Abstract
This paper investigates the impact of the drivers dynamic behaviors on the demand of charging Plug-in Electric Vehicles (PEVs). The presented work in this study focuses on modeling the temporal-spatial characteristics of the drivers daily activities based on obtaining all statistical valuable information from the National Household Travel Survey (NHTS) data. The proposed work considers multiple trips per day and takes into account drivers movement, Driving Duration (DDT), Driven Distance (DDM), and Parking Duration (PDT). The trip chain, bases on the Monte Carlo Markov Chain (MCMC) process, is developed to properly model the driver states during the day. Charging facilities are assumed to be available at home, work, and Direct Current (DC) Fast Charging Stations (FCSs), within charging levels of 3.7 kW, 6.6 kW, and 50 kW, respectively. In this work, the impact of PEV charging at multi locations is discussed, and is followed by studying the impact of the market share versus Tesla S. The proposed work overcomes the disadvantages in the oversimplified models of the PEV charging demand by providing a more robust and realistic representation of driver behaviors.