This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Input Adaptation for Control Oriented Physics-Based SI Engine Combustion Models Based on Cylinder Pressure Feedback

Journal Article
2015-01-0877
ISSN: 1946-3936, e-ISSN: 1946-3944
Published April 14, 2015 by SAE International in United States
Input Adaptation for Control Oriented Physics-Based SI Engine Combustion Models Based on Cylinder Pressure Feedback
Sector:
Citation: Wang, S., Zhu, Q., Prucka, R., Prucka, M. et al., "Input Adaptation for Control Oriented Physics-Based SI Engine Combustion Models Based on Cylinder Pressure Feedback," SAE Int. J. Engines 8(4):1463-1471, 2015, https://doi.org/10.4271/2015-01-0877.
Language: English

Abstract:

As engines are equipped with an increased number of control actuators to meet fuel economy targets, they become more difficult to control and calibrate. The additional complexity created by a larger number of control actuators motivates the use of physics-based control strategies to reduce calibration time and complexity. Combustion phasing, as one of the most important engine combustion metrics, has a significant influence on engine efficiency, emissions, vibration and durability. To realize physics-based engine combustion phasing control, an accurate prediction model is required. This research introduces physics-based control-oriented laminar flame speed and turbulence intensity models that can be used in a quasi-dimensional turbulent entrainment combustion model. The influence of laminar flame speed and turbulence intensity on predicted mass fraction burned (MFB) profile during combustion is analyzed. Then a rule based methodology for laminar flame speed and turbulence intensity correction is proposed. The combustion model input adaptation algorithm can automatically generate laminar flame speed and turbulence intensity correction multipliers based on cylinder pressure feedback for different engine operating conditions. The correction multipliers can be stored into maps or regression equations that then feed into the main combustion model to improve overall prediction accuracy. Research results from this paper show the combustion model prediction error (RMSE) decreased by 88% (from 0.8 CAD to 0.1CAD) for fitting data and 58% (from 1.2 CAD to 0.5 CAD) for validation data when this method was utilized.