Individual cylinder fuel blend estimation in a dual-fuel engine using an in-cylinder pressure based observer. (April 2021)

• Record Type: Journal Article
• Authors: Guardiola, Carlos; Pla, Benjamín; Bares, Pau; Barbier, Alvin
• Is Part Of: Control engineering practice. Volume 109(2021)
• Publication Date: 2021-04
• Subjects: Dual-fuel combustion -- Kalman filter -- In-cylinder pressure -- Heat release calculation -- Cylinder dispersion -- Adaptive look-up table; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104760
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Cylinder-to-cylinder combustion dispersion in internal combustion engines might be caused by various factors (e.g. manufacturing variations of the injectors or nozzle coking) which can result in an increase in pollutant emissions. When dealing with low temperature combustion concepts such as premixed dual-fuel, the system might suffer from an additional source of dispersion due to port fuel injection distribution. Conventional cylinder fuel concentration estimation is based on look-up tables previously calibrated and saved in the ECU. The aging and the fuel distribution characterization of the injectors are a challenging task when relying only on a single UEGO sensor placed at the exhaust. In-cylinder pressure sensors offer a powerful solution to evaluate the energy released by the fuel with one cycle resolution. The present work proposes to combine the information provided by such sensor together with conventional sensors, in this case air mass flow and lambda sensor, for estimating the fuel concentration and blending ratio entering each cylinder in a heavy-duty dual-fuel engine. A Kalman filter was designed to tackle the dynamics of the system, e.g. lambda sensor delay and port fuel distribution, and validated in both conventional diesel and dual-fuel combustion. The output of the filter was then used to update the injectors look-up table in order to cope with aging and possible bias over time. Highlights: An observer is proposed to estimate the cylinder fuel blend in a dual-fuel engine. The cylinders fuel distribution is characterized in direct and port fuel injection. A learning algorithm is used to update the injector look-up tables. Experimental results from pure diesel and dual-fuel combustion are analyzed.