Development and Comparison of Virtual Sensors Constructed using AI Techniques to Estimate the Performances of IC Engines

Arravind Jeyamoorthy*, Takuma Degawa, Ratnak Sok, Toshikado Akimichi, Shigeaki Kurita, Masatoshi Ogawa, Takayuki Takei, Ikuta Hayashi, Jin Kusaka, Beini Zhou, Kyohei Yamaguchi, Iku Tanabe

*この研究の対応する著者

研究成果: Conference article査読

抄録

Alternative propulsion systems such as renewable fuels and electric powertrains are expensive; thus, efficient internal combustion engines (ICE) with hybrid powertrains still play significant roles in the transportation fleet in the coming decades. Modern engine technologies have been adopted to meet stringent emissions and fuel economy standards. As a result, engine control systems are becoming more complex. Furthermore, as ICE control parameters increase exponentially, engine calibration and design become bottlenecks in the development process. While a map-based feed-forward control method is a current de facto standard in combustion control, online closed-loop feedback control can improve engine performance and robustness. However, adding physical sensors to measure the various data for the online feedback control and calibration increase the vehicle cost. Therefore, this research proposes a method to replace various physical sensors with virtual ones, developed using Artificial Intelligence (AI) techniques. Other advantages of the data-driven-based virtual sensors over the physical ones are their high robustness and accuracy. This work develops AI-based virtual sensors to estimate engine performance and emission parameters, including NOx and CO2, from in-cylinder pressure data. Multiple AI techniques in the Keras library, such as Artificial Neural Network (ANN), Extreme Gradient Boosting (XG-Boost), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree Regression (DT), are used to construct the virtual sensors. The results confirm that the computation time for estimating the combustion parameters depend on the AI techniques. However, the computational load should be low for the feedback control using in-cylinder pressure data. Therefore, the data-driven techniques were compared to optimize the computational load and prediction accuracy. The results show that the XG-Boost outperforms other AI techniques regarding model accuracy and prediction time.

本文言語English
ジャーナルSAE Technical Papers
DOI
出版ステータスPublished - 2022 8月 30
イベントSAE 2022 Powertrains, Fuels and Lubricants Conference and Exhibition, PFL 2022 - Krakow, Poland
継続期間: 2022 9月 62022 9月 8

ASJC Scopus subject areas

  • 自動車工学
  • 安全性、リスク、信頼性、品質管理
  • 汚染
  • 産業および生産工学

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