A Simulation Framework for Evaluating Mobile Autonomous Charging Pod Operations

Abstract

Recent advances in automation have accelerated the development of autonomous electric vehicles (AEVs), which offer the potential for continuous operation, constrained primarily by the need for recharging. We propose a dynamic charging strategy based on Mobile Autonomous Charging Pods (MAPs), which are battery-equipped electric vehicles capable of transferring energy to AEVs while in motion. We introduce a dedicated simulation framework within the microscopic traffic simulator SUMO, incorporating MAP-specific modules for assignment, navigation, and real-time energy transfer under realistic traffic constraints. We model the behavior of both MAPs and AEVs in a stylized looped network and evaluate system-level performance under various demand and fleet configurations. Key performance indicators include energy consumption, charging efficiency, battery utilization, and reductions in AEV battery capacity requirements. Simulation results demonstrate that MAPs can effectively support continuous AEV operation, achieving up to 14% battery downsizing with minimal infrastructure investment, while also reducing travel time by 7%, relative to fixed charging solutions. This study lays the foundation for simulation-based evaluation of MAP-based dynamic charging as a scalable, flexible, and efficient alternative to fixed charging solutions.