Real-Time Electric Vehicle Intelligent Charging Scheduling Strategy in Real Traffic Scenarios

With the rapid development of social production and the economy, environmental problems have increasingly become prominent. Electric vehicles are very popular due to their characteristics of zero pollution emmissions, which has led to the increasing scale of electric vehicles. As the number of electric vehicles increases, the problem of traffic congestion has become more and more serious, and the difficulty of charging has become a problem for people. How to solve the timeliness and uncertainty of electric vehicle charging and reduce the electric vehicle’s charging costs are two challenges in the new energy field. In this paper, we focus on the real-time electric vehicle charging problem with the consideration of road conditions and weather influence. By constructing state, action, system reward, and state transition functions, the problem of electric vehicle charging scheduling is formulated as a Markov Decision Process. We propose a Soft Actor-Critic algorithm based on deep reinforcement learning to dynamically learn the optimal charging strategy with the aim of minimizing charging time and battery power consumption for users, to improve the charging experience. In addition, we design a deep learning model for real-time electricity price prediction to assist intelligent charging decisions and further save charging costs for users. Numerical experimental results verify the effectiveness and superiority of our proposed method.