Evaluation of residual gas fraction estimation methods for cycle-to-cycle combustion variability analysis and modeling

• Published in: International journal of engine research Vol. 23; no. 2; pp. 198 - 213
• Main Authors: Maldonado, Bryan P, Kaul, Brian C
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.02.2022
• Subjects: estimation; Residual gas; control-oriented modeling; misfire limit; cycle-to-cycle variability
• ISSN: 1468-0874; 2041-3149
• DOI: 10.1177/1468087420983087

Cycle-to-cycle combustion variability in spark-ignition engines during normal operation is mainly caused by random perturbations of the in-cylinder conditions such as the flow velocity field, homogeneity of the air-fuel distribution, spark energy discharge, and turbulence intensity of the flame front. Such perturbations translate into the variability of the energy released observed at the end of the combustion process. During normal operating conditions, the cycle-to-cycle variability (CCV) of the energy release behaves as random uncorrelated noise. However, during diluted combustion, in either the form of exhaust gas recirculation (EGR) or excess air (lean operation), the CCV tends to increase as dilution increases. Moreover, when the ignition limit is reached at high dilution levels, the combustion CCV is exacerbated by sporadic occurrences of incomplete combustion events, and the uncorrelation assumption no longer holds. The low or null energy released by partial burns and misfires has an impact on the following combustion event due to the residual gas that carries burned and unburned gases, which contributes to the deterministic coupling between engine cycles. Many residual gas fraction estimation methods, however, only address the nominal case where complete combustion occurs and combustion events are uncorrelated. This study evaluates the efficacy of such methods on capturing the effects of partial burns and misfires on the residual gas estimate for high-EGR operation. The advantages and disadvantages of each method are discussed based on their ability to generate cycle-to-cycle estimates. Finally, a comparison between the different estimation techniques is presented based on their usefulness for control-oriented modeling.