Date of Award

December 2020

Document Type


Degree Name

Doctor of Philosophy (PhD)


Automotive Engineering

Committee Member

Robert Prucka

Committee Member

Zoran Filipi

Committee Member

Benjamin Lawler

Committee Member

Jiangfeng Zhang


Substantial fuel economy improvements for light-duty automotive engines demand novel combustion strategies. Low temperature combustion (LTC) demonstrates potential for significant fuel efficiency improvement; however, control complexity is an impediment for real-world transient operation. Spark-assisted compression ignition (SACI) is an LTC strategy that applies a deflagration flame to generate sufficient energy to trigger autoignition in the remaining charge. For other LTC strategies, control of autoignition timing is difficult as there is no direct actuator for combustion phasing. SACI addresses this challenge by using a spark plug to initiate a flame that then triggers autoignition in a significant portion of the charge. The flame propagation phase limits the rate of cylinder pressure increase, while autoignition rapidly completes combustion. High dilution is generally required to maintain production-feasible reaction rates. This high dilution, however, increases the likelihood of flame quench, and therefore potential misfires. Mitigating these competing constraints requires careful mixture preparation strategies for SACI to be feasible in production. Operating a practical engine within this restrictive regime is a key modeling and control challenge. Current models are not sufficient for control-oriented work such as calibration optimization, transient control strategy development, and real-time control. To resolve the modeling challenge, a fast-running cylinder model is developed and presented in this work. It comprises of five bulk gas states and a fuel stratification model comprising of ten equal-mass zones within the cylinder. The zones are quasi-dimensional, and their state varies with crank angle to capture the effect of fuel spray and mixing. For each zone, combustion submodels predict flame propagation burn duration, autoignition phasing, and the concentration of oxides of nitrogen. During the development of the combustion submodels, both physics-based and data-driven techniques are considered. However, the best balance between accuracy and computational expense leads to the nearly exclusive selection of data-driven techniques. The data-driven models are artificial neural networks (ANNs), trained to an experimentally-validated one-dimensional (1D) engine reference model. The simplified model matches the reference 1D engine model with an R2 value of 70‒96% for key combustion parameters. The model requires 0.8 seconds to perform a single case, a 99.6% reduction from the reference 1D engine model. The reduced model simulation time enables rapid exploration of the control space. Over 250,000 cases are evaluated across the entire range of actuator positions. From these results, a transient-capable calibration is formulated. To evaluate the strength of the steady-state calibration, it is operated over a tip-in and tip-out. The response to the transients required little adjustment, suggesting the steady-state calibration is robust. The model also demonstrates the capability to adapt in-cylinder state and spark timing to offset combustion phasing disturbances. This positive performance suggests the candidate model developed in this work retains sufficient accuracy to be beneficial for control-oriented objectives. There are four contributions of this research: 1) a demonstration of the impact of combustion fundamentals on SACI combustion, 2) an identification of suitable techniques for data-driven modeling, 3) a quasi-dimensional fuel stratification model for radially-stratified engines, and 4) a comprehensive cylinder model that maintains high accuracy despite substantially reduced computational expense.



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