Title
Extended Kalman Filter Based Battery State of Charge (SOC) Estimation for Electric Vehicles
Document Type
Conference Proceeding
Publication Date
8-5-2013
Publication Title
IEEE Xplore
Conference Name
2013 IEEE Transportation Electrification Conference and Expo (ITEC)
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%.
Rights Statement
Copyright © 2013, IEEE
Recommended Citation
Jiang, Chenguang; Taylor, Allan; Duan, Chen; and Bai, Kevin, "Extended Kalman Filter Based Battery State of Charge (SOC) Estimation for Electric Vehicles" (2013). Electrical & Computer Engineering Presentations And Conference Materials. 8.
https://digitalcommons.kettering.edu/electricalcomp_eng_conference/8
Comments
https://doi.org/10.1109/ITEC.2013.6573477