Application of neural network for air-fuel ratio identification in spark ignition engine

Authors:
Samir Saraswati;Satish Chand
Affiliations:
Department of Mechanical Engineering, MNNIT, Allahabad 211004, India.;Department of Mechanical Engineering, MNNIT, Allahabad 211004, India
Venue:
International Journal of Computer Applications in Technology
Year:
2008

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Abstract

In the present work, Recurrent Neural Network (RNN) is used forAir-Fuel Ratio (AFR) identification in Spark Ignition (SI) engine.AFR identification is difficult due to nonlinear and dynamicbehaviour of SI engines. Delays present in the engine dynamicslimits the performance of engine controller. Identifying AFR fewsteps in advance can help engine controller to take care of these.RNN is trained using data from engine simulations inMATLAB/SIMULINK© environment. Uncorrelated signals weregenerated for training and generalisation and it has been shownthat RNN can predict engine simulations with reasonably goodaccuracy. RNN discussed can also work as a virtual AFR sensor andit can very well replace costly AFR sensor used in SI engines.