Spatiotemporal Prediction of Urban EV Charging Demand Through Synthesizing Electromobility Trajectories in an Integrated EV-Transportation-Land Use-Energy Framework

Abstract

In the absence of empirical data on urban electric vehicle (EV) mobility and charging demand, this paper presents a methodology to predict spatiotemporal urban charging demand by synthesizing electromobility trajectories (e-trajectories) within an integrated EV-Transportation-Land Use-Energy (EVTLUE) framework. The EVTLUE comprises: 1) a power-based EV model for estimating EV energy consumption; 2) an integrated model combining a graph-based street network with land-use polygons; 3) a dynamic traffic module leveraging Google Maps traffic data; and 4) an optimal planning model for the deployment of slow chargers in land-use areas and fast charging stations (FCS) on high-traffic streets. For each EV trip, an e-trajectory is synthesized from interactions between the EV user and EVTLUE across driving, charging, and idle sub-trajectories. The driving sub-trajectory is simulated using user driving behavior to infer trip purpose, destination, and route choices, and to generate profiles of speed, energy consumption, and battery state. The charging sub-trajectory is derived from user charging behavior, modeling decisions regarding the time, location, and mode of charging under four charging strategies: obligatory, convenient, postponement, and delayed charging. Daily e-trajectories for EV users are synthesized to predict individual charging demand profiles, which are then aggregated to derive the spatiotemporal distribution of slow- and fast-charging demand across the urban area. A numerical simulation for 45,360 EVs in Campinas, Brazil, reveals a total urban charging load of 1,164.18 MWh, with 77.66% attributed to slow charging and 22.34% to fast charging. Slow charging demand (904 MWh) is distributed across homes, workplaces, and public locations with respective shares of 42.21%, 44.16%, and 13.63%. Fast charging demand (260.1 MWh) is met by 71 optimally planned FCSs.