Scheduling intelligent charging robots for electric vehicle: A deep reinforcement learning approach

The surge in popularity of electric vehicles (EVs) has created a need for adaptable and flexible charging infrastructure. Intelligent Charging Robots (ICRs) have emerged as a promising solution to overcome issues faced by fixed charging stations, such as insufficient coverage, station occupancy, spatial constraints, and strain on the power grid. Nonetheless, optimizing the operational efficiency of ICRs presents a significant challenge. This study focuses on optimizing the scheduling of ICRs in a public parking facility through Deep Reinforcement Learning (DRL) methods. We first introduce the Intelligent Charging Robots Scheduling Problem (ICRSP) that maximizes either the number of EVs served (MN) or the total output electricity of ICRs (ME), and establish the corresponding mathematical model. Then, a DRL framework based on the Transformer structure is proposed to tackle ICRSP by integrating decisions of ICR assignment and EV sequencing to enhance solution quality. Furthermore, we devise a masking mechanism in the decoder to manage ICRs’ self-charging behavior during the charging service. Finally, experimental results validate the effectiveness of the proposed DRL approach in providing efficient scheduling solutions for large-scale ICRSP instances. The comparative analysis of MN-ICRSP and ME-ICRSP models offers valuable insights for ICRs operation scheduling, aiding in balancing operator revenue and customer satisfaction.