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This paper presents a spatial-temporal urban charging power demand forecasting method based on EV travel trajectory simulation in the integrated urban street network and functional zones. Each EV trip is simulated via a travel trajectory based on the EV user's travel behavior, which refers to the user's decision-making processes regarding the choices of trip purpose, departure time, destination, trip route, parking time, and the time, location, and mode of EV charging. EV's daily travel trajectory is built by merging the travel trajectories of the EV's daily trips. The spatial-temporal slow and fast urban charging power demands are predicted using the simulated daily travel trajectories of available EVs in the urban area. The proposed simulation method is applied in the urban area of Campinas, Brazil. The results indicate that EVs' daily travel trajectories are realistically produced to predict the daily charging load profiles at urban activity locations and functional zones.
Mahmoudi et al. (Sat,) studied this question.