Abstract: With the widespread adoption of electric vehicles (EVs) and the implementation of environmental policies, efficiently scheduling EV charging behaviors has become increasingly important for meeting user demands and can ensure grid stability. Addressing the different charging needs of EV owners in smart grids and the curse of dimensionality problem in traditional machine learning algorithms, this paper proposes an EV charging scheduling algorithm based on Deep Q-Network (DQN). This algorithm leverages the advantages of deep reinforcement learning, combining real-time electricity prices and vehicle status information to dynamically adjust EV charging and discharging behaviors, aiming to maximize economic benefits and optimize grid load. Experimental results show that compared with uncontrolled charging strategies, the proposed algorithm can reduce charging costs by approximately 65.6% over a 30-day test period. It effectively adapts to real-time grid price fluctuations and changes in user demands, reduces peak charging requirements, effectively lowers charging costs, and improves grid stability.

Key words: electric vehicle charging scheduling, deep reinforcement learning, energy optimization management, vehicle-to-grid, smart grid, information interactive