論文

査読有り
2013年11月

A methodology for economic and environmental analysis of electric vehicles with different operational conditions

ENERGY
  • Qi Zhang
  • ,
  • Benjamin C. Mclellan
  • ,
  • Tetsuo Tezuka
  • ,
  • Keiichi N. Ishihara

61
開始ページ
118
終了ページ
127
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.energy.2013.01.025
出版者・発行元
PERGAMON-ELSEVIER SCIENCE LTD

A simulation model is proposed in the present study to analyze economic and environmental performance of electric vehicles (EVs) operated under different conditions including electricity generation mix, smart charging control strategies and real-time pricing mechanisms. The model is organized into an input output framework and actualized using an hour-by-hour computer simulation to achieve a real-time electricity supply demand balance emphasizing the integrations of EVs. The battery cost, real-time solar and wind power generations, and traditional electricity demand are used as preconditions. The model has been developed as a flexible software package and applied to case studies in the Tokyo area, Japan in 2030 with different combinations of three electricity generation mix options, two charging control strategies and two hourly real-time electricity pricing mechanisms. The fuel costs and CO2 emissions of EVs in different operational environments were obtained and compared, and optimized operational conditions for EVs were suggested from the perspective of economic and environmental benefit. The feasibility of the proposed methodology was thereby demonstrated practically through the case studies. (C) 2013 Elsevier Ltd. All rights reserved.

リンク情報
DOI
https://doi.org/10.1016/j.energy.2013.01.025
J-GLOBAL
https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201302268429953321
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000327685000014&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.energy.2013.01.025
  • ISSN : 0360-5442
  • eISSN : 1873-6785
  • J-Global ID : 201302268429953321
  • Web of Science ID : WOS:000327685000014

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