Study of engine operating point optimization incorporating human cognitive processing
人の認知処理を取り入れたエンジン動作点最適化の検討
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20234855
- Paper/Info type
- Symposium Text
No.11-23
- Pages
- 30-40(Total 11 p)
- Date of publication
- Dec 2023
- Publisher
- JSAE
- Language
- English
- Event
- JSAE Symposium 2023
Detailed Information
| Category(J) | PPT資料 Translation |
|---|---|
| Category(E) | PPT slides |
| Author(J) | 1) 長江 新平, 2) 後藤 昌也, 3) 山中 高章, 4) 古宮 亜友美, 5) 榎本 俊夫 |
| Author(E) | 1) Nagae Shimpei, 2) Gotou Masaya, 3) Yamanaka Takaaki, 4) Komiya Ayumi, 5) Enomoto Toshio |
| Affiliation(J) | 1) 日産自動車株式会社, 2) 日産自動車株式会社, 3) 日産自動車株式会社, 4) 日産自動車株式会社, 5) 日産自動車株式会社 |
| Affiliation(E) | 1) Nissan Motor Co., Ltd, 2) Nissan Motor Co., Ltd, 3) Nissan Motor Co., Ltd, 4) Nissan Motor Co., Ltd, 5) Nissan Motor Co., Ltd |
| Abstract(J) | 日産のハイブリッド「e-POWER」はICEやHEVよりエンジン制御の設計自由度が高い反面，複数の性能間でトレードオフが存在する．今回，人の認知処理を取り入れた機械学習により，低速走行時のエンジン騒音と燃費を両立させるエンジン動作点の最適化に取り組んだ．結果主観的なうるささは維持しつつ，燃費1.2％，EV時間1.8％向上した． Translation |
| Abstract(E) | Nissan's e-POWER hybrid powertrain offers flexibility to design in engine control than conventional ICEs and HEVs, but there are trade-offs between multiple performances. we optimize the engine operating point by incorporating human cognitive processing using machine learning to further balance engine noise and fuel economy in situations such as traffic jams, where the vehicle repeatedly starts and stops at low vehicle speeds. As a result, the optimization succeeded in achieving a 1.2% improvement in fuel economy and a 1.8% improvement in EV mode time, while maintaining subjective quietness. |
