Title :
SOC estimation and simulation of electric vehicle lead-acid storage battery with Kalman filtering method
Author :
Wang Huihui ; Zhang Hongpeng
Author_Institution :
Sch. of Electron. Inf. Eng., Xi´an Technol. Univ., Xi´an, China
Abstract :
Low carbon economy being the theme of economic development, the progress of electric vehicle is emphasized with policies in China. Storage battery, functioning as the power source of electric vehicle, has always been confining the progress of electric vehicle. This paper, based on the charge-discharge characteristics and their relations with remaining power, builds an equivalent Randle circuit model of lead-acid battery, and presents that the remaining power of the storage battery (SOC) could be continuously predicted and estimated. By using the Kalman filtering method, and combining the overall model made by MATLAB-simulink tool with the Kalman filtering method, the estimation and simulation is carried out. The result shows that the Kalman filtering method has advantages of efficiency and accuracy over other filtering methods in terms of estimating the SOC of storage battery.
Keywords :
Kalman filters; battery powered vehicles; lead acid batteries; Kalman filtering method; MATLAB-simulink tool; Randle circuit model; SOC estimation; electric vehicle lead-acid storage battery simulation; state-of-charge estimation; storage battery; Batteries; Discharges (electric); Estimation; Kalman filters; Mathematical model; System-on-chip; Kalman filtering; MATLAB-simulink; SOC; storage battery;
Conference_Titel :
Electronic Measurement & Instruments (ICEMI), 2013 IEEE 11th International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4799-0757-1
DOI :
10.1109/ICEMI.2013.6743170