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

  • Yanhong Zhang
  • Lifeng Xi
  • James Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4693)

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.

Keywords

Air-fuel ratio air-fuel ratio estimation recurrent neural network 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yanhong Zhang
    • 1
  • Lifeng Xi
    • 1
  • James Liu
    • 2
  1. 1.School of Computer Science and Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100P.R. China
  2. 2.BorgWarner Automotive Components (Ningbo) Co.,Ltd, Ningbo, Zhejiang 315104P.R. China

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