{"title":"基于充电服务提供商的电动汽车行程预测研究","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":"{\"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}","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}
Toward electric vehicle trip prediction for a charging service provider
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.