Common rail injection system iterative learning control based parameter calibration for accurate fuel injection quantity control

This paper presents an accurate engine fuel injection quantity control technique for high pressure common rail (HPCR) injection systems by an iterative learning control (ILC)-based, on-line calibration method. Accurate fuel injection quantity control is of importance in improving engine combustion efficiency and reducing engine-out emissions. Current Diesel engine fuel injection quantity control algorithms are either based on pre-calibrated tables or injector models, which may not adequately handle the effects of disturbances from fuel pressure oscillation in HPCR, rail pressure sensor reading inaccuracy, and the injector aging on injection quantity control. In this paper, by using an exhaust oxygen fraction dynamic model, an on-line parameter calibration method for accurate fuel injection quantity control was developed based on an enhanced iterative learning control (EILC) technique in conjunction with HPCR injection system. A high-fidelity, GT-Power engine model, with parametric uncertainties and measurement disturbances, was utilized to validate such a methodology. Through simulations at different engine operating conditions, the effectiveness of the proposed method in rejecting the effects of uncertainties and disturbance on fuel injection quantity control was demonstrated.