Journal
ENERGIES
Volume 6, Issue 10, Pages 5088-5100Publisher
MDPI
DOI: 10.3390/en6105088
Keywords
electric vehicles; dynamic modeling; SoC estimation; extended Kalman filter; unscented Kalman filter
Categories
Funding
- National High Technology Research and Development Program of China [2012AA111603, 2011AA11A228, 2011AA1290]
- International Cooperation Research Program of Chinese Ministry of Science and Technology [2011DFB70020]
- Program for New Century Excellent Talents in University [NCET-11-0785]
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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.
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