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The paper presents a study of the profile of the load imposed on a power system by grid-charging of the onboard battery pack of electric and plug-in hybrid vehicles. The study uses a large database of field-recorded driving cycles stamped with parking times and locations to predict realistic driving habits of drivers in an urban setting. A fuzzy-logic inference system is designed to emulate the decision-making process of a driver when deciding to charge the vehicle's battery. The charging load is then estimated on an hourly basis for a number of electric and plug-in hybrid vehicles with different storage capacities. Level-1 and level-2 charging regimes as well as two scenarios for charging, namely charging at home and charging at home and work, are considered. The load profile is presented as an hourly probability of charging for each vehicle type. The results demonstrate how penetration of plug-in hybrid and electric vehicles affects the load on a utility network.