Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms

Hongwen He, Hongzhou Qin, Xiaokun Sun and Yuanpeng Shui
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Hongwen He: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Hongzhou Qin: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Xiaokun Sun: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Yuanpeng Shui: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

Energies, 2013, vol. 6, issue 10, 1-13

Abstract: The battery state of charge (SoC), whose estimation is one of the basic functions of battery management system (BMS), is a vital input parameter in the energy management and power distribution control of electric vehicles (EVs). In this paper, two methods based on an extended Kalman filter (EKF) and unscented Kalman filter (UKF), respectively, are proposed to estimate the SoC of a lithium-ion battery used in EVs. The lithium-ion battery is modeled with the Thevenin model and the model parameters are identified based on experimental data and validated with the Beijing Driving Cycle. Then space equations used for SoC estimation are established. The SoC estimation results with EKF and UKF are compared in aspects of accuracy and convergence. It is concluded that the two algorithms both perform well, while the UKF algorithm is much better with a faster convergence ability and a higher accuracy.

Keywords: electric vehicles; dynamic modeling; SoC estimation; extended Kalman filter; unscented Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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