Reactivity controlled compression ignition (RCCI) is an advanced low temperature combustion strategy introduced to achieve near-zero NOx and soot emissions while maintaining diesel-like efficiencies. Precise control of RCCI combustion phasing is necessary in realizing high fuel conversion efficiency as well as meeting stringent emission standards. Model-based control of combustion phasing provides a powerful tool for real-time control during transient operation of the RCCI engine, which 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 combustion phasing of a 1.9-liter RCCI engine. A mean value control-oriented model (COM) of RCCI is developed by combining the auto-ignition model, the burn duration model, and a Wiebe function to predict combustion phasing. Development of a model-based controller requires a dynamic model which can predict engine operation, i.e., estimation of combustion phasing, on a cycle-to-cycle basis. Hence, the mean-value model is extended to encompass the full-cycle engine operation by including the expansion and exhaust strokes. In addition, the dynamics stemming from the thermal coupling between cycles are accounted for, that results in a dynamic RCCI control-oriented model capable of predicting the transient operation of the engine. This model is then simplified and linearized in order to develop a linear observer-based feedback controller to control the combustion phasing using the premixed ratio (the ratio of the port injected gasoline fuel to the total gasoline/diesel fuel injected). The designed controller depicts an accurate tracking performance of the desired combustion phasing and successfully rejects external disturbances in engine operating conditions.

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