Abstract

This study investigates the effects of key technological and operating factors on daily routing of battery electric truck (BET) in urban goods delivery. The key factors are service area, number of customers, battery capacity, number of charging stations, vehicle capacity, and charging rate. A primary cost metric used is daily vehicle routing cost (DVRC), which is defined as the sum of driver salary associated with driving time and idling time for en-route battery recharging activities, and electricity (energy) cost. It is estimated with a proposed green EVRP (G-EVRP) model. Overall, BET works with the economies of scale. However, BET may be better suited for a market place with high number of customers (customer density) at a city or a county scale but often technologically or financially constrained at a regional (intercity) scale. For a given network size and number of customers, battery capacity must be carefully chosen to minimize the BET DVRC. Vehicle capacity affects the number of vehicles needed to dispatch and the optimal fleet size is a compromise between the level of consolidation and that of en-route recharging activity. Little effect is found in number of charging stations on BET daily routing in the study setting as optimal routing strategies typically result in either zero or very low en-route recharging activity level.

摘要

这项研究调查了关键技术和操作因素对城市货运中电池电动卡车（BET）日常路线的影响。关键因素是服务区域，客户数量，电池容量，充电站数量，车辆容量和充电率。所使用的主要成本度量标准是每日车辆路线成本（DVRC），其定义为与驾驶员在路途中为电池充电活动所花费的时间，驾驶时间和怠速时间以及电力（能源）成本之和。使用建议的绿色EVRP（G-EVRP）模型进行估算。总体而言，BET与规模经济合作。但是，BET可能更适合于城市或县级客户数量众多（客户密度）但市场在技术或财务上受区域（城际）规模限制的市场。对于给定的网络规模和客户数量，必须谨慎选择电池容量以最小化BET DVRC。车辆容量会影响需要调度的车辆数量，而最佳车队规模则是整合水平与沿途充电活动水平之间的折衷。在研究环境中，BET日常路线上的充电站数量几乎没有影响，因为最佳路线策略通常会导致零或非常低的路线充电活动水平。