Hierarchical User-Driven Trajectory Planning and Charging Scheduling of Autonomous Electric Vehicles

Abstract

Autonomous electric vehicles (A-EVs), regarded as one of the innovations to accelerate transportation electrification, have sparked a flurry of interest in trajectory planning and charging scheduling. In this regard, this work employs mobile edge computing (MEC) to design a decentralized hierarchical algorithm for finding an optimal path to the nearby A-EV parking lots (PLs), selecting the best PL, and executing an optimal charging scheduling. The proposed model makes use of unmanned aerial vehicles (UAVs) to assist edge servers in trajectory planning by surveying road traffic flow in real time. Furthermore, the target PLs are selected using a user-driven multiobjective problem to minimize the cost and waiting time of A-EVs. To tackle the complexity of the optimization problem, a greedy-based algorithm has been developed. Finally, charging/discharging power is scheduled using a local optimizer based on the PLs’ real-time loads, which minimizes the deviation of the charging/discharging power from the average load. The obtained results show that the proposed model can handle charging/discharging requests of on-move A-EVs and bring fiscal and nonfiscal benefits for A-EVs and the power grid, respectively. Moreover, it observed that user satisfaction in terms of traveling time and traveling distance is increased by using the edge-UAV model.