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Experiments were performed on a small displacement (< 2 L), high compression ratio, 4 cylinder, port injected gasoline engine equipped with Dedicated EGR® (D-EGR®) technology using fuels with varying anti-knock properties. Gasolines with anti-knock indices of 84, 89 and 93 anti-knock index (AKI) were tested. The engine was operated at a constant nominal EGR rate of ∼25% while varying the reformation ratio in the dedicated cylinder from a ϕD-EGR = 1.0 - 1.4. Testing was conducted at selected engine speeds and constant torque while operating at knock limited spark advance on the three fuels. The change in combustion phasing as a function of the level of overfuelling in the dedicated cylinder was documented for all three fuels to determine the tradeoff between the reformation ratio required to achieve a certain knock resistance and the fuel octane rating.

Spark-ignition engine fuel standards have been put in place to ensure acceptable hot and cold weather driveability (HWD and CWD). Vehicle manufacturers and fuel suppliers have developed systems that meet our driveability requirements so effectively that drivers overwhelmingly find that their vehicles reliably start up and operate smoothly and consistently throughout the year. For HWD, fuels that are too volatile perform more poorly than those that are less volatile. Vapor lock is the apparent cause of poor HWD, but there is conflicting evidence in the literature as to where in the fuel system it occurs. Most studies have found a correlation between degraded driveability and higher dry vapor pressure equivalent or lower TV/L = 20, and less consistently with a minimum T50. For CWD, fuels with inadequate volatility can cause difficulty in starting and rough operation during engine warmup.

An investigation was performed to identify the benefits of cooled exhaust gas recirculation (EGR) when applied to a potential ethanol flexible fuelled vehicle (eFFV) engine. The fuels investigated in this study represented the range a flex-fuel engine may be exposed to in the United States; from 85% ethanol/gasoline blend (E85) to regular gasoline. The test engine was a 2.0-L in-line 4 cylinder that was turbocharged and port fuel injected (PFI). Ethanol blended fuels, including E85, have a higher octane rating and produce lower exhaust temperatures compared to gasoline. EGR has also been shown to decrease engine knock tendency and decrease exhaust temperatures. A natural progression was to take advantage of the superior combustion characteristics of E85 (i.e. increase compression ratio), and then employ EGR to maintain performance with gasoline. When EGR alone could not provide the necessary knock margin, hydrogen (H2) was added to simulate an onboard fuel reformer.

While it is well known that “MPG will vary” based on how one drives, little independent research exists on the aggregate fuel savings potential of improving driver efficiency and on the best ways to motivate driver behavior changes. This paper finds that reasonable driving style changes could deliver significant national petroleum savings, but that current feedback approaches may be insufficient to convince many people to adopt efficient driving habits. To quantify the outer bound fuel savings for drive cycle modification, the project examines completely eliminating stop-and-go driving plus unnecessary idling, and adjusting acceleration rates and cruising speeds to ideal levels. Even without changing the vehicle powertrain, such extreme adjustments result in dramatic fuel savings of over 30%, but would in reality only be achievable through automated control of vehicles and traffic flow.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

The Energy Independence and Security Act of 2007 requires the U.S. to use 36 billion gallons of renewable fuel per year by 2022. Domestic ethanol production has increased steadily in recent years, growing from less than 5 billion gallons per year (bgpy) in 2006 to over 13 bgpy in 2010. While there is interest in developing non-oxygenated renewable fuels for use in conventional vehicles as well as interest in expanding flex-fuel vehicle (FFV) production for increased E85 use, there remains concern that EISA compliance will require further use of oxygenated biofuels in conventional vehicles. The Environmental Protection Agency (EPA) recently granted partial approval to a waiver allowing the use of E15 in 2001 and newer light-duty vehicles.

In some studies, a relationship has been observed between increasing ethanol content in gasoline and increased particulate matter (PM) emissions from vehicles equipped with spark ignition engines. The fundamental cause of the PM increase seen for moderate ethanol concentrations is not well understood. Ethanol features a greater heat of vaporization (HOV) than gasoline and also influences vaporization by altering the liquid and vapor composition throughout the distillation process. A droplet vaporization model was developed to explore ethanol’s effect on the evaporation of aromatic compounds known to be PM precursors. The evolving droplet composition is modeled as a distillation process, with non-ideal interactions between oxygenates and hydrocarbons accounted for using UNIFAC group contribution theory. Predicted composition and distillation curves were validated by experiments.

