An Online Degradation Forecasting and Abatement Framework for Hybrid Electric Vehicles
- 提供方法
- 版元よりダウンロードリンクを連絡
- 形態
- 価格
- 一般価格(税込)：￥6,600 会員価格(税込)：￥5,280
- 文献・情報種別
- SAE Paper
No.2021-01-0161
- 掲載ページ
- 1-6(Total 6 p)
- 発行年月
- 2021年 4月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- SAE WCX Digital Summit 2021
書誌事項
| 著者（英） | 1) Phuong Huu Hoang, 2) Gokhan Ozkan, 3) Payam Ramezani Badr, 4) Christopher S. Edrington, 5) Behnaz Papari |
|---|---|
| 勤務先（英） | 1) Clemson University, 2) Clemson University, 3) Clemson University, 4) Clemson University, 5) University of North Carolina Charlotte |
| 抄録（英） | The increasing electrification of vehicles raises system reliability concerns as the electrical and electronic components deteriorate faster after an event. In addition, the traditional method of scheduled maintenance is not efficient for managing a fleet of vehicles; because, the degradation processes are distinct in different vehicles. Therefore, integrating an online degradation forecasting and abatement module into a vehicle that is able to assess the vehicle status and predict the degradation process to take timely appropriate actions to reach satisfactory reliability and long-term goals, is valuable. Quantifying uncertainty is one of the main challenges of degradation forecasting; because, the degradation process of a vehicular system is distinct. This paper proposes an online degradation forecasting framework to predict the degradation processes to reallocate energy sources in the system, obtaining long-term goals while adhering to the reliability requirements. The proposed method consists a sequence of blocks: 1) representation, 2) combination, 3) propagation and 4) decision making to reliably quantify uncertainty. 翻訳 |
