Ankur Sarker, Chenxi Qiu, Haiying Shen, A. Gil, J. Taiber, M. Chowdhury, Jim Martin, Mac Devine, A. J. Rindos
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引用次数: 23
Abstract
As an alternate form in the road transportation system, electric vehicle (EV) can help reduce the fossil-fuel consumption. However, the usage of EVs is constrained by the limited capacity of battery. Wireless Power Transfer (WPT) can increase the driving range of EVs by charging EVs in motion when they drive through a wireless charging lane embedded in a road. The amount of power that can be supplied by a charging lane at a time is limited. A problem here is when a large number of EVs pass a charging lane, how to efficiently distribute the power among different penetrations levels of EVs? However, there has been no previous research devoted to tackling this challenge. To handle this challenge, we propose a system to balance the State of Charge (called BSoC) among the EVs. It consists of three components: i) fog-based power distribution architecture, ii) power scheduling model, and iii) efficient vehicle-to-fog communication protocol. The fog computing center collects information from EVs and schedules the power distribution. We use fog closer to vehicles rather than cloud in order to reduce the communication latency. The power scheduling model schedules the power allocated to each EV. In order to avoid network congestion between EVs and the fog, we let vehicles choose their own communication channel to communicate with local controllers. Finally, we evaluate our system using extensive simulation studies in Network Simulator-3, MatLab, and Simulation for Urban MObility tools, and the experimental results confirm the efficiency of our system.
电动汽车作为道路交通系统的一种替代形式,有助于减少化石燃料的消耗。然而,电动汽车的使用受到电池容量的限制。无线电力传输技术(WPT)通过嵌入道路的无线充电通道,为行驶中的电动汽车充电,从而增加电动汽车的续航里程。充电通道一次能提供的电量是有限的。问题是,当大量电动汽车通过充电车道时,如何在不同穿透水平的电动汽车之间有效地分配电力?然而,之前还没有专门针对这一挑战的研究。为了应对这一挑战,我们提出了一种平衡电动汽车充电状态(BSoC)的系统。它由三个部分组成:1)基于雾的配电架构,2)电力调度模型,3)高效的车雾通信协议。雾计算中心收集电动汽车的信息,并调度配电。为了减少通信延迟,我们使用离车辆更近的雾而不是云。功率调度模型对分配给每台EV的功率进行调度。为了避免电动汽车之间的网络拥塞和雾,我们让车辆选择自己的通信通道与本地控制器通信。最后,我们使用Network Simulator-3、MatLab和simulation for Urban MObility工具对我们的系统进行了广泛的仿真研究,实验结果证实了我们系统的效率。