Distributed Edge Intelligence Empowered Hybrid Charging Scheduling in Internet of Electric Vehicles

As Distributed Edge Intelligence (DEI) advances within the Internet of Electric Vehicles (IoEV), the deployment of Mobile Charging Stations (MCS) offers a solution to the uneven distribution of Fixed Charging Stations (FCS), enhancing energy access in remote areas. However, MCS faces the problem of passive scheduling, limiting effective resource utilization and prolonging charging waiting time. This paper proposes a Hybrid Charging Model Algorithm (HCMA) to address the above challenge, particularly in regions with limited available FCS. We first formulate a multi-objective optimization problem to optimize electric vehicle charging modes, volumes, and MCS scheduling arrangements. Then, we decompose the original problem into two subproblems. By determining electric vehicle charging locations and electric vehicle charging mode, the two subproblems are solved respectively. Finally, simulations based on real-world data demonstrate that HCMA performs better compared to several representative methods, including random-working, ARMM and RBA, in terms of average charging waiting time, extra travelling distance, average unit price of energy, and number of MCS schedules.