Multi-agent Reinforcement Learning for Online Placement of Mobile EV Charging Stations

Lo Pang-Yun Ting, Chi-Chun Lin, Shih-Hsun Lin, Yu-Lin Chu, Kun-Ta Chuang

Abstract: As global interest shifts toward sustainable transportation with the proliferation of electric vehicles (EVs), the demand for an efficient, real-time, and robust charging infrastructure becomes increasingly pronounced. This paper introduces an approach to address the imbalance between the surging EV demand and the existing charging infrastructure: the concept of Mobile Charging Stations (MCSs). The research develops an algorithm for the dynamic placement of MCSs to significantly reduce the waiting time for EV owners. The core of this research is the Two-stage Placement and Management with Multi-Agent Reinforcement Learning (2PM-MARL) for a dynamic balancing of charging demand and supply. The complexity of the problem is elaborated by showing the NP-hard nature of the MCS placement issue through a relation to the Uncapacitated Facility Location Problem (UFLP), underscoring the computational challenges and emphasizing the need for intelligent real-time solutions. Our framework is validated through comprehensive experiments using real-world charging session data. The results exhibit significant reductions in the waiting time, suggesting the potential practicality and efficiency of our proposed model.