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Energies 2012, 5(4), 1098-1115; doi:10.3390/en5041098
Article

Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

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Received: 17 February 2012; in revised form: 27 March 2012 / Accepted: 11 April 2012 / Published: 19 April 2012
(This article belongs to the Special Issue Vehicle to Grid)
Download PDF [460 KB, uploaded 19 April 2012]
Abstract: A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.
Keywords: lithium-ion batteries; SOC estimation; robust estimation; EKF; HEV lithium-ion batteries; SOC estimation; robust estimation; EKF; HEV
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Zhang, C.; Jiang, J.; Zhang, W.; Sharkh, S.M. Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering. Energies 2012, 5, 1098-1115.

AMA Style

Zhang C, Jiang J, Zhang W, Sharkh SM. Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering. Energies. 2012; 5(4):1098-1115.

Chicago/Turabian Style

Zhang, Caiping; Jiang, Jiuchun; Zhang, Weige; Sharkh, Suleiman M. 2012. "Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering." Energies 5, no. 4: 1098-1115.


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