Multi-step predictive model of air fuel ratio for gasoline engine based on neural network

Air fuel ratio is a key index affecting the emission of gasoline engine. A multi-step predictive model of air fuel ratio based on neural network was provided in this paper to overcome air fuel ratio transmission delay affection on air fuel ratio control accuracy of gasoline engine during transient conditions. Input vectors of neural network multi-step predictive model was determined by the mathematic model of air fuel ratio, and derivation of air fuel ratio reflecting the air fuel ratio tendency was included within input vector of neural network to improve the prediction accuracy. The simulation was accomplished using experiment data of HL495 gasoline engine during transient conditions, and the results show the maximal error of predictive model is less than 3% and the average error is less than 2%, and the model can approximate air fuel ratio process of gasoline engine during transient condition.