Operating Status Prediction Model at EV Charging Stations With Fusing Spatiotemporal Graph Convolutional Network

Abstract

This article proposes the operating status prediction model at electric vehicle (EV) charging stations based on the spatiotemporal graph convolutional network (SGCN). The SGCN combines graph convolutional network (GCN) and the gated recurrent unit (GRU), alleviating the queuing time at charging stations due to the lack of information for EV users. First, an urban charging station-traffic flow model is established to portray the interrelationship between charging stations and traffic. Second, a multistep prediction model based on SGCN for operating status at charging stations is proposed to forecast the occupancy of charging stations over the next tens of minutes. The comparison case study with the forecast and actual data reveals that the mean forecast error is around 19.21% when estimating 18 min ahead. Incrementing errors are subtle even after adding random noise to the original data. Finally, the model is applied to charging guidance decisions. Our model can reduce the number of EV queuing by 60% during high charging demand. It also shortens the average charging waiting time by 4 min.