Optimal Routing and Charging Strategy of Electric Vehicles Based on Traffic Flow Prediction

With the increasing number of electric vehicles (EVs) in recent years, road congestion is becoming a common phenomenon, which not only prolongs travel time but also causes anxiety for EV users. Therefore, this paper proposes a time-varying shortest path search method for traffic flow (TF) and establishes an EV route planning model based on this method to plan the optimal path for users. Firstly, a convolutional neural network (CNN) is used to predict TF and a new queuing model is established to calculate the charging queuing time. Then, a path planning model considering mid-way charging is established based on the predicted TF data and charging queuing model. Finally, the performance of the proposed method is tested using road network maps of different scales, and a case study on the optimal path of EV with mid-way charging under the minimum objective function is conducted based on a real traffic network. The results show that the proposed time-varying shortest path search method under the TF network can quickly calculate the optimal path and has great potential for solving practical problems.