Large-scale public charging demand prediction with a scenario- and activity-based approach

Abstract

Transportation system electrification is expected to bring millions of electric vehicles (EVs) on road within decades. Accurately predicting the charging demand is necessary to accommodate the surge in EV deployment. This paper presents a novel modeling framework to predict the public charging demand profile derived from people’s travel trajectories, with the consideration of the demand and supply stochasticity of transportation systems and the charging behavior heterogeneity of EV users. The vehicle charging decision-making process is explicitly modeled, and the charging need of each EV user is estimated associated with their travel trajectories. The methodology enables charging demand prediction with a high spatial–temporal resolution for transportation system electrification planning. A case study was conducted in Los Angeles County to predict the demand for public charging facilities in 2035 and perform corresponding spatial–temporal analysis of EV public charging under various scenarios of future electrification levels and network conditions.