Vicente Torres-Sanz, Julio A. Sanguesa, Piedad Garrido, F. Martinez, C. Calafate, J. Márquez-Barja
{"title":"On the prediction of electric vehicles energy demand by using vehicular networks","authors":"Vicente Torres-Sanz, Julio A. Sanguesa, Piedad Garrido, F. Martinez, C. Calafate, J. Márquez-Barja","doi":"10.1109/WD.2017.7918143","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a comprehensive architecture based on vehicular communication technologies, considering vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In addition, we present a study about EVs charging load. Our proposal addresses three main issues: (i) knowledge of the number of vehicles that are going to recharge their batteries at a particular point and instant, (ii) knowledge of the available charging points, and (iii) predicting the electricity demand. Results show that our system is able to predict the electricity requirements of the EVs that are expected to recharge their batteries up to 180 minutes in advance.","PeriodicalId":179998,"journal":{"name":"2017 Wireless Days","volume":"275 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Wireless Days","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WD.2017.7918143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we propose a comprehensive architecture based on vehicular communication technologies, considering vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In addition, we present a study about EVs charging load. Our proposal addresses three main issues: (i) knowledge of the number of vehicles that are going to recharge their batteries at a particular point and instant, (ii) knowledge of the available charging points, and (iii) predicting the electricity demand. Results show that our system is able to predict the electricity requirements of the EVs that are expected to recharge their batteries up to 180 minutes in advance.