Online State of Charge Estimation for Battery of Electric Vehicle Using Sigma-Points Kalman Filters

Abstract:

An accurate state-of-charge (SOC) estimation of the hybrid electric vehicle (HEV) and electric vehicle (EV) battery pack is a difficult task to be performed online in a vehicle because of the noisy and low accurate measurements and the wide operating conditions in which the vehicle battery can operate. A Sigma-points Kalman Filters (SPKF) algorithm based on an improved Lithium battery cell model to estimate the SOC of a Lithium battery cell is proposed in this paper. The simulation and experiment results show the effectiveness and ease of implementation of the proposed technique.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 824-829
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.824
• Online since: September 2013
• Authors: Li Cun Fang*, Gang Xu, Tian Li Li, Ke Min Zhu
• Keywords: Battery Management System (BMS), Sigma-Points Kalman Filters, State of Charge (SOC)

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