MODELING THE VOLUMETRIC EFFICIENCY OF IC ENGINES - PARAMETRIC, NONPARAMETRIC AND NEURAL TECHNIQUES

The volumetric efficiency (eta(v)) represents a measure of the effecti veness 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 eta(v) require the knowledge o f some quantities usually not available in normal operating conditions , Hence, a purely black-box approach is often used to determine the de pendence of eta(v) upon the main engine variables, like the crankshaft speed and the intake manifold pressure. Various black-box approaches for the estimation of eta(v) are reviewed, from parametric (polynomial -type) models, to non-parametric and neural techniques, like additive models, radial basis function neural networks and multi-layer perceptr ons. The benefits and limitations of these approaches are examined and compared. The problem considered here can be viewed as a realistic be nchmark for different estimation techniques.