Title: Electric vehicle range estimation based on the road congestion level classification

Authors: Hong Liang; Wenjiao Wang; Yunlei Sun; Min Zhong; Yuan Gao; Xiao Sun; Jianhang Liu

Addresses: College of Computer and Communication Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Huangdao District, Qingdao 266580, China ' College of Computer and Communication Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Huangdao District, Qingdao 266580, China ' College of Computer and Communication Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Huangdao District, Qingdao 266580, China ' College of Computer and Communication Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Huangdao District, Qingdao 266580, China ' College of Computer and Communication Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Huangdao District, Qingdao 266580, China ' College of Computer and Communication Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Huangdao District, Qingdao 266580, China ' College of Computer and Communication Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Huangdao District, Qingdao 266580, China

Abstract: Electric vehicles have been an emerging industry in recent years even though the remaining driving range bothers the drivers. To strengthen the user acceptance and relieve the range anxiety, an efficient and accurate estimation approach of remaining driving range would be a solution. In this paper, a road congestion level classification based on support vector machines is proposed and an electric vehicle power model is implemented based on the real-world dataset collected from LF620 battery vehicles. The experiment includes data pre-processing, best parameters searching, support vector machine model training and remaining driving range calculation. The results show the significant influence of considering the big data analysis results on range estimation.

Keywords: electric vehicle; driving range estimation; support vector machine; python.

DOI: 10.1504/IJITM.2019.097883

International Journal of Information Technology and Management, 2019 Vol.18 No.1, pp.32 - 46

Available online: 13 Feb 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article