Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers
It addresses urban electric vehicle charging inefficiencies by combining fixed and mobile chargers, offering a novel solution to fluctuating demand.
This paper tackles the problem of inefficient charging infrastructure for electric vehicles by optimizing the planning and operation of hybrid systems with fixed and mobile chargers, resulting in up to 244.4% increased coverage and 79.8% reduced waiting times.
The success of vehicle electrification relies on efficient and adaptable charging infrastructure. Fixed-location charging stations often suffer from underutilization or congestion due to fluctuating demand, while mobile chargers offer flexibility by relocating as needed. This paper studies the optimal planning and operation of hybrid charging infrastructures that combine both fixed and mobile chargers within urban road networks. We formulate the Hybrid Charging Station Planning and Operation (HCSPO) problem, jointly optimizing the placement of fixed stations and the scheduling of mobile chargers. A charging demand prediction model based on Model Predictive Control (MPC) supports dynamic decision-making. To solve the HCSPO problem, we propose a deep reinforcement learning approach enhanced with heuristic scheduling. Experiments on real-world urban scenarios show that our method improves infrastructure availability - achieving up to 244.4% increase in coverage - and reduces user inconvenience with up to 79.8% shorter waiting times, compared to existing solutions.