The present paper proposed a cycle to cycle prediction of in-cylinder pressure profile using double Wiebe function in a reactivity controlled compression ignition (RCCI) engine. RCCI engines lack direct control over combustion by means of any explicit hardware such as fuel injection or spark timing. Therefore, cylinder pressure sensor based control or model driven control is necessary for RCCI engines. In this work, an iterative algorithm to generate double Wiebe function parameters, were designed to model cycle average measured cylinder pressure. The model and measured data were in good agreement. However, when, this model was compared with measured cycles, the error and regression exhibited a near normal distribution. The quality of error and regression was found to deteriorate with premix energy share (ES) in RCCI mode due to higher cycle to cycle variations. To address this challenge, a range of double Wiebe parameters were generated to mimic the inner and outer most pressure cycle at three times the standard deviation from the cycle average cylinder pressure (99.7% confidence interval). This parameter range was randomized using Gaussian distribution and model cycles were generated stochastically. Leveraging this modelling approach for realistic sensing of selected combustion control parameters, indicated robustness and reliability. The stochastic approach was found to be an innovative strategy for RCCI engine model driven control.
