En-Route Electric Vehicles Charging Navigation Considering the Traffic-Flow-Dependent Energy Consumption
Hui Lu, Changzheng Shao, Bo Hu, Kaigui Xie, Chunyan Li, Yue Dong Sun
Abstract
En-route charging navigation system is significant for electric vehicle (EV) owners to relieve the range anxiety. Considering the limited battery capacity of EVs, accurate battery energy consumption prediction is crucial for charging navigation in that it determines the maximum traveling distance. This article develops a comprehensive EV energy consumption prediction model and the impacts of the traffic flow on the motor efficiency and driving resistances have been thoroughly considered. Moreover, a stochastic traffic flow model is proposed to improve the energy consumption prediction precision, in which the related uncertainties, such as travel time, vehicle speed, etc., are captured. Then, a novel cost-effective en-route EV charging navigation problem is formulated as a two-stage stochastic optimization problem. In addition, the EV charging behavior and its interaction with the routing selection are characterized in the navigation problem. Finally, an improved Benders Algorithm is modified to obtain the global optimal solution of the proposed navigation model. The simulation verification is carried out in the actual road network of a certain city and the IEEE 33-node coupling system. Results demonstrate the feasibility and effectiveness of the proposed model and solution algorithm.