Reinforcement Learning based Scheduling for Cooperative EV-to-EV Dynamic Wireless Charging

Previous Electric Vehicle (EV) charging scheduling methods and EV route planning methods require EVs to spend extra waiting time and driving burden for a recharge. With the advancement of dynamic wireless charging for EVs, Mobile Energy Disseminator (MED), which can charge an EV in motion, becomes available. However, existing wireless charging scheduling methods for wireless sensors, which are the most related works to the deployment of MEDs, are not directly applicable for the scheduling of MEDs on city-scale road networks. We present MobiCharger: a Mobile wireless Charger guidance system that determines the number of serving MEDs, and the optimal routes of the MEDs periodically (e.g., every 30 minutes). Through analyzing a metropolitan-scale vehicle mobility dataset, we found that most vehicles have routines, and the temporal change of the number of driving vehicles changes during different time slots, which means the number of MEDs should adaptively change as well. Then, we propose a Reinforcement Learning based method to determine the number and the driving route of serving MEDs. Our experiments driven by the dataset demonstrate that MobiCharger increases the medium state-of-charge and the number of charges of all EVs by 50% and 100%, respectively.