Title :
Neural network control of air-to-fuel ratio in a bi-fuel engine
Author :
Gnanam, Gnanaprakash ; Habibi, Saeid R. ; Burton, Richard T. ; Sulatisky, Michael T.
Author_Institution :
Dept. of Mech. Eng., Univ. of Saskatchewan, Saskatoon, Sask.
Abstract :
In this paper, a neural network-based control system is proposed for fine control of the intake air/fuel ratio in a bi-fuel engine. This control system is an add-on module for an existing vehicle manufacturer´s electronic control units (ECUs). Typically the ECU is calibrated for gasoline and provides a good control of the intake air/fuel ratio with gasoline. The neural network-based control system is developed to allow the conversion of a gasoline ECU to a bi-fuel form with compressed natural gas at minimal cost. The effectiveness of the neural control system is demonstrated by using a simulation of a Dodge four-stroke bi-fuel engine
Keywords :
dual fuel engines; fuel systems; intelligent control; neurocontrollers; Dodge four-stroke bi-fuel engine; air-to-fuel ratio; compressed natural gas; fuel injection control; gasoline electronic control unit; neural etwork control; Control system synthesis; Control systems; Costs; Engines; Fuels; Manufacturing; Natural gas; Neural networks; Petroleum; Vehicles; Artificial neural networks; bi-fuel engines; compressed natural gas (CNG); fuel injection control;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/TSMCC.2005.855524
