Volume 06: Proceedings of 11th International Conference on Applied Energy, Part 5, Sweden, 2019

Economical Path Planning for Electric Vehicles Considering Traffic Information Hongwen He, Jianbin Lin , Jiankun Peng , Qingwu Liu , Jianwei Li

Abstract

Vehicle exhaust pollution and traffic congestion are plaguing the daily life of the citizens. Although electric vehicles represent green travel, the problem of mileage anxiety still troubles electric occupants. Aiming at the existing problems, an electric vehicle energy consumption prediction based on LSTM deep learning technology combined with traffic information is proposed to plan the economical driving path with the best coupling of energy consumption and driving distance. The method has the ability to integrate multidimensional data of heterogeneous heads, solves the problem that electric vehicle energy consumption estimation cannot take into account real traffic information. And getting rid of the shortcomings of path planning relying only on driving distance, effectively improving the driving feeling of electric vehicles and bettering travel efficiency to optimize urban traffic conditions.

Keywords Electric vehicle, Data fusion, Energy consumption forecast, Path planning

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