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

The volumetric efficiency (eta(v)) 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 eta(v) 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 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 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.