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Online Automatic Adaptation for Model-based Control of Diesel Engine
ISSN: 0148-7191, e-ISSN: 2688-3627
Published December 11, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In this paper, an online automatic adaptation method for model-based control of a diesel engine is developed. Control-oriented models based on physics has been proposed as substitutes for conventional control methods to improve the performance of engine under real driving situation. Even such physical-rich models have fitting parameters and it is preferable to adapt the parameters according to the real-time operating condition. Therefore, an automatic adaptation method for the model is developed, and the method is based on neural network. The prediction accuracy of the model is evaluated by simulation and it is confirmed that the method can be applied online to a real engine by experiment.
CitationCAO, J., TAKAHASHI, M., YAMASAKI, Y., and KANEKO, S., "Online Automatic Adaptation for Model-based Control of Diesel Engine," SAE Technical Paper 2019-01-2320, 2019.
Data Sets - Support Documents
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- Drews, P. et al. , Model-based optimal control for PCCI combustion engines. IFAC Proc. Vol. 43, 288-293 (2010).
- Isermann, R. & Sequenz, H. Model-based development of combustion-engine control and optimal calibration for driving cycles: general procedure and application. IFAC-PapersOnLine 49, 633-640 (2016).
- Ravi, N., Roelle, M. J., Jungkunz, A. F. & Gerdes, J. C. A Physically Based Two-State Model for Controlling Exhaust Recompression HCCI in Gasoline Engines. Dyn. Syst. Control. Parts A B 2006, 483-492 (2006).
- Yamasaki, Y., Ikemura, R., Takahashi, M., Shimizu, F. & Kaneko, S. Simple combustion model for a diesel engine with multiple fuel injections. Int. J. Engine Res. 146808741774276 (2017). doi:10.1177/1468087417742764
- Yamasaki, Y., Ikemura, R. & Takahashi, M. MIMO Control of a Diesel Engine Using a Control-Oriented Model. Symp. Combust. Control 2017 1-8 (2017).
- Grasreiner, S., Neumann, J., Wensing, M. & Hasse, C. Model-based virtual engine calibration with the help of phenomenological methods for spark-ignited engines. Appl. Therm. Eng. 121, 190-199 (2017).
- Eguchi, M., Mengxing, Q., Ohmori, H., Yamasaki, Y. & Kaneko, S. Diesel Engine Combustion Control Using Feedback Error Learning with Artificial Intelligence Feedforward Controller. Trans. Soc. Automot. Eng. Japan 49, (2018).
- Fuyuto, T. et al. Noise and emissions reduction by second injection in diesel PCCI combustion with split injection. SAE Int. J. Engines 7, 1900-1910 (2014).
- Nakayama, D., Okamoto, Y., Ishii, K., Shibata, G. & Ogawa, H. Improvements of Diesel Engine Combustion Noise and Performance. Trans. Soc. Automot. Eng. Japan, Inc. 47, 649-655 (2016).
- Yamasaki, Y., Ikemura, R. & Kaneko, S. Model-based control of diesel engines with multiple fuel injections. Int. J. Engine Res. 19, 257-265 (2018).
- Reitz, R.D. & Bracco, F. B. On the dependence of spray angle and other spray parameters on nozzle design and operating conditions. (SAE technical paper, 1979).
- Livengood, J. C. & Wu, P. C. Correlation of autoignition phenomena in internal combustion engines and rapid compression machines. Symp. Combust. 5, 347-356 (1955).