文献类型：期刊文献

英文题名：ADMM-Based decentralized electric vehicle charging with trip duration limits

作者：He, Gaoqi[1,2]; Chai, Zhifu[2]; Lu, Xingjian[1]; Kong, Fanxin[3]; Sheng, Bing[4]

机构：[1] School of Computer Science and Technology, East China Normal University, China; [2] Department of Computer Science and Engineering, East China University of Science and Technology, China; [3] Department of Electrical Engineering and Computer Science, Syracuse University, United States; [4] Department of Computer Science and Engineering, Shanghai Jiaotong University, China

年份：2019

卷号：2019-December

起止页码：107

外文期刊名：Proceedings - Real-Time Systems Symposium

收录：EI(收录号：20201608458691)

语种：英文

外文关键词：Integer programming - Traffic congestion - Quality of service - Charging (batteries)

摘要：With the large-scale deployment of Electric Vehicles (EVs), the unbalanced distribution of charging needs and random charging behaviors cause charging stations (CSs) congestion. This degrades EV drivers' quality of experience by extending charging waiting time and increasing charging fee. Thus, EV owners are facing a critical issue on how to decrease the cost of charging, which consists of two parts: charging duration and charging fee. A great deal of existing work is confined to finding CSs to optimize the two parts individually. However, it still remains unexplored how to jointly minimize charging duration and charging fee under an overall time limit (i.e., deadline) of a scheduled trip. The problem is the focus of this paper. First, we formulate this problem as a 0-1 Integer Linear Programming problem and show its NP-Hardness. Then, we propose an efficient distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM). The algorithm decomposes the original problem into sub-problems that can be solved locally and in parallel between charging stations and the global coordinator. Finally, we carry out extensive simulations based on real-life transport network data, and the results show that the proposed approach brings significant cost savings over existing ones. ? 2019 IEEE.