Title :
Extended Kalman Filter based battery state of charge(SOC) estimation for electric vehicles
Author :
Chenguang Jiang ; Taylor, Andrew ; Chen Duan ; Bai, Ke
Author_Institution :
Dept. of Electr. & Comput. Eng., Kettering Univ., Flint, MI, USA
Abstract :
This paper proposed a battery state of charge (SOC) estimation methodology utilizing the Extended Kalman Filter. First, Extended Kalman Filter for Li-ion battery SOC was mathematically designed. Next, simulation models were developed in MATLAB/Simulink, which indicated that the battery SOC estimation with Extended Kalman filter is much more accurate than that from Coulomb Counting method. This is coincident with the mathematical analysis. At the end, a test bench with Lithium-Ion batteries was set up to experimentally verify the theoretical analysis and simulation. Experimental results showed that the average SOC estimation error using Extended Kalman Filter is <;1%.
Keywords :
Kalman filters; electric vehicles; secondary cells; Coulomb counting method; Li; Li-ion battery; MATLAB/Simulink; battery state of charge estimation; electric vehicles; estimation error; extended Kalman filter; mathematical analysis; Indexes; Kalman filters; Software packages; System-on-chip; Electric Vehicles; Kalman Filter; Li-ion Battery; State of Charge(SOC);
Conference_Titel :
Transportation Electrification Conference and Expo (ITEC), 2013 IEEE
Conference_Location :
Detroit, MI
Print_ISBN :
978-1-4799-0146-3
DOI :
10.1109/ITEC.2013.6573477