  Cylinder pressure-based control is a key enabler for advanced pre-mixed
combustion concepts. Besides guaranteeing robust and safe operation, it allows
for cylinder pressure and heat release shaping. This requires fast
control-oriented combustion models. Over the years, mean-value models have been
proposed that can predict combustion measures (e.g., Gross Indicated Mean
Effective Pressure, or the crank angle where 50% of the total heat is released)
or models that predict the full in-cylinder pressure. However, these models are
not able to capture cyclic variations. This is important in the control design
for combustion concepts, like Reactivity Controlled Compression Ignition, that
can suffer from large cyclic variations. In this study, the in-cylinder
pressure and cyclic variation are modelled using a data-based approach. The
model combines Principle Component Decomposition and Gaussian Process
Regression. A detailed study is performed on the effects of the different
hyperparameters and kernel choices. The approach is applicable to any
combustion concept, but most valuable for advance combustion concepts with
large cyclic variation. The potential of the proposed approach is demonstrated
for an Reactivity Controlled Compression Ignition engine running on Diesel and
E85. The prediction quality of the evaluated combustion measures has an overall
accuracy of 13.5% and 65.5% in mean behaviour and standard deviation,
respectively. The peak-pressure rise-rate is traditionally hard to predict, in
the proposed model it has an accuracy of 22.7% and 96.4% in mean behaviour and
standard deviation, respectively. This Principle Component Decomposition-based
approach is an important step towards in-cylinder pressure shaping. The use of
Gaussian Process Regression provides important information on cyclic variation
and provides next-cycle controls information on safety and performance
criteria.
