Modelling the volumetric efficiency of ic engines: Parametric, non-parametric and neural techniques

Abstract The volumetric efficiency (Th.) represents a measure of the effectiveness of an air pumping system, and is one of the most commonly used parameters in the characterization and control of four-stroke internal combustion engines. Physical models of r),. require the knowledge of some quantities usually not available in normal operating conditions. Hence, a purely black-box approach is often used to determine the dependence of 11,. upon the main engine variables, like the crankshaft speed and the intake manifold pressure. Various black-box approaches for the estimation of ην are reviewed, from parametric (polynomial-type) models, to non-parametric and neural techniques, like additive models, radial basis function neural networks and multi-layer perceptrons. The benefits and limitations of these approaches are examined and compared. The problem considered here can be viewed as a realistic benchmark for different estimation techniques.

[1]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[2]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[3]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[4]  J. David Powell,et al.  Engine Air-Fuel Ratio Control Using an Event-Based Observer , 1993 .

[5]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[6]  C. F. Taylor,et al.  The internal-combustion engine in theory and practice , 1985 .

[7]  Giorgio Rizzoni,et al.  Fault detection and isolation for an experimental internal combustion engine via fuzzy identification , 1995, IEEE Trans. Control. Syst. Technol..

[8]  James V. Beck,et al.  Parameter Estimation in Engineering and Science , 1977 .

[9]  Jorge Martins,et al.  A model for predicting engine torque response during rapid throttle transients in port-injected spark-ignition engines , 1989 .

[10]  Gabriele Serra,et al.  Analysis & Validation of Mean Value Models for SI IC-Engines , 1995 .

[11]  L. Ljung,et al.  Overtraining, Regularization, and Searching for Minimum in Neural Networks , 1992 .

[12]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[13]  Serge Boverie,et al.  Nonlinear control of a spark-ignition engine , 1995, IEEE Trans. Control. Syst. Technol..

[14]  R. Matthews,et al.  Intake and ECM submodel improvements for dynamic SI engine models: Examination of Tip-In/Tip-Out , 1991 .

[15]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[16]  G. Wahba Spline models for observational data , 1990 .

[17]  John B. Heywood,et al.  Internal combustion engine fundamentals , 1988 .