Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

Abstract

In the past few years, the importance of electric mobility has increased in response to growing concerns about climate change. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of electric vehicles (EVs). To mitigate the problem, the need for optimal route planning algorithms emerged. In this paper, we propose a mathematical formulation of the EV-specific routing problem in a graph-theoretical context, which incorporates the ability of EVs to recuperate energy. Furthermore, we consider a possibility to recharge on the way using intermediary charging stations. As a possible solution method, we present an off-policy model-free reinforcement learning approach that aims to generate energy feasible paths for EV from source to target. The algorithm was implemented and tested on a case study of a road network in Switzerland. The training procedure requires low computing and memory demands and is suitable for online applications. The results achieved demonstrate the algorithm’s capability to take recharging decisions and produce desired energy feasible paths.

A Novel Routing Solution for EV Fleets: A Real-World Case Study Leveraging Double DQNs and Graph-Structured Data to Solve the EVRPTW Problem

“…Reinforcement learning techniques, which are the main focus of the current study, have also been explored. The Q-learning algorithm was applied by Ottoni et al [6] and Dorokhova et al [5] while the authors of [9] used the State-Action-Reward-State-Action (SARSA) algorithm. Similarly [6] used SARSA to compare with Q learning.…”