Common rail injection system iterative learning control based parameter calibration for accurate fuel injection quantity control |
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Authors: | F Yan J Wang |
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Institution: | (1) FH Jena (University of Applied Sciences), Jena, Germany;(2) Ruhstorf, Germany |
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Abstract: | 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. |
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