Abstract

With the rising need for efficient and flexible short-distance urban transportation, more vehicle sharing companies are offering one-way car-sharing services. Electrified vehicle sharing systems are even more effective in terms of reducing fuel consumption and carbon emission. In this article, we investigate a dynamic fleet management problem for an Electric Vehicle (EV) sharing system that faces time-varying random demand and electricity price. Demand is elastic in each time period, reacting to the announced price. To maximize the revenue, the EV fleet optimizes trip pricing and EV dispatching decisions dynamically. We develop a new value function approximation with input convex neural networks to generate high-quality solutions. Through a New York City case study, we compare it with standard dynamic programming methods and develop insights regarding the interaction between the EV fleet and the power grid.

摘要

随着对高效、灵活的短途城市交通的需求不断增加，越来越多的汽车共享公司开始提供单向汽车共享服务。电动汽车共享系统在降低油耗和碳排放方面更加有效。在本文中，我们研究了电动汽车 (EV) 共享系统的动态车队管理问题，该系统面临时变的随机需求和电价。每个时间段的需求都是有弹性的，对公布的价格做出反应。为了最大限度地提高收入，电动汽车车队会动态优化行程定价和电动汽车调度决策。我们使用输入凸神经网络开发了一种新的价值函数近似，以生成高质量的解决方案。通过纽约市的案例研究，