Title :
EKF-Ah Based State of Charge Online Estimation for Lithium-ion Power Battery
Author :
He, Zhiwei ; Gao, Mingyu ; Xu, Jie
Author_Institution :
Coll. of Commun. Eng., Hangzhou Dianzi Univ., Hangzhou, China
Abstract :
One of the most essential and significant aspect of the battery management systems (BMS) is to estimate the state of charge (SOC) accurately, which can provide the judgment basis to system control strategy. In view of the lithium-ion power battery´ s properties and its operation condition in electric vehicles, a new method named EKF-Ah that derives from the extended Kalman filtering (EKF) algorithm and ampere hour counting method is proposed, which has a good performance on SOC estimation in complicated environment and is able to accomplish the requirements on power batteries. Results of tests show that the maximal SOC estimation error is fewer than 6.5%, which validates the feasibility and reliability of the proposed method.
Keywords :
Kalman filters; battery charge measurement; battery management systems; electric vehicles; secondary cells; EKF-Ah method; ampere hour counting method; battery management system; electric vehicles; extended Kalman filtering algorithm; lithium-ion power battery; online estimation; state of charge; system control strategy; Battery management systems; Circuits; Educational institutions; Electric vehicles; Estimation error; Mathematical model; Mathematics; State estimation; Temperature; Voltage; EKF; Power battery; SOC;
Conference_Titel :
Computational Intelligence and Security, 2009. CIS '09. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-5411-2
DOI :
10.1109/CIS.2009.47