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Advances in engine controls and sensor technology are making advanced, direct, high-speed control of engine combustion more feasible. Control of combustion rate and phasing in low-temperature combustion regimes and active control of cyclic variability in dilute SI combustion are being pursued in laboratory environments with high-quality data acquisition systems, using metrics calculated from in-cylinder pressure. In order to implement these advanced combustion controls in production, lower-quality data will need to be tolerated even if indicated pressure sensors become available. This paper examines the effects of several data quality issues, including phase shifting (incorrect TDC location), reduced data resolution, pressure pegging errors, and random noise on calculated combustion metrics that are used for control feedback.

We investigate lean-fueling cyclic dispersion in spark ignition engines in terms of experimental nonlinear mapping functions representing the connection between past and future combustion events. Nonlinear mapping functions provide a relatively easy method for identifying the deterministic dynamics associated with lean combustion instability, even in the presence of very high levels of noise. Observed experimental maps appear to have strong similarities to those predicted by an existing nonlinear spark ignition engine model. Differences between the observed map and model predictions become more pronounced at very lean fueling and high residual fraction. Map function details are shown to be useful in model validation, identifying model deficiencies, and comparing the characteristics of different engines. We expect that such maps will also be useful for developing real-time control strategies.