Locating Electric Vehicle Charging Stations

Abstract

Access to electric vehicle (EV) charging stations will affect EV adoption rates, use decisions, electrified mile shares, petroleum demand, and power consumption across times of day. Parking information from more than 30,000 records of personal trips in the Puget Sound, Washington, Regional Council's 2006 Household Activity Survey is used to determine public (nonresidential) parking locations and durations. Regression equations predict parking demand variables (i.e., total vehicle hours per zone or neighborhood and parked time per vehicle trip) as a function of site accessibility, local job and population density, trip attributes, and other variables available in most regions and travel surveys. Several of these variables are key inputs to a mixed-integer programming problem developed to determine optimal location assignments for EV charging stations. The algorithm minimizes EV users' costs for station access while penalizing unmet demand. This useful specification is used to determine the top locations for installing a constrained number of charging stations within 10 mi of the downtown area of Seattle, Washington, and shows how the access costs of charging location schemes respond to parking demand and station location. The models developed here are generalizable to data sets available for almost any region and can be used to make more informed decisions about station locations around the world.