Transient Air-Fuel Ratio Estimation in Spark Ignition Engine Using Recurrent Neural Networks

Authors:
Yanhong Zhang;Lifeng Xi;James Liu
Affiliations:
School of Computer Science and Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China;School of Computer Science and Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China;BorgWarner Automotive Components (Ningbo) Co.,Ltd, Ningbo, Zhejiang 315104, P.R. China
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

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Abstract

Neural networks is very useful in modeling processes for which mathematical modeling is difficult or impossible. In the present work recurrent neural network (RNN) is used for air-fuel ratio (AFR) estimation in Spark Ignition (SI) Engine. AFR estimation is difficult due to the nonlinearity and dynamic behavior in SI engines. Additionally, delays in engine dynamics limit the performance of engine controller. Estimating AFR a few steps in advance can help engine controller to take care of these. RNN is trained using data from engine simulations in MATLAB/SIMULINK environment. Uncorrelated signals were generated for training and validation. It has been shown that recurrent neural network can predict engine simulations with reasonably good accuracy.