We describe a study to identify potential biofuels that enable advanced spark ignition (SI) engine efficiency strategies to be pursued more aggressively. A list of potential biomass-derived blendstocks was developed. An online database of properties and characteristics of these bioblendstocks was created and populated. Fuel properties were determined by measurement, model prediction, or literature review. Screening criteria were developed to determine if a bioblendstock met the requirements for advanced SI engines. Criteria included melting point (or cloud point) < -10°C and boiling point (or T90) <165°C. Compounds insoluble or poorly soluble in hydrocarbon were eliminated from consideration, as were those known to cause corrosion (carboxylic acids or high acid number mixtures) and those with hazard classification as known or suspected carcinogens or reproductive toxins.

In the last three years, three quality surveys on ethanol-blended fuels intended for use in flex-fuel vehicles have been published. Two of these surveys cover Flex-Fuel quality, and the third encompasses the quality of mid-level ethanol blends (MLEBs) from blender pumps. The purpose of these surveys was to report on the quality of the fuels and provide a snapshot in time of fuel quality. This study examines the larger picture portrayed by these surveys and looks for broader trends in fuel quality. The analysis found that compliance with vapor pressure specification limits for Flex Fuel improved from 40% to 66% in Class 1, from 31% to 43% in Class 2, and from 12% to 30% in Class 3 between 2008 and 2010. Failures on other critical properties, such as acidity, pHe, water, and inorganic chloride were less than 6% in these studies. The 2010 Flex Fuel samples readily met the ethanol content specification, with 88%, 92%, and 95% compliance for Classes 1, 2, and 3, respectively.

The objective of this work was to measure knock resistance metrics for ethanol-hydrocarbon blends with a primary focus on development of methods to measure the heat of vaporization (HOV). Blends of ethanol at 10 to 50 volume percent were prepared with three gasoline blendstocks and a natural gasoline. Performance properties and composition of the blendstocks and blends were measured, including research octane number (RON), motor octane number (MON), net heating value, density, distillation curve, and vapor pressure. RON increases upon blending ethanol but with diminishing returns above about 30 vol%. Above 30% to 40% ethanol the curves flatten and converge at a RON of about 103 to 105, even for the much lower RON NG blendstock. Octane sensitivity (S = RON - MON) also increases upon ethanol blending. Gasoline blendstocks with nearly identical S can show significantly different sensitivities when blended with ethanol.

There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing.

One-dimensional single-zone and two-zone analyses have been exercised to calculate the mass fraction burned in an engine operating on ethanol/gasoline-blended fuels using the cylinder pressure and volume data. The analyses include heat transfer and crevice volume effects on the calculated mass fraction burned. A comparison between the two methods is performed starting from the derivation of conservation of energy and the method to solve the mass fraction burned rates through the results including detailed explanation of the observed differences and trends. The apparent heat release method is used as a point of reference in the comparison process. Both models are solved using the LU matrix factorization and first-order Euler integration.

In this work, the influences of ethanol and iso-butanol blended with gasoline on engine-out and post three-way catalyst (TWC) particle size distribution and number concentration were studied using a General Motors (GM) 2.0L turbocharged spark ignition direct injection (SIDI) engine. The engine was operated using the production engine control unit (ECU) with a dynamometer controlling the engine speed and the accelerator pedal position controlling the engine load. A TSI Fast Mobility Particle Sizer (FMPS) spectrometer was used to measure the particle size distribution in the range from 5.6 to 560 nm with a sampling rate of 1 Hz. U.S. federal certification gasoline (E0), two ethanol-blended fuels (E10 and E20), and 11.7% iso-butanol blended fuel (BU12) were tested. Measurements were conducted at 10 selected steady-state engine operation conditions. Bi-modal particle size distributions were observed for all operating conditions with peak values at particle sizes of 10 nm and 70 nm.

The Texas Department of Transportation (TxDOT) began using an emulsified diesel fuel in July 2002. They initiated a simultaneous study of the effectiveness of this fuel in comparison to 2D on-road diesel fuel, which they use in both their on-road and off-road equipment. The study also incorporated analyses for the fleet operated by the Associated General Contractors (AGC) in the Houston area. Some members of AGC use 2D off-road diesel fuel in their equipment. The study included comparisons of fuel economy and emissions for the emulsified fuel relative to the conventional diesel fuels. Cycles that are known to be representative of the typical operations for TxDOT and AGC equipment were required for use in this study. Four test cycles were developed from data logged on equipment during normal service: 1) the TxDOT Telescoping Boom Excavator Cycle, 2) the AGC Wheeled Loader Cycle, 3) the TxDOT Single-Axle Dump Truck Cycle, and 4) the TxDOT Tandem-Axle Dump Truck Cycle.

