Application of neural network for air-fuel ratio identification in spark ignition engine
by Samir Saraswati, Satish Chand
International Journal of Computer Applications in Technology (IJCAT), Vol. 32, No. 3, 2008

Abstract: In the present work, Recurrent Neural Network (RNN) is used for Air-Fuel Ratio (AFR) identification in Spark Ignition (SI) engine. AFR identification is difficult due to nonlinear and dynamic behaviour of SI engines. Delays present in the engine dynamics limits the performance of engine controller. Identifying AFR 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 generalisation and it has been shown that RNN can predict engine simulations with reasonably good accuracy. RNN discussed can also work as a virtual AFR sensor and it can very well replace costly AFR sensor used in SI engines.

Online publication date: Sun, 26-Oct-2008

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