A Phenomenological Model for Predicting the Combustion Phasing and Variability of Spark Assisted Compression Ignition (SACI) Engines

An advanced combustion mode, Spark Assisted Compression Ignition (SACI) has shown the ability to extend loads relative to Homogenous Charge Compression Ignition (HCCI) combustion but at reduced fuel conversion efficiency. SACI combustion is initiated by a spark, with flame propagation followed by a rapid autoignition of the remaining end-gas fuel fraction. Extending upon previous work [1,2], the Wiebe function coefficients used to fit the two combustion phases are regressed here as functions of the air path variables and actuator settings. The parameterized regression model enables mean-value modeling and model-based combustion phasing control. SACI combustion however, exhibits high cyclic variability with random characteristics. Thus, combustion phasing feedback control needs to account for the cyclic variability to correctly filter the phasing data. This paper documents the success in regressing the cyclic variability (defined as the standard deviation in combustion phasing) at various operating conditions, again as a function of air path variables and actuator settings. The combination of the regressed mean and standard deviation models is a breakthrough in predicting the mean-value engine behavior and the random statistics of the cycle-to-cycle variability.