AFR control in si engine with neural prediction of cylinder air mass

Accurate Air-Fuel Ratio (AFR) control in a sparkignition engine is a critical point to satisfy pollutant emission legislation. Using a three-way catalytic converter with an electronic fuel injection, today's most effective solution, requires the regulation of the cylinder AFR in a narrow band around the stoichiometric conditions during both steady and transient engine operation to be efficient. AFR control depends essentially on prediction of the air mass to be admitted. In this paper, the building of an air mass predictive neural network is described and its performances are evaluated. Using this predictor in addition with transient fuel film compensation for AFR control allows to drastically reduce the AFR excursions during fast transients.

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