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Advanced powertrains for modern vehicles require the optimization of conventional combustion engines in combination with tailored electrification and vehicle connectivity strategies. The resulting systems and their control devices feature many degrees of freedom with a large number of available adjustment parameters. This obviously presents major challenges to the development of the corresponding powertrain control logics. Hence, the identification of an optimal system calibration is a non-trivial task. To address this situation, physics-based control approaches are evolving and successively replacing conventional map-based control strategies in order to handle more complex powertrain topologies. Physics-based control approaches enable a significant reduction in calibration effort, and also improve the control robustness.

Gasoline Controlled Auto Ignition offers a high CO2 emission reduction potential, which is comparable to state-of-the-art, lean stratified operated gasoline engines. Contrary to the latter, GCAI low temperature combustion avoids NOX emissions, thereby trying to avoid extensive exhaust aftertreatment. The challenges remain in a restricted operation range due to combustion instabilities and a high sensitivity towards changing boundary conditions like ambient temperature, intake pressure or fuel properties. Once combustion shows instability, cyclic fluctuations are observed. These appear to have near-chaotic behavior but are characterized by a superposition of clearly deterministic and stochastic effects. Previous works show that the fluctuations can be predicted precisely when taking cycle-tocycle correlations into account. This work extends current approaches by focusing on additional dependencies within one single combustion cycle.