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It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.

Quantitative measurements of direct injection fuel spray density and mixing are difficult to achieve using optical diagnostics, due to the substantial scattering of light and high optical density of the droplet field. For multi-hole sprays, the problem is even more challenging, as it is difficult to isolate a single spray plume along a single line of sight. Time resolved x-ray radiography diagnostics developed at Argonne's Advanced Photon Source have been used for some time to study diesel fuel sprays, as x-rays have high penetrating power in sprays and scatter only weakly. Traditionally, radiography measurements have been conducted along any single line of sight, and have been applied to single-hole and group-hole nozzles with few plumes. In this new work, we extend the technique to multi-hole gasoline direct injection sprays.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

Interest in natural gas as an alternative fuel source to petroleum fuels for light-duty vehicle applications has increased due to its domestic availability and stable price compared to gasoline. With its higher hydrogen-to-carbon ratio, natural gas has the potential to reduce engine out carbon dioxide emissions, which has shown to be a strong greenhouse gas contributor. For part-load conditions, the lower flame speeds of natural gas can lead to an increased duration in the inflammation process with traditional port-injection. Direct-injection of natural gas can increase in-cylinder turbulence and has the potential to reduce problems typically associated with port-injection of natural gas, such as lower flame speeds and poor dilution tolerance. A study was designed and executed to investigate the effects of direct-injection of natural gas at part-load conditions.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

This work involves modeling internal and near-nozzle flows of a gasoline direct injection (GDI) nozzle. The Engine Combustion Network (ECN) Spray G condition has been considered for these simulations using the nominal geometry of the Spray G injector. First, best practices for numerical simulation of the two-phase flow evolution inside and the near-nozzle regions of the Spray G injector are presented for the peak needle lift. The mass flow rate prediction for peak needle lift was in reasonable agreement with experimental data available in the ECN database. Liquid plume targeting angle and liquid penetration estimates showed promising agreement with experimental observations. The capability to assess the influence of different thermodynamic conditions on the two-phase flow nature was established by predicting non-flashing and flashing phenomena.

The sparking behavior in an internal combustion engine affects the fuel efficiency, engine-out emissions, and general drivability of a vehicle. As emissions regulations become progressively stringent, combustion strategies, including exhaust gas recirculation (EGR), lean-burn, and turbocharging are receiving increasing attention as models of higher efficiency advanced combustion engines with reduced emissions levels. Because these new strategies affect the working environment of the spark plug, ongoing research strives to understand the influence of external factors on the spark ignition process. Due to the short time and length scales involved and the harsh environment, experimental quantification of the deposited energy from the sparking event is difficult to obtain. In this paper, we present the results of x-ray radiography measurements of spark ignition plasma generated by a conventional spark plug.

The first commercially available Plug-In Hybrid Electric Vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in December 2010. The Volt's powertrain architecture provides four modes of operation, including two that are unique and maximize the Volt's efficiency and performance. The electric transaxle has been specially designed to enable patented operating modes both to improve the electric driving range when operating as a battery electric vehicle and to reduce fuel consumption when extending the range by operating with an internal combustion engine (ICE). However, details on the vehicle control strategy are not widely available because the supervisory control algorithm is proprietary. Since it is not possible to analyze the control without vehicle test data obtained from a well-designed Design-of-Experiment (DoE), a highly instrumented GM Volt, including thermal sensors, was tested at Argonne National Laboratory's Advanced Powertrain Research Facility (APRF).

Previous studies have shown that fuels with higher laminar flame speed also have increased tolerance to EGR dilution. In this work, the effects of fuel laminar flame speed on both lean and EGR dilute spark ignition combustion stability were examined. Fuels blends of pure components (iso-octane, n-heptane, toluene, ethanol, and methanol) were derived at two levels of laminar flame speed. Each fuel blend was tested in a single-cylinder spark-ignition engine under both lean-out and EGR dilution sweeps until the coefficient of variance of indicated mean effective pressure increased above thresholds of 3% and 5%. The relative importance of fuel laminar flame speed to changes to engine design parameters (spark ignition energy, tumble ratio, and port vs. direct injection) was also assessed.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

Cavitation plays an important role in fuel injection systems. It alters the nozzle's internal flow structure and discharge coefficient, and also contributes to injector wear. Quantitatively measuring and mapping the cavitation vapor distribution in a fuel injector is difficult, as cavitation occurs on very short time and length scales. Optical measurements of transparent model nozzles can indicate the morphology of large-scale cavitation, but are generally limited by the substantial amount of scattering that occurs between vapor and liquid phases. These limitations can be overcome with x-ray diagnostics, as x-rays refract, scatter and absorb much more weakly from phase interfaces. Here, we present an overview of some recent developments in quantitative x-ray diagnostics for cavitating flows. Measurements were conducted at the Advanced Photon Source at Argonne National Laboratory, using a submerged plastic test nozzle.

