Electric vehicles fast charger location-routing problem under ambient temperature

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's 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.

Optimal timing for nonsubsidized policy implementation in the electric vehicle industry

“…Another stream of literature closely related to our paper is the studies on the nonsubsidized policies for EVs, for example, building EV charging stations (Aghalari et al, 2021; Kumar et al, 2021; Liu et al, 2023; Yu et al, 2022), license plate (Li, Jiao, & Tang, 2019; Xian et al, 2022), free parking and road toll (Zhang & Qin, 2018). A growing number of researchers have focused on the charging infrastructure, such as the optimal distribution of chargers (Kontou et al, 2019; Yang et al, 2017; Yi & Bauer, 2016; Yu et al, 2016) and the investment model (Kumar et al, 2021; Wang & Deng, 2019; Yu et al, 2022).…”