Development of Method to Measure the State of Fuel Supply During Cold Start and Application of the Data to Modeling of Emission Using Machine Learning
冷間始動時における燃料供給状態の計測法の開発と機械学習による排気モデリングへの適用
- Delivery
- Available on the other site
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- Publication code
- 20214257
- Paper/Info type
- JSAE Transaction
Vol.52 No.3
- Pages
- 653-658(Total 6 p)
- Date of publication
- May 2021
- Publisher
- JSAE
- Language
- Japanese
Detailed Information
| Category(J) | 研究論文 Translation |
|---|---|
| Category(E) | ResearchPaper |
| Author(J) | 1) 山田 智久, 2) 稲垣 英人, 3) 高鳥 芳樹, 4) 伯田 祐輔, 5) 羽原 輝晃, 6) 梅本 寿丈 |
| Author(E) | 1) Tomohisa Yamada, 2) Hideto Inagaki, 3) Yoshiki Takatori, 4) Yusuke Hakuta, 5) Teruaki Haibara, 6) Kazuhiro Umemoto |
| Affiliation(J) | 1) 豊田中央研究所, 2) 豊田中央研究所, 3) 豊田中央研究所, 4) 豊田中央研究所, 5) トヨタ自動車, 6) トヨタ自動車 |
| Abstract(J) | ガソリンエンジンの冷間始動時における燃料供給状態を非燃焼の排気ガス分析によって計測する方法を考案し，燃料噴射量やポート壁温に対して妥当な影響感度を持つことを確認した．取得データから作成した機械学習モデルは高いTHC予測精度を持ち，THC排出の少ない始動噴射パターンを導出できることを示した． Translation |
| Abstract(E) | To investigate the behavior of injected fuel during cold start of gasoline engines, a non-combustive method using exhaust gas without combustion was constructed. The THC concentration of exhaust gas is relevant to the quantity of fuel introduced into the cylinder at the current and next cycle. The sensitivities of the concentration to the amount of injected fuel, fuel property and intake port temperature were proved by the experimental data. The proved data were used to model THC behavior applying machine learning. The model was demonstrated high accuracy and applied to derive the injection strategy to achieve low THC emission. |
