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 (PL), 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. Further, the target PLs are selected using a user-driven multi-objective 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 non-fiscal benefits for A-EVs and the power grid, respectively. Moreover, it observed that user satisfaction in terms of traveling time and traveling distance are increased by using the edge-UAV model.

A Multiagent Framework for Electric Vehicles Charging Power Forecast and Smart Planning of Urban Parking Lots

“…Charging demand according to different places such as supermarket, road junctions, have been accounted and the objective is to minimize the energy loss, voltage deviation and the land cost. Authors in [28], [29], have formulated a stochastic mixed integer linear programming model for standalone charging stations for EVs using green energy of renewables. The stochastic behaviour of EVs and renewables has been considered.…”

“…The stochastic behaviour of EVs and renewables has been considered. A novel carbon-oriented expansion planning model for EV fast-charging stations is proposed in [28], [29] to determine the optimal locations and size of charging stations. Authors in [30], have proposed a realistic and sustainable framework for optimal planning of the location and capacity of the EVs charging stations and expansion of the electrical distribution system to handle the future load growth.…”

Dynamic pricing strategy for efficient electric vehicle charging and discharging in microgrids using multi-objective jaya algorithm

“…The Jaya algorithm is an optimization technique inspired by the behavior of individuals in a society. It is developed by Rao and Savsani in 2011 [20] which simulates the process of social interaction to find the optimal solution to a problem. In Jaya algorithm, the solutions are represented as individuals, and their quality is determined by an objective function.…”