Remaining Driving Range Estimation for Electric Vehicles through Information Fusion Method

Abstract:

Electric vehicle (EV) is one of the current research focuses due to its environmental friendliness, but the inaccuracy in remaining driving range (RDR) estimation lowers the consumers’ confidence on EVs. This paper introduces a remaining driving range estimation method based on information fusion (RDRIF), in which the RDR is determined by the fusion of the calculated range (calculation of the present battery and vehicle consumption states) and the cumulated range (cumulation of the real-traveled distance) to provide a accuracy RDR result. The RDRIF algorithm was embedded in a type of pure electric vehicle, and a set of vehicle experiments was carried out on a dynamometer test system. Two urban driving cycles representing normal and aggressive driving conditions are implemented in the tests to validate the effectiveness of RDRIF. The results show that the RDRIF could provide a stable and convergent estimation result under both driving conditions compared with the traditional methods, and the estimation error could be rapidly limited to ±5%, which effectively reduces the passengers’ range anxiety.

• Periodical: Applied Mechanics and Materials (Volumes 668-669)
• Pages: 641-646
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.668-669.641
• Online since: October 2014
• Authors: Guang Ming Liu*, Hong Fu, Lan Guang Lu, Yan Jing Wang, Jian Feng Hua, Jian Qiu Li, Ming Gao Ouyang, Chao Feng, Shan Xue, Ping Chen
• Keywords: Electric Vehicle (EV), Information Fusion Method, Range Anxiety, Remaining Driving Range

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