Adaptive Prediction of Transient Air Fuel Ratio Based on Forgetting Factor Algorithm for a Coal-Bed Gas Engine
In order to solve the problems of pumping fluctuations and bandwidth limitation to dynamic air fuel ratio (AFR) control for a coal-bed gas engine, adaptive models for air mass flow rate and fuel gas mass flow rate in intake system and exhaust AFR were constructed by a recursive identification method based on the forgetting factor (FF) algorithm. A linear time-varying equation error model was selected as the structure of the adaptive models. Firstly, the throttle position and crankshaft speed signals were used to predict the air and fuel gas flow rates. Secondly, the AFR was predicted in real time according to the estimated air and fuel gas flow rates. The trade-off between tracking ability and noise sensitivity was realized by adjusting a FF. The experiment validations at transient operating conditions of the engine accelerating and decelerating show that, adaptive recursive models of the air and gas flow rates with a larger FF can not only track the averaging values of the flow rates, but also deal with the phase delays introduced by the filter, the AFR adaptive recursive model with a smaller FF can predict transient AFR accurately.
Q. Teng et al., "Adaptive Prediction of Transient Air Fuel Ratio Based on Forgetting Factor Algorithm for a Coal-Bed Gas Engine", Applied Mechanics and Materials, Vols. 130-134, pp. 814-819, 2012