Title :
SOC estimation of electric vehicle based on the establishment of battery management system
Author :
Yajun Rong ; Wei Yang ; Hong Wang ; Hanhong Qi
Author_Institution :
Inst. of Electr. Eng., Yanshan Univ., Qinhuangdao, China
fDate :
Aug. 31 2014-Sept. 3 2014
Abstract :
Taking the DSP (TMS320LF2407) as the main control chip, the construction of the platform for electric vehicle battery management system (BMS) is completed in this paper. Then the expanded Kalman filter (EKF) algorithm is combined with the Thevenin battery model through the MATLAB simulation and with which the working status of the battery is simulated successfully. Finally through the simulated test of working condition for lithium iron phosphate battery in the platform, the error got of the battery state of charge (SOC) estimation is less than 5%.
Keywords :
Kalman filters; battery management systems; digital signal processing chips; electric vehicles; nonlinear filters; secondary cells; BMS; DSP; EKF algorithm; MATLAB simulation; SOC estimation; TMS320LF2407; Thevenin battery model; battery state of charge estimation; control chip; electric vehicle battery management system; expanded Kalman filter algorithm; lithium iron phosphate battery; Batteries; Battery management systems; Discharges (electric); Electric vehicles; Estimation; Mathematical model; System-on-chip; Battery management system; Electric vehicle; Extended Kalman filter; State of charge;
Conference_Titel :
Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-4240-4
DOI :
10.1109/ITEC-AP.2014.6940791
