Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers

TL;DR

This paper presents a deep reinforcement learning approach for planning and operating hybrid fixed and mobile EV charging stations, significantly improving coverage and reducing waiting times in urban networks.

Contribution

It introduces the HCSPO problem formulation and a novel RL-based solution with heuristic scheduling for hybrid charging infrastructure optimization.

Findings

Up to 244.4% increase in coverage.

Up to 79.8% reduction in waiting times.

Effective dynamic decision-making in urban scenarios.

Abstract

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.