Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger equipment costs were found to be the most significant component in the objective function, leading to a substantial reduction in cost when decreased. VOT costs exhibited a significant decrease with rising energy costs. An increase in VOT resulted in a notable rise in the number of fast chargers. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible.

中文翻译：

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固定线路网络电动汽车供电设备选址及容量分配

由于电气化趋势的转变，货运车辆的电动汽车 (EV) 供电设备位置和分配 (EVSELCA) 问题变得越来越重要。之前的一些工作通过从头开始生成车辆路线来同时解决电动汽车充电器位置和车辆路线问题。尽管此类路线可能很高效，但引入新路线可能会违反实际限制，例如行驶时间表，并且满足电气化要求可能需要大幅改变现有路线。为了解决现行采用方案中的挑战，我们从固定路线的角度来解决问题。我们开发了混合整数线性程序、聚类方法和使用遗传算法 (GA) 的元启发式解决方法来解决 EVSELCA 问题。聚类方法通过将客户分组来简化问题，而遗传算法生成的解决方案对于小问题案例来说几乎是最佳的。案例研究研究了充电器成本、能源成本、时间价值 (VOT) 和电池容量如何影响 EVSELCA 的成本。研究发现，充电设备成本是目标函数中最重要的组成部分，降低后成本会大幅降低。随着能源成本的上升，VOT 成本显着下降。 VOT 的增加导致快速充电器数量显着增加。更长的电动汽车续航里程会在一定程度上降低总成本，超过该值，总成本的降低可以忽略不计。