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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.

Drive cycles are a core piece of vehicle development testing methodology. The control and calibration of the vehicle is often tuned over drive cycles as they are the best representation of the real-world driving the vehicle will see during deployment. To obtain general performance numerous drive cycles must be generated to ensure final control and calibration avoids overfitting to the specifics of a single drive cycle. When real-world driving cycles are difficult to acquire methods can be used to create statistically similar synthetic drive cycles to avoid the overfitting problem. This subject has been well addressed within the passenger vehicle domain but must be expanded upon for utilization with tracked off-road vehicles. Development of hybrid tracked vehicles has increased this need further. This study shows that turning dynamics have significant influence on the vehicle power demand and on the power demand on each individual track.