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To improve fuel economy and reduce regulated emissions spark-ignition engines are equipped with a large number of control actuators, motivating the use of model-based ignition timing prediction strategies. Model-based ignition timing strategies require a target combustion phasing for proper calibration, generally defined by the crank angle location where fifty percent of the air/fuel mixture is burned (CA50). When fuel type is altered the target CA50 must be updated in the ‘knock region’ to avoid engine damage while maintaining the highest possible efficiency. This process is particularly important when switching between gasoline and E85 because they have vastly different octane ratings. A semi-physical virtual octane sensor, based on an Arrhenius function combined with a quasi-dimensional turbulent flame entrainment combustion model, is described that identifies the size of the knock region for a given fuel.

Map-based ignition timing control and calibration routines become cumbersome when the number of control degrees of freedom increases and/or a wide range of fuels are used, motivating the use of model-based methods. Purely physics based control techniques can decrease calibration burdens, but require high complexity to capture non-linear engine behavior with low computational requirements. Artificial Neural Networks (ANN), on the other hand, have been recognized as a powerful tool for modeling systems which exhibit nonlinear relationships, but they lack physical significance. Combining these two techniques to produce semi-physical artificial neural network models that can provide high accuracy and low computational intensity is the focus of this research. Physical input parameters are selected based on their sensitivity to combustion duration prediction accuracy.

This research describes several data processing and analysis techniques that can be used to quantify indicated torque losses associated with in-cylinder thermodynamic events. The detailed thermodynamic techniques are intended to aid the development of performance engines under high-load conditions. This study investigates potential IMEP gains that could be made to an engine based on evaluating cylinder and manifold pressure data collected during wide-open-throttle operation. Examination of the data can guide engine design changes by exposing inefficiencies that may have otherwise gone unnoticed. Examples of calibration adjustments and physical intake and exhaust manifold design changes are also presented to validate the data analysis techniques presented. The research data sets were recorded using a 5.3L V8 engine in conjunction with a highly-controlled transient dynamometer.