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.
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.
| Original language | English |
|---|---|
| Article number | 2578666 |
| Number of pages | 17 |
| Journal | Inventions-Special Issue "Connected Vehicles and Charging Infrastructure: Innovations and Security" |
| Publication status | Published - 2023 |