Neural prediction of cylinder air mass for AFR control in SI engine

Abstract

As parsimonious and flexible universal approximator, the one hidden layer perceptron can be used for non linear prediction, An application is described in the framework of Air-Fuel Ratio (AFR) control in spark-ignition engines, a critical point to satisfy pollutant emission legislation, AFR control depends essentially on the prediction of the air mass to be admitted in cylinder. The building of an air mass predictive neural network is described and its performances are evaluated, Compared to classical solutions based on static mappings, the neural predictor allows for reduction of AFR excursions on rapid torque transients