Reactivity-controlled compression ignition (RCCI) is a promising combustion strategy to achieve near-zero NOx and soot emissions and diesel-like efficiencies. Model-based control of RCCI combustion phasing requires a computationally efficient combustion model that encompasses factors such as injection timings, fuel blend composition, and reactivity. In this work, physics-based models are developed to predict the onset of auto-ignition in RCCI and to estimate the burn duration based on an approximation of the spontaneous ignition front speed. A mean value control-oriented model of RCCI is then developed by combining the auto-ignition model, the burn duration model, and a Wiebe function to predict combustion phasing. The control-oriented model is parameterized and validated using simulation data from an experimentally validated, detailed computational fluid dynamics combustion model developed using the KIVA-3V code. The validation results show that the control-oriented model can predict the start of combustion, burn duration, and crank angle of 50% burnt fuel with an average error of less than 2 crank angle degrees. Thus, the control-oriented model demonstrates sufficient accuracy in predicting RCCI combustion phasing for control applications. The control-oriented model is an integral part of designing a model-based controller, which in the case of RCCI is of paramount importance due to various attributes concerning combustion, particularly for transient engine operation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.