The Electric Location Routing Problem under Ambient Temperature

TL;DR

This paper models the impact of ambient temperature on electric vehicle charging station location and routing decisions, proposing heuristics to optimize placement and routing under temperature variations, validated through a case study in Fargo, North Dakota.

Contribution

It introduces a mixed-integer linear programming model for EV charging station location-routing considering ambient temperature effects and develops two novel heuristics for solving it.

Findings

Charging station placement is highly sensitive to ambient temperature.

The proposed heuristics effectively optimize location and routing decisions.

Temperature significantly influences EV charging behavior and station utilization.

Abstract

This study investigates how the location-routing decisions of the electric vehicle (EV) DCFC charging stations are impacted by the ambient temperature. We formulated this problem as a mixed-integer linear programming model that captures the realistic charging behavior of the DCFC in association with the ambient temperature and their subsequent impact on the EV charging station location and routing decisions. Two innovative heuristics are proposed to solve this challenging model in a realistic test setting, namely, the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. We use Fargo city in North Dakota as a testbed to visualize and validate the algorithm performances. The results clearly indicate that the EV DCFC charging station location decisions are highly sensitive to the ambient temperature, the charging time, and the initial state of charge.