Range coverage location model: An optimization model for the charging station location problem in a transportation network to cover intercity travels

Yilmaz H., Yağmahan B.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, vol.46, no.2, pp.1538-1552, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1002/er.7268
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Page Numbers: pp.1538-1552
  • Keywords: charging station location problem, driving range, electric vehicles, highway network, integer linear programming, ELECTRIC VEHICLES, INFRASTRUCTURE, FORMULATION
  • Bursa Uludag University Affiliated: Yes


Equipping highways with charging stations (CSs) is a fundamental step for travelling with electric vehicles (EVs) between cities and countries conveniently. This article focuses on locating CSs to fully connect roads that may require multiple charging events by considering the minimum driving range for all possible paths in a transportation network. For this purpose, we present a new binary integer linear programming model named the Range Coverage Location Model (RCLM) to find the minimum required CSs and their locations that the driving range can cover without defining the paths in the network. By adding the result of RCLM as a constraint to the model, the optimum locations that maximize the EV flows are determined with the RCLM-Max model. Two versions of the RCLM are introduced. The link-based RCLM is designed for problems in which there are CSs in each of the origin/destination (OD) nodes (intersections), while the network-based RCLM aims to connect links without stopping by the OD nodes, making the model stricter but convenient for EV travels. The proposed models are validated through extensive computational experiments with real data from a highway network in Turkey. The experiments show that RCLM and RCLM-Max can solve very large-scale problems in a very short CPU time. The findings suggest that the link-based RCLM can be applied when the budget is at the forefront, and the network-based model is preferred if the aim is to connect the main roads without stopping by the OD nodes.