Early in 2007 President Bush announced in his State of the Union Address a plan to off-set 20% of gasoline with alternative fuels in the next ten years. Ethanol, due to its excellent fuel properties for example, high octane number, renewable character, etc., appears to be a favorable alternative fuel from an engine perspective. Replacing gasoline with ethanol without any additional measures results in unacceptable disadvantages mainly in terms of vehicle range. This paper summarizes combustion studies performed with gasoline as well as blends of gasoline and ethanol. These tests were performed on a modern, 4-cylinder spark ignition engine with direct fuel injection and exhaust gas recirculation. To evaluate the influence of blending on the combustion behavior the engine was operated on the base gasoline calibration. Cylinder pressure data taken during the testing allowed for detailed analysis of rates of heat release and combustion stability.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

In order to determine any synergistic effects from reforming multicomponent fuels and to determine the effects of fuel additives and impurities we have investigated the autothermal reforming of fuel blends, including paraffin-aromatic, paraffin-naphthene, paraffin-oxygenate, and paraffin-detergent binary mixtures. The results indicate that aromatic, naphthenic, and detergent components adversely effect the reforming of paraffinic species. The results suggest that competitive adsorption at the catalyst sites decreases conversion rates of the paraffinic species. The paraffinic species are displaced by more strongly adsorbing species, leading to decreased kinetics for paraffin conversion.

Different blends of gasoline range hydrocarbons were investigated to determine the effect of aromatic, naphthenic, and paraffinic content on performance in an autothermal reformer. In addition, we investigated the effects of detergent, antioxidant, and oxygenate additives. These tests indicate that composition effects are minimal at temperatures of 800°C and above, but at lower temperatures or at high gas hourly space velocities (GHSV approaching 100,000 h-1) composition can have a large effect on catalyst performance. Fuels high in aromatic and naphthenic components were more difficult to reform. In addition, additives, such as detergents and oxygenates were shown to decrease reformer performance at lower temperatures.

When compared with new oil, used diesel engine oils exhibited thermal conductivity that increases as the concentration of soot increases. The magnitude of the effect depends on the oil composition, and on the size and dispersion of the soot particles. Although soot in engine oil is generally deleterious to engine performance from the standpoint of wear and deposits, no negative effects were observed on the thermal performance of the oil itself; indeed, even slight positive effects are expected for oils that maintain soot in stable dispersion. Therefore, the thermal challenge for engine oils in diesel engines that use exhaust gas recirculation will be to prevent soot deposition on engine surfaces.

Gasoline is a complex fuel. Many of the constituents of gasoline that are beneficial for the internal combustion engine (ICE) are expected to be challenging for on-board reformers in fuel-cell vehicles. To address these issues, the autothermal reforming of gasoline and individual components of gasoline has been investigated. The results indicate that aromatic components require higher temperatures and longer contact times to reform than paraffinic components. Napthenic components require higher temperatures to reform, but can be reformed at higher space velocities than paraffinic components. The effects of sulfur are dependent on the catalyst. These results suggest that further evolution of gasoline could reduce the demands on the reformer and provide a better fuel for a fuel-cell vehicle.

The physical and morphological properties of the particulate matter emitted from a 1.7-liter light-duty diesel engine were characterized by observing its evolution in size and fractal geometry along the exhaust system. A common-rail direct-injection diesel engine, the exhaust system of which was equipped with a turbocharger, EGR, and two oxidation catalysts, was powered with a California low-sulfur diesel fuel at various engine-operating conditions. A unique thermophoretic sampling system, a high-resolution transmission electron microscope (TEM), and customized image processing/data acquisition systems were key instruments that were used for the collection of particulate matter, subsequent imaging of particle morphology, and detailed analysis of particle dimensions and fractal geometry, respectively. The measurements were carried out at four different positions along the exhaust pipe.

Hybrid electric vehicles (HEVs) may have key fuel economy and emissions advantages over current conventional vehicles, but they have drawbacks such as frequent engine starts that can slow down market penetration of HEVs. First, the hydrocarbon emissions due to the numerous engine starts would make newly developed HEV powertrains even more demanding on the emission control system. Second, frequent starts may make the engine deteriorate quickly. This study is an attempt to gain a better understanding of the engine start characteristics of two limited-production HEVs (Toyota Prius and Honda Insight). Using fast-response (5 ms) hydrocarbon and NO (nitric oxide) analyzers, the transient emissions were measured in the engine exhaust ports during cold and hot engine starts. On the basis of the experimental findings, several recommendations were made to improve performance and emissions of future HEVs.