Optimal Siting and Sizing of Electric Vehicle Energy Supplement Infrastructure in Highway Networks

Abstract

The electric vehicle (EV) market is expanding rapidly to achieve the future goal of eco-friendly transportation. The scientific planning of energy supplement infrastructures (ESIs), with appropriate locations and capacity, is imperative to develop the EV industry. In this research, a mixed integer linear programming (MILP) model is proposed to optimize the location and capacity of ESIs, including vehicle charging stations (VCSs), battery swapping stations (BSSs), and battery charging stations (BCSs), in highway networks. The objective of this model is to minimize the total cost with the average waiting time for EVs being constrained. In this model, battery swapping and transportation behaviors are optimized such that the EV average waiting time can be reduced, and the average queue and service process waiting time is estimated by the M/M/1 model. Real-world data, i.e., from the London M25 highway network system, are used as a case study to test the effectiveness of the proposed method. The results show that considering battery transportation behaviors is more cost efficient, and the results are sensitive to the EV average waiting time tolerance, battery cost, and charging demand.

Keywords

• mixed integer linear programming
• EV charging station
• battery swapping station
• battery transportation
• M/M/1 queue model