Optimization of Electric Vehicle Evacuation Integrating Mobile Charging Stations and Considering Vehicle Diversity

This paper addresses the challenges of electric vehicles (EVs) long-distance mass evacuations, particularly those posed by the extended charging time. We focus on optimizing evacuation planning in high EV ownership areas by considering route selection, vehicle grouping, departure timing, and charging scheduling, while incorporating Mobile Charging Stations (MCS) to supplement the existing Fixed Charging Stations (FCS). A two-stage optimization approach is used, i.e. route optimization through a recursive Dijkstra algorithm, followed by vehicle scheduling and MCS deployment via Mixed Integer Linear Programming (MILP). Apart from demonstrating the effectiveness of MCS in reducing the evacuation time, the study reveals key insights on the optimal scheduling and MCS placement patterns. In addition, this paper also investigates the impact of nonuniform properties (such as battery sizes and initial energy level) among EVs on the evacuation time, relating to the more complicated real-world operating conditions. Furthermore, through quantifying the impact of infrastructure capacities on evacuation, specifically charging rate and traveling speed, insights on cost-effective resource allocation for infrastructure upgrade are generated. The outcome provides a valuable tool for local agencies to optimize and evaluate evacuation strategies and infrastructure, with results showcasing the significant potential of MCS in enhancing EV evacuations in high-risk regions.