Facility location optimization of battery electric vehicle (BEV) fast charging stations in an urban transportation network

Abstract

Electric Vehicles (EVs) are expected to play a significant role in the reduction of global emissions originating from the transportation sector. Penetration of EVs has been shown to be a significant enabler for rapid adoption of EV technology among light-duty passenger vehicles. An analysis of contemporary EV charging technologies and charging mechanics is performed. The location of EV fast charging facilities in an urban transportation network is posited as a part of an existing body of research on facility location optimization. A flow capturing facility location problem is formulated for identifying optimal locations of EV fast charging points in an urban transportation network. In the solution, vehicular traffic is allocated using a shortest path algorithm to optimally located charging stations. The weighted total supply chain costs consisting of operating costs (OC), travel costs (TC), and service costs (SC) is minimized. A concave service cost function is included to ensure adherence to preset service levels, which is then approximated as a set of linear constraints. A computationally efficient solution to the mixed integer linear programming (MILP) problem is obtained using IBM ILOG CPLEX. The sensitivity of optimum facility count and computational time with increasing order of magnitude of operating cost is analyzed. Using a social equity approach, facility count and total costs are analyzed by restricting the maximum customer detour permitted within the model.