Abstract:
In present research Air to Fuel Ratio for 2NZFE fuel injection engine is controlled by using
Artificial Neural Network (ANN). ANN is an information processing paradigm which is
inspired by a biological nervous system. ANN trains controller by passively accepts inputs
and its corresponding desired outputs. Case study is done on 2NZFE fuel injection engine. All
the required data for ANN training is collected by performing several active tests on 2NZFE
fuel injection engine at TOYOTA EFI workshop located in I-9 sector of Islamabad. Total 100
data sets are recorded comprising of 11 parameters (10 inputs+01 output) in each set. Offline
training of ANN is done by taking 10 parameters as input upon which AFR is dependent,
whereas AFR is taken as an output parameter. ANN is modeled by using three layers
including input, output and hidden layer with five neurons in hidden layer. “Gradient Descent
Back-propagation with Momentum and with Adaptive learning rate” is used as a training
algorithm with sigmoid tangent as an activation function between layers. 3000 iteration are
carried out. Trained neural network predicted outputs which are then analyzed and compared
with the actual targets. Based on this information electronic control unit of automobile engine
can adjust engine parameters according to the stoichiometric AFR.
