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This study focuses on evaluation of various fuels within a conventional gasoline internal combustion engine (ICE) vehicle and the implementation of advanced emissions reduction technology. It shows the robustness of the implemented technology packages for achieving ultra-low tailpipe emissions to different market fuels and demonstrates the potential of future GHG neutral powertrains enabled by drop-in lower carbon fuels (LCF). An ultra-low emission (ULE) sedan vehicle was set up using state-of-the-art engine technology, with advanced vehicle control and exhaust gas aftertreatment system including a prototype rapid catalyst heating (RCH) unit. Currently regulated criteria pollutant emission species were measured at both engine-out and tailpipe locations. Vehicle was run on three different drive cycles at the chassis dynamometer: two standard cycles (WLTC and TfL) at 20°C, and a real driving emission (RDE) cycle at -7°C.

Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The non-linearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 - C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions.

Particulate matter emissions from internal combustion engines have become an increasingly important area of focus for development teams in recent years. This is due to greater regulatory scrutiny on vehicles globally, and especially on particulate emissions. The chemical composition and bulk physical properties of the fuel have been shown to influence the particulate number emissions characteristics. Although some predictive models have been proposed, the causality of specific properties or constituents has not been demonstrated due to the co-linearity of the variables considered in previous studies. In this work, fuels were formulated to capture the expected variation in three key properties of United States (US) market gasoline fuels. Specifically, total aromatics, volatility, and particulate matter index (PMI) were varied across market extremes within regulatory limits--while holding other properties constant.

Studies have shown that fuel quality plays an important role in engine-out emissions. The wide variation in composition and properties of gasoline fuels available in the market can lead to discrepancies between the expected emission levels as per set regulations and actual on-road measurements. This study compares engine-out gaseous and particulate emission results between 5 US market fuels, 5 certification fuels and one street-legal race fuel. The market fuels were acquired from different terminals in Michigan. Tests were performed on a 4-cylinder 2.3 L turbocharged direct injection spark-ignited engine. The tests covered a wide range of steady-state operating conditions including load, injection timing and engine speed sweeps. Transient load steps were also performed under warm and cold engine conditions.

This research evaluates the capability of data-science models to classify the combustion events in Cooperative Fuel Research Engine (CFR) operated under Homogeneous Charge Compression Ignition (HCCI) conditions. A total of 10,395 experimental data from the CFR engine at the University of Michigan (UM), operated under different input conditions for 15 different fuel blends, were utilized for the study. The combustion events happening under HCCI conditions in the CFR engine are classified into four different modes depending on the combustion phasing and cyclic variability (COVimep). The classes are; no ignition/high COVimep, operable combustion, high MPRR, and early CA50. Two machine learning (ML) models, K-nearest neighbors (KNN) and Support Vector Machines (SVM), are compared for their classification capabilities of combustion events. Seven conditions are used as the input features for the ML models viz.