Abstract
The purpose of this study is to develop model-based methodologies, which employ thermo-fluid dynamic engine simulation and multiple-objective optimization schemes, for engine control and calibration, and to validate the reliability of the method using a dynamometer test. In our technique, creating a total engine system model begins by first entirely capturing the characteristics of components affecting the engine system’s behaviour, then using experimental data to strictly adjust the tuning parameters in physical models. Engine outputs over the engine operation conditions determined by design of experiment (DOE) are simulated, followed by fitting the provided dataset using a nonlinear response surface model (RSM) to express the causal relationship among engine operational parameters, environmental factors and engine output. The RSM is applied to an L-jetronic® air-intake system control logic for a turbocharged engine. Coupling the engine simulator with a multi-objective genetic algorithm, the optimal valve timings are investigated from the viewpoints of fuel consumption rate, emissions and torque. The calibrations are made over all the operation points; the control map is implemented in the turbocharged air-intake system control logic. The validation of the control logic was demonstrated using a model-in-the-loop simulation (MILS). The logic output of the charging efficiency transition due to the varying throttle valve opening angle and variable valve timing was compared with the simulator output. According to the results of the MILS, in-cylinder air mass estimations are in good agreement with the engine simulator under several transient operations.
F2012-A06-026
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References
Guerrier M et al (2004) The development of model based methodologies for gasoline IC engine calibration. SAE paper 2004-01-1466
Holliday T et al (1998) Engine—mapping experiments: a two stage regression approach. Techno metrics 40:120–126
Rose DW et al (2002) An engine mapping case study—a two stage regression approach. ImechE C606/025/2002
Morton TM et al (2002) Radial basis functions for engine modeling. ImechE C606/022/2002
Heywood JB (1988) Internal combustion engine fundamentals. McGraw-Hill Inc, Â
Merker GP et al (2005) Simulating combustion. Springer
Suzuki K et al (2011) Model-based technique for air-intake-system control using thermo-fluid dynamic simulation of SI engines and multiple-objective optimization. SAE paper 2011-28-0119
Montgomery DC et al (2006) Introduction to linear regression analysis. Wiley
Montgomery DC (2008) Design and analysis of experiments. Wiley
DuMouchel W et al (1989) Integrating a robust option into a multiple regression computing environment. Proceedings of the 21st symposium on the interface, American Statistical Association, pp 297–301
Turin R et al (2009) Low-cost air estimation. SAE paper 2009-01-0590
Tanabe H et al (2007) Fuel behavior model-based injection control for motorcycle port-injection gasoline engines. SAE paper 2007-32-0045
Iwadare M et al (2009) Multi-variable air path management for a clean diesel engine using model predictive control. SAE paper 2009-01-0733
Okazaki S et al (2009) Development of a new model based air-fuel ratio control system. SAE paper 2009-01-0585
Sekozawa T et al (1992) Development of a highly accurate air-fuel ratio control method based on internal state estimation. SAE paper 920290
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Suzuki, K., Asano, S. (2013). Model-Based Control and Calibration for Air-Intake Systems in Turbocharged Spark-Ignition Engines. In: Proceedings of the FISITA 2012 World Automotive Congress. Lecture Notes in Electrical Engineering, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33750-5_16
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DOI: https://doi.org/10.1007/978-3-642-33750-5_16
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