Study on Transient Air-Fuel Ratio Predictive Model of Gasoline Engine Based on Artificial Intelligence

Abstract

For multidimensional nonlinear characteristics of gasoline engine air-fuel ratio, predictive model of the gasoline engine transient air-fuel ratio chaotic time series support vector machine is proposed. And use chaos RBF neural network to Identify fuel film dynamic parameters of transient air-fuel ratio, then obtained the calculated value of Air-fuel ratio according to the theoretical formula of air-fuel ratio mean value model. Finally, take it makes analysis and comparison with prediction model of chaotic time series support vector machine and Elman neural network, and it adequately verifies that the predicted model has a higher prediction accuracy, experimental simulation results show that the predicted model of the chaotic time series using support vector machine has a stronger nonlinear prediction capabilities. And it can improve the transient condition identification precision air-fuel ratio effectively. Therefore, this study would provide a strong basis to precise control of air-fuel ratio transient conditions.