Online Learning of a Neural Fuel Control System for Gaseous Fueled SI Engines

This PhD dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid fuels, both at the tailpipe and on a total cycle. Unfortunately, it can be expensive to convert vehicles to gaseous fuels. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase. The controller is based upon a two-part model, using online training of a neural network to model the engine’s steady state characteristics. An experimental test was performed, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and required no calibration and very little information about the engine.