高速公路服务站电动汽车充电在线定价策略及数据缓存

Zhou Su, Tianxin Lin, Qichao Xu, Nan Chen, Shui Yu, Song Guo
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引用次数: 3

摘要

随着交通电气化和车联网技术的进步,越来越多的电动汽车及其相关基础设施(如兼具充电和通信服务的服务站)被部署到智能公路系统中。电动汽车不仅可以进入服务站区域充电,还可以在服务站上传/下载缓存数据,以访问多种网络服务。然而,由于电动汽车是单独运行的,其独特的行驶模式,如何激励电动汽车,使能源和通信资源得到最佳分配的问题就出现了。本文提出了一种基于高速公路沿线服务站的电动汽车充电在线定价机制和数据缓存机制。首先,我们在每辆电动汽车上设计了一个在线预约系统,以确定电动汽车进入高速公路时的最佳停车服务站。在此基础上,针对电力系统状态的变化,设计了一种基于q -学习的在线定价机制,对充电和缓存价格进行更新,激励电动汽车到达指定站点进行服务。最后,仿真结果验证了该方案在提高车站利用率方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Online Pricing Strategy of EV Charging and Data Caching in Highway Service Stations
With the technical advancement of transportation electrification and Internet of vehicle, an increasing number of electric vehicles (EVs) and related infrastructures (e.g., service stations with both charging and communication services) are deployed in the intelligent highway systems. Not only can EVs enter the service station areas for charging, but they can also upload/download cached data at service stations to access multiple networking services. However, as EVs are operated individually with their unique travelling patterns, questions arise as how to incent EVs so that both energy and communication resources are optimally allocated. In this paper, we propose an online pricing mechanism of EV charging and data caching for service stations along the highway. First, we design an online reservation system at each EV to decide the best service station to park when the EV enters the highway. Furthermore, based on the variant power system status, an online pricing mechanism is devised to update the charging and caching price based on Q-learning, by which EVs can be motivated to arrive at the designated station for services. Finally, simulation results validate the effectiveness of the proposed scheme in improving the station’s utility.
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