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

fYear

2014

fDate

Aug. 31 2014-Sept. 3 2014

Firstpage

1

Lastpage

5

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo

Conference_Location

Beijing

Print_ISBN

978-1-4799-4240-4

Type

conf

DOI

10.1109/ITEC-AP.2014.6940791

Filename

6940791