accurate battery power capability estimation is a key to battery life and performance in electric and hybrid vehicles. If power capability is predicted to be higher than actual, battery life is reduced and there is potential for vehicle shutdown. If power capability is predicted lower than actual, the customer may experience slower acceleration, lower top speed, and reduced usable electric drive range. In this paper an approach is proposed to estimate lithium-ion battery charge and discharge power capabilities online. First a simplified Randles circuit model is used to represent a battery cell. Then an Extended Kalman Filter (EKF) is constructed to estimate the model parameters and voltage across the RC network. Algorithms to calculate the charge and discharge power capabilities are presented. Both desktop simulation and pack level vehicle data are shown to support the correctness and accuracy of the proposed algorithms.
Keywords :
Kalman filters; electric drives; hybrid electric vehicles; lithium; secondary cells; EKF; Li; battery cell; charge power capabilities; desktop simulation; discharge power capabilities; discharge power capabilities online; electric drive; extended Kalman filter; hybrid vehicles; lithium-ion battery charge; power capability; real time battery power capability estimation; vehicle shutdown; Batteries; Computational modeling; Discharges (electric); Estimation; Integrated circuit modeling; Mathematical model; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2012
Conference_Location :
Montreal, QC
ISSN :
0743-1619
Print_ISBN :
978-1-4577-1095-7
Electronic_ISBN :
0743-1619
Type :
conf
DOI :
10.1109/ACC.2012.6314892
Filename :
6314892
Link To Document :
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