This work details two approaches for evaluating transmission warming technology: experimental dynamometer testing and development of a simplified transmission efficiency model to quantify effects under varied real world ambient and driving conditions. Two vehicles were used for this investigation: a 2013 Ford Taurus and a highly instrumented 2011 Ford Fusion (Taurus and Fusion). The Taurus included a production transmission warming system and was tested over hot and cold ambient temperatures with the transmission warming system enabled and disabled. A robot driver was used to minimize driver variability and increase repeatability. Additionally the instrumented Fusion was tested cold and with the transmission pre-heated prior to completing the test cycles. These data were used to develop a simplified thermally responsive transmission model to estimate effects of transmission warming in real world conditions.

A turbocharged 2.0 L PFI engine was modified to operate in a low-pressure loop and Dedicated EGR (D-EGR®) engine configuration. Both engine architectures were operated with a low and high octane gasoline as well as three ethanol blends. The core of this study focused on examining combustion differences at part and high loads between the selected fuels and also the different engine configurations. Specifically, the impact of the fuels on combustion stability, burn rates, knock mitigation, required ignition energy, and efficiency were evaluated. The results showed that the knock resistance generally followed the octane rating of the fuel. At part loads, the burn rates, combustion stability, and EGR tolerance was marginally improved with the high ethanol blends. When combustion was not knock or stability limited, the efficiency differences between the fuels were negligible. The D-EGR engine was much less sensitive to fuel changes in terms of burn rates than the LPL EGR setup.

Southwest Research Institute developed an Autonomous Ground Vehicle (AGV) capable of navigating in urban environments. The paper first gives an overview of hardware and software onboard the vehicle. The systems onboard are classified into perception, intelligence, and command and control modules to mimic a human driver. Perception deals with sensing from the world and translating it into situation awareness. This awareness is then fed into intelligence modules. Intelligence modules take inputs from the user to understand the need to navigate from its current location to another destination and, then, generate a path between them on urban, drivable surfaces using its internal urban database. Situational awareness helps intelligence to update the path in real time by avoiding any static/moving obstacles while following traffic rules.

A simulation framework with a validated system model capable of estimating fuel consumption is a valuable tool in analysis and design of the hybrid vehicles. In particular, the framework can be used for (1) benchmarking the fuel economy achievable from alternate hybrid powertrain technologies, (2) investigating sensitivity of fuel savings with respect to design parameters (for example, component sizing), and (3) evaluating the performance of various supervisory control algorithms for energy management. This paper describes such a simulation framework that can be used to predict fuel economy of series hydraulic hybrid vehicle for any specified driver demand schedule (drive cycle), developed in MATLAB/Simulink. The key components of the series hydraulic hybrid vehicle are modeled using a combination of first principles and empirical data. A simplified driver model is included to follow the specified drive cycle.

Knock-limited loads for a set of surrogate gasolines all having nominal 100 research octane number (RON), approximately 11 octane sensitivity (S), and a heat of vaporization (HOV) range of 390 to 595 kJ/kg at 25°C were investigated. A single-cylinder spark-ignition engine derived from a General Motors Ecotec direct injection (DI) engine was used to perform load sweeps at a fixed intake air temperature (IAT) of 50 °C, as well as knock-limited load measurements across a range of IATs up to 90 °C. Both DI and pre-vaporized fuel (supplied by a fuel injector mounted far upstream of the intake valves and heated intake runner walls) experiments were performed to separate the chemical and thermal effects of the fuels’ knock resistance. The DI load sweeps at 50°C intake air temperature showed no effect of HOV on the knock-limited performance. The data suggest that HOV acts as a thermal contributor to S under the conditions studied.

The characteristics of combustion knock metrics over a number of engine cycles can be an essential reference for knock detection and control in internal combustion engines. In a Spark-Ignition (SI) engine, the stochastic nature of combustion knock has been shown to follow a log-normal distribution. However, this has been derived from experiments done with gasoline only and applicability of log-normal distribution to dual-fuel combustion knock has not been explored. To evaluate the effectiveness and accuracy of log-normal distributed knock model for methane-gasoline blended fuel, a sweep of methane-gasoline blend ratio was conducted at two different engine speeds. Experimental investigation was conducted on a single cylinder prototype SI engine equipped with two fuel systems: a direct injection (DI) system for gasoline and a port fuel injection (PFI) system for methane.