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Plug-in electric vehicles (PEVs) can contribute to energy and environmentalchallenges. Among different types of PEVs, plug-in hybrid electric taxis(PHETs) go in advance. In this study, we provide a spatial and temporal PHETcharging demand forecasting method based on one-month global positioning system(GPS)-based taxi travel data in Beijing. Then, using the charging demandforecasting results, a mixed integer linear programming (MILP) model isformulated to plan PHET charging stations in the central area of Beijing. Themodel minimizes both investment and operation costs of all the PHET chargingstations and takes into account the service radius of charging stations,charging demand satisfaction and rational occupation rates of chargers. Atlast, the test of the planning method is carried out numerically throughsimulations and the analysis is complemented according to the results.

Data-Driven Planning of Plug-in Hybrid Electric Taxi Charging Stations in Urban Environments: A Case in the Central Area of Beijing