Distributed Routing and Charging Scheduling Optimization for Internet of Electric Vehicles

作     者：Tang, Xiaoying Bi, Suzhi Zhang, Ying-Jun Angela 

作者机构：Ecole Polytech Fed Lausanne Sch Comp & Commun Sci CH-1010 Lausanne Switzerland Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2019年第6卷第1期

页      面：136-148页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：General Research Funding through the Research Grants Council of Hong Kong [14209414, 14208107] National Natural Science Foundation of China [61501303, 61871271] Guangdong Province Pearl River Scholar Funding Scheme Department of Education of Guangdong Province [2017KTSCX163] Foundation of Shenzhen City [JCYJ20170818101824392] Science and Technology Innovation Commission of Shenzhen [827/000212] 

主　　题：Charging scheduling distributed algorithm electric vehicles (EVs) Internet of Things (IoT) routing 

摘      要：In this paper, we consider an Internet of Electric Vehicles (IoEV) powered by heterogeneous charging facilities in the transportation network. In particular, we take into account the state-of-the-art vehicle-to-grid (V2G) charging and renewable power generation technologies implemented in the charging stations, such that the charging stations differ from each other in their energy capacities, electricity prices, and service types (i.e., with or without V2G capability). In this case, each electric vehicle (EV) user needs to decide which path to take (i.e., the routing problem) and where and how much to charge/discharge its battery at the charging stations in the chosen path (i.e., the charging scheduling problem) such that its journey can be accomplished with the minimum monetary cost and time delay. From the system operator s perspective, we formulate a joint routing and charging scheduling optimization problem for an IoEV network, and show that the problem is NP-hard in general. To tackle the NP-hardness, we propose an approximate algorithm that can achieve affordable computational complexity in large-size IoEV networks. The proposed algorithm allows the routing and charging solution to be calculated in a distributed manner by the system operator and EV users, which can effectively reduce the computational complexity at the system operator and protect the EV users privacy and autonomy. Besides, a proximal method is introduced to improve the convergence rate of the proposed algorithm. Extensive simulations using real world data show that the proposed distributed algorithm can achieve near-optimal performance with relatively low computational complexity in different system set-ups.