Design of Power Management Strategy Using Artificial Neural Networks for Mild Hybrid Electric Vehicles,"Presenter"
- 提供方法
- 本サイト上にてダウンロード・閲覧可
- 形態
- 価格
- 一般価格(税込):¥1,100 会員価格(税込):¥880
- 文献番号
- 20181829
- 文献・情報種別
- AVEC
No.ThE1-6
- 掲載ページ
- 1-5(Total 5 p)
- 発行年月
- 2018年 7月
- 出版社
- その他・不明
- 言語
- 英語
- イベント
- AVEC '18
書誌事項
| カテゴリ(英) | Hybrid EV Control I 翻訳 |
|---|---|
| 著者(英) | 1) Bo-Chiuan Chen |
| 抄録(英) | An adaptive power management strategy (APMS) is developed for the mild hybrid electric vehicle with a belt-driven starter generator (BSG). According to the state of charge (SOC) of the battery, a self-organizing fuzzy controller is used to adaptively adjust the equivalence factor which is used to convert the electric power usage to the equivalent fuel consumption. Equivalent fuel consumption minimization (ECMS) is used to obtain the optimal power split ratio between the engine and the BSG. Due to the high computation load of ECMS, an artificial neural network (ANN) is designed to replace the ECMS for the real-time implementation. Simulation results show that the proposed APMS with ANN can achieve the fuel economy close to that of the APMS with ECMS while reducing the computation load significantly. 翻訳 |