Paper Info
Title
An auxiliary decision method of the charging selection for electric vehicle users based on energy internet
Abstract
Considering the different load and peak hours of the charging stations and the common anxiety of electric vehicles, the paper puts forward a method of charging decision-making based on knowledge automation for electric vehicle users. Firstly, based on the concept of automation, electric vehicle users' charging decision management framework and decision demand chart are made by knowledge component model. Secondly, taking the users' preferences and charging habits into account and obtaining information from different channels by big data to establish charge decision aided model of electric vehicle users based on knowledge automation. The model develops the real-time electricity price, according to the predictions of the real-time load in each charging station, to guide peak load shifting and improve the utilization rate of charging station equipment. Finally, the analytic hierarchy process (AHP) and TOPSIS (TOPSIS) are used to establish a comprehensive evaluation index system of charge select based on AHP-TOPSIS to complete the optimal choice of electric vehicle users. the feasibility of the method is verified by the example analysis.
Year
DOI
Venue
2017
10.1109/IECON.2017.8216018
IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Keywords
Field
DocType
electric vehicle,charging selection,knowledge automation,decision-aid,analytic hierarchy process,ranking method of approaching ideal solution
Electric vehicle,Charging station,Operations research,Automation,Control engineering,Decision management,Decision model,TOPSIS,Engineering,Big data,Analytic hierarchy process
Conference
ISSN
ISBN
Citations 
1553-572X
978-1-5386-1128-9
0
PageRank 
References 
Authors
0.34
1
5
Name
Order
Citations
PageRank
Ying Zhong111.03
Bin Duan211.73
Yinxin Yan300.68
Zhuang Yang400.34
Qiaoxuan Yin500.68