A review of static and dynamic charging in electric vehicle routing: Transition, algorithms and future directions

Abstract

Electric vehicles (EVs) have significant potential to reduce emissions and revolutionise transportation. This paper provides the first comprehensive review of Electric Vehicle Routing Problems (EVRPs) explicitly emphasising dynamic charging (DC-EVRP) methodologies, contrasting with conventional static charging (SC-EVRP). By systematically analysing 140 studies, we identify critical transitions from static to dynamic charging paradigms, highlighting dynamic wireless and overhead catenary systems that enable continuous vehicle operation, reduced battery capacity needs, and enhanced operational flexibility. From an algorithmic perspective, this review rigorously analyses heuristic, meta-heuristic, and deep reinforcement learning (DRL) methods employed across EVRP variants. While classical algorithms have been extensively developed for SC-EVRP, their adaptation to DC-EVRP remains limited, presenting unique challenges in real-time decision-making and energy management. Conversely, DRL and data-driven approaches show promise for integrating traffic, grid, and vehicle states, but remain under-explored in large-scale, dynamic charging contexts. To bridge existing research gaps, we propose future directions, including the design of resilient hybrid-charging strategies, the development of unified digital frameworks and benchmark datasets, and the advancement of transferable DRL/GNN models tailored to the complexities of DC-EVRP. By combining algorithmic innovation with integrated dynamic charging infrastructure, this review outlines a pathway for advancing EVRP solutions towards scalable, sustainable, and system-wide intelligent electric mobility.