Effective Charging Planning Based on Deep Reinforcement Learning for Electric Vehicles

Abstract

Electric vehicles (EVs) are viewed as an attractive option to reduce carbon emission and fuel consumption, but the popularization of EVs has been hindered by the cruising range limitation and the inconvenient charging process. In public charging stations, EVs usually spend a lot of time on queuing especially during peak hours of charging. Therefore, building an effective charging planning system has become a crucial task to reduce the total charging time for EVs. In this paper, we first introduce EVs charging scheduling problem and prove the NP-hardness of the problem. Then, we formalize the scheduling problem of EV charging as a Markov Decision Process and propose deep reinforcement learning algorithms to address it. The objective of the proposed algorithms is to minimize the total charging time of EVs and maximal reduction in the origin-destination distance. Finally, we experiment on real-world data and compare with two baseline algorithms to demonstrate the effectiveness of our approach. It shows that the proposed algorithms can significantly reduce the charging time of EVs compared to EST and NNCR algorithms.