{"title":"Toward electric vehicle trip prediction for a charging service provider","authors":"O. Sundstrom, Olivier Corradi, C. Binding","doi":"10.1109/IEVC.2012.6183221","DOIUrl":null,"url":null,"abstract":"This paper outlines the need for and the requirements of trip prediction to optimally derive the charging behavior of plug-in electric vehicles. The information required for trip prediction by a charging-service provider is shown, and a novel trip prediction model is proposed. The proposed model is a semi-Markov model that predicts the next arrival location and the waiting time at the current location. Combining this with a prediction of the energy need and the duration of the trip to the predicted location provides a basis for determining the charging behavior. The proposed prediction model is compared with a naive predictor that uses yesterday's trips to predict today's trips. It is shown that the proposed model predicts the next location with 84% accuracy.","PeriodicalId":134818,"journal":{"name":"2012 IEEE International Electric Vehicle Conference","volume":"113 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Electric Vehicle Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEVC.2012.6183221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
This paper outlines the need for and the requirements of trip prediction to optimally derive the charging behavior of plug-in electric vehicles. The information required for trip prediction by a charging-service provider is shown, and a novel trip prediction model is proposed. The proposed model is a semi-Markov model that predicts the next arrival location and the waiting time at the current location. Combining this with a prediction of the energy need and the duration of the trip to the predicted location provides a basis for determining the charging behavior. The proposed prediction model is compared with a naive predictor that uses yesterday's trips to predict today's trips. It is shown that the proposed model predicts the next location with 84% accuracy.