DocumentCode
627656
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
fYear
2013
fDate
16-19 June 2013
Firstpage
1
Lastpage
5
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);
fLanguage
English
Publisher
ieee
Conference_Titel
Transportation Electrification Conference and Expo (ITEC), 2013 IEEE
Conference_Location
Detroit, MI
Print_ISBN
978-1-4799-0146-3
Type
conf
DOI
10.1109/ITEC.2013.6573477
Filename
6573477
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