Large-scale public charging demand prediction with a scenario- and activity-based approach
提出一个建模框架，基于人们出行轨迹预测公共充电需求曲线，考虑交通系统供需随机性和用户充电行为异质性，并在洛杉矶县进行案例预测2035年充电需求。
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.