Enhancing Large-Scale Evacuations of Electric Vehicles Through Integration of Mobile Charging Stations

Electric vehicles (EVs) pose significant challenges for long-distance mass evacuation during natural hazards due to their long charging time compared to traditional gasoline vehicles. This paper studies the evacuation planning for high EV ownership regions by exploring the optimization of route selection, vehicle grouping, and departure and charging scheduling. More importantly, we also consider the Mobile Charging Stations (MCS), which can be deployed temporarily to supplement the Fixed Charging Stations (FCS) for the evacuation operation, and study the optimization of their placement. The overall optimization scheme is divided into two phases: route optimization via a recursive Dijkstra algorithm and combined vehicle grouping, scheduling, and MCS deployment through constrained Mixed Integer Linear Programming (MILP). Our case study, using a modified Sioux Falls map, reveals that additional MCSs could significantly reduce average evacuation time (nearly 40% reduction for vehicles required charging in the case study), and gives several other interesting insights. For example, the number of charging ports available at charging stations is identified as a critical factor affecting the size distribution of the evacuating vehicle groups. Strategically, the best locations for MCSs are nodes with the highest number of charging visits and network centrality measures. This pioneering study demonstrates the prominent potential of MCS in enabling EV evacuation in high-risk areas, and the proposed optimization scheme provides a tool for governments and agencies to perform evacuation operation planning, optimization, and evaluation.