EV充電ナビシステム実証実験による消費電力量予測の検証

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2020/02/01

タイトル(英語): Study on Energy Consumption Prediction by Demonstration Experiment for EV Charging Navigation System

著者名: 矢野　亨（（株）東芝 研究開発センター），福島　亜梨花（（株）東芝 研究開発センター），下川　裕亮（東芝インフラシステムズ（株） 社会システム事業部），柴田　康弘（東芝インフラシステムズ（株） 社会システム事業部），能登谷　英樹（東日本高速道路（株） 事業創造企画室），田村　聡一朗（東日本高速道路（株） 事業創造企画室）

著者名(英語): Toru Yano (Corporate Research & Development Center, Toshiba Corporation), Arika Fukushima (Corporate Research & Development Center, Toshiba Corporation), Yusuke Shimokawa (Toshiba Infrastructure Systems & Solutions Corporation), Yasuhiro Shibata (Toshiba Infrastructure Systems & Solutions Corporation), Hideki Notoya (East Nippon Expressway Company Limited), Soichiro Tamura (East Nippon Expressway Company Limited)

キーワード: 電気自動車，消費電力量，SOC，予測　　electric vehicles，energy consumption，state-of-charge，prediction

要約(英語): This study shows performance evaluation of a data-driven prediction model for energy consumption of electric vehicles (EVs) on expressways by using data obtained from a demonstration experiment where the authors and testers from the public actually drove EVs with the proposed EV charging navigation system in 2018. By comparing actual energy consumption and predicted one, we have found that the predicted model has a good prediction accuracy, whereas predicted results tend to deviate from actual ones in the region where actual energy consumption is 15 kWh or more. We also confirmed a possibility that high velocity may degrade prediction performance. With respect to error of state of charge (SOC), most of the SOCs calculated by the prediction model do not have more than 20% error, which may lead to run-out of electricity of EV. Although the prediction model is effective in respect of SOC error, the result indicates a necessity that automatic grouping and prediction model based on such groups should be studied as a future work.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.2 （2020）　特集：エネルギーデータを対象としたIoT，AI活用技術

本誌掲載ページ: 164-173 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/2/140_164/_article/-char/ja/