An Innovative Method For Route Mapping of Battery Electric Vehicles

Route mapping is a useful method to save time for drivers, but for electric vehicles (EVs), electrical energy saving should also be considered. In this paper, a novel approach to the route mapping of battery electric vehicles (BEVs) is proposed. The objective of this approach is to simultaneously optimize the total electrical energy consumption and travel time of the BEVs driving on a path. Four factors (vehicle speed, road grade, ambient temperature, and wind speed) are considered for the electrical energy management of BEVs. An artificial neural network (ANN) is used to correct the real-time speed data. Evaluation of the proposed approach is performed on a large part of Kowloon city and is implemented by MATLAB. It was found that using this approach, reduces the total electrical energy consumption and travel time of BEVs, as well as improving their lifespan.

[1]  Antonio T. Alexandridis,et al.  Power Electronic Control Design for Stable EV Motor and Battery Operation during a Route , 2019, Energies.

[2]  M. A. DAIGLE,et al.  Brief Papers , 1996, Brain and Cognition.

[3]  João L. Afonso,et al.  Real-life comparison between diesel and electric car energy consumption , 2013 .

[4]  Hamed Nafisi,et al.  Investigation on distribution transformer loss-of-life due to plug-in hybrid electric vehicles charging , 2019, International Journal of Ambient Energy.

[5]  Fuyuan Yang,et al.  Optimization of compound power split configurations in PHEV bus for fuel consumption and battery degradation decreasing , 2019, Energy.

[6]  Simona Onori,et al.  Analysis of impact factors for plug-in hybrid electric vehicles energy management , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[7]  Da Xie,et al.  Integrated Route Planning Algorithm Based on Spot Price and Classified Travel Objectives for EV Users , 2019, IEEE Access.

[8]  Jeremy J. Michalek,et al.  Effects of regional temperature on electric vehicle efficiency, range, and emissions in the United States. , 2015, Environmental science & technology.

[9]  Matthew Shirk,et al.  Factors Affecting the Fuel Consumption of Plug-In Hybrid Electric Vehicles , 2010 .

[10]  Xiaoming Ma,et al.  Comparative Life Cycle Energy and GHG Emission Analysis for BEVs and PhEVs: A Case Study in China , 2019, Energies.

[11]  Mehrdad Abedi,et al.  Network Security Perspectives of Plug-in Hybrid Electric Vehicles , 2014 .

[12]  Xu Guo,et al.  Optimal Charging Navigation Strategy for Electric Vehicles , 2016, 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII).