  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.
