Abstract:

Mobile charging stations (MCS) are a new technology for electric vehicle charging, addressing issues like long waiting times, charging congestion, and travel anxiety. However, the challenge lies in the large batteries required to provide charging services, leading to high operating costs and prolonged waiting times for users. Despite offering convenience, MCSs also pose challenges in terms of user travel and convenience. To overcome the issues, a novel framework is proposed by optimizing the MCS operation for economic efficiency. In this study, a variant of the mixed integer linear programming (MILP) model is developed to maximize the MCS operator's profits while considering the user's perspective, including clustering and covering user demands, setting temporary charging locations for users, dispatching MCSs for charging and discharging, and scheduling EV charging. As the scale of the problem increases, the solution time for using the proposed MILP model becomes inefficient. Whereas an improved genetic algorithm is developed for solving a large-scale of the proposed model. Solomon's benchmarking instance data is adopted to evaluate the performance and validity of the proposed algorithm, complemented by various sensitivity analyses aimed at providing managerial insights into MCS operations.