Estimation of In-cylinder Trapped Gas Mass and Composition

• Main Author: Nikkar, Sepideh
• Format: Others
• Language: English
• Published: Linköpings universitet, Fordonssystem 2017
• Subjects: Diesel Engine; Cylinder Pressure; Residual Gas Mass; Gas Mass Composition; Cylinder Wall Temperature; Cylinder Pressure Correction; Engineering and Technology; Teknik och teknologier
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-141580

To meet the constantly restricting emission regulations and develop better strategiesfor engine control systems, thorough knowledge of engine behavior is crucial.One of the characteristics to evaluate engine performance and its capabilityfor power generation is in-cylinder pressure. Indeed, most of the diagnosis andcontrol signals can be obtained by recording the cylinder pressure trace and predictingthe thermodynamic variables [3].This study investigates the correlation between the in-cylinder pressure andtotal trapped gas mass [10] with the main focus on estimating the in-cylinder gasmass as a part of a lab measuring procedure using the in-cylinder pressure sensors,or as a real-time method for implementation in an engine control unit thatare not equipped with the cylinder pressure sensors. The motivation is that precisedetermination of air mass is essential for the fuel control system to convey themost-efficient combustion with lower emissions delivered to the after-treatmentsystem [10].For this purpose, a six-cylinder Diesel engine is used for recording the enginespeed, engine torque, measuring the cylinder pressure profile resolved bythe crank angle, intake and exhaust valve phasing as well as intake and exhaustmanifold pressures and temperatures. Next, the most common ways of estimatingthe in-cylinder trapped gas mass are studied and the most reliable ones areinvestigated in-depth and a model with the acceptable accuracy in different operatingconditions is proposed, explained and implemented. The model in has athermodynamics basis and the relative errors is lower than 3% in all the investigatedtests. Afterwards, the most important findings are highlighted, the sourcesof errors are addressed and a sensitivity analysis is performed to evaluate themodel robustness. Subsequently, method adjustment for other operating conditionsis briefly explained, the potential future work is pointed and a complete setof results is presented in Appendix B.