Abstract:

Battery electric vehicles (BEVs) have gained an increasing popularity over the past decade due to their environmental friendliness and high energy efficiency. This study aims to determine the optimal deployment of charging stations for BEVs by maximizing the covered path flows taking into account the path deviation and nonlinear elastic demand (DCSDE). Under the assumption that the travel demand between OD pairs follows a nonlinear inverse cost function with respect to the generalized travel cost, a BCAP-based (battery charging action-based path) model will be first formulated for DCSDE problem. A tailored branch-and-price (B&P) approach is proposed to solve the model. The pricing problem to determine an optimal path of BEV is not easily solvable by available algorithms due to the path-based nonlinear cost term in the objective function. This paper thus proposes a customized two-phase method for the pricing problem. The model framework and solution method can easily be extended to incorporate other practical requirements in the context of e-mobility, such as the maximal allowable number of stops for charging and the asymmetric round trip. The numerical experiments in a benchmark 25-node network and a real-world California State road network are conducted to assess the efficiency of the proposed model and solution approach. The current study mainly focuses on the location of charging stations in highway networks. Another line of future studies may concern the optimal deployment of charging stations in an urban environment subject to more sophisticated constraints, such as road congestion, queue formation at charging stations due to limited capacities and long charging time.