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

Mansour Saatloo, Amin, Mehrabidavoodabadi, Abbas, Marzband, Mousa and Aslam, Nauman (2023) Hierarchical User-Driven Trajectory Planning and Charging Scheduling of Autonomous Electric Vehicles. IEEE Transactions on Transportation Electrification, 9 (1). pp. 1736-1749. ISSN 2332-7782

A_EV.pdf - Accepted Version

Download (5MB) | Preview
Official URL: https://doi.org/10.1109/TTE.2022.3196741


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.

Item Type: Article
Additional Information: Funding information: This work was funded by PGR scholarship (RDF studentship) at Northumbria University and supported from DTE Network+ funded by EPSRC grant reference EP/S032053/1.
Uncontrolled Keywords: Mobile edge computing (MEC), autonomous electric vehicle (A-EV), trajectory planning, greedy algorithm
Subjects: H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 03 Aug 2022 11:21
Last Modified: 05 Apr 2023 13:15
URI: https://nrl.northumbria.ac.uk/id/eprint/49714

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics