Search Results

Urea-selective catalytic reduction (SCR) catalysts are the leading aftertreatment technology for diesel engines, but there are major challenges associated with meeting future NOx emission standards, especially under transient drive cycle conditions that include large swings in exhaust temperatures. Here we present a simplified, transient, one-dimensional integral model of NOx reduction by NH₃ on a commercial small-pore Cu-zeolite urea-SCR catalyst for which detailed kinetic parameters have not been published. The model was developed and validated using data acquired from bench reactor experiments on a monolith core, following a transient SCR reactor protocol. The protocol incorporates NH₃ storage, NH₃ oxidation, NO oxidation and three global SCR reactions under isothermal conditions, at three space velocities and at three NH₃/NOx ratios.

The affordability of today's and future advanced technology vehicles (i.e., diesel, hybrid, and fuel cell) developed for improved fuel economy remains a concern with respect to final consumer acceptance. The automotive system cost model (ASCM) developed for the production cost estimates at a level of five major subsystems and 35+ components, has been used here to address the affordability issue of advanced technology vehicles. Scenarios encompassing five alternative powertrain and three body options for a mid-size vehicle under two different timeframes (i.e., 2002 and 2010) were considered to determine the cost-effectiveness of among the competing technology options within the same timeframe and between the two timeframes.

The Magnesium Front End Research and Development (MFERD) project under the sponsorship of Canada, China, and USA aims to develop key technologies and a knowledge base for increased use of magnesium in automobiles. The primary goal of this life cycle assessment (LCA) study is to compare the energy and potential environmental impacts of advanced magnesium based front end parts of a North American-built 2007 GM-Cadillac CTS using the current steel structure as a baseline. An aluminium front end is also considered as an alternate light structure scenario. A “cradle-to-grave” LCA is conducted by including primary material production, semi-fabrication production, autoparts manufacturing and assembly, transportation, use phase, and end-of-life processing of autoparts. This LCA study was done in compliance with international standards ISO 14040:2006 [1] and ISO 14044:2006 [2].

In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

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.

While gasoline surrogate development has progressed in the areas of more complex surrogate mixtures and in kinetic modeling tools and mechanism development, it is generally recognized that further development is still needed. This paper represents a small step in supporting this development by providing comparisons between experimental engine data and surrogate-based kinetic models. In our case, the HCCI engine data comes from a port-injected, single-cylinder research engine with intake-air heating for combustion phasing control. Timing sweeps were run at constant fuel rate for three market gasolines and five surrogate mixtures. Modeling was done using the CHEMKIN software with a gasoline mechanism set containing 1440 species and 6572 reactions. Five pure compounds were selected for the surrogate blends and include iso-octane, n-heptane, toluene, methylcyclohexane, and 1-hexene.

Hat-sections, single and double, made of steel are frequently encountered in automotive body structural components. These components play a significant role in terms of impact energy absorption during vehicle crashes thereby protecting occupants of vehicles from severe injury. However, with the need for higher fuel economy and for compliance to stringent emission norms, auto manufacturers are looking for means to continually reduce vehicle body weight either by employing lighter materials like aluminum and fiber-reinforced plastics, or by using higher strength steel with reduced gages, or by combinations of these approaches. Unlike steel hat-sections which have been extensively reported in published literature, the axial crushing behavior of hat-sections made of fiber-reinforced composites may not have been adequately probed.

One scenario for reducing engine out NOx in a spark ignition engine is to introduce small amounts of supplemental hydrogen to the combustion process. The supplemental hydrogen enables a gasoline engine to run lean where NOx emissions are significantly reduced and engine efficiency is increased relative to stoichiometric operation. This paper reports on a mass and energy balance model that has been developed to evaluate the overall system efficiencies of a thermal reformer-heat exchanger system capable of delivering hydrogen to the air intake of a gasoline engine. The mass and energy balance model is utilized to evaluate the conditions where energy losses associated with fuel reformation may be offset by increases in engine efficiencies.

An experimental fuel surrogate validation approach is proposed for a compression ignition application, and applied to validate a Jet-A POSF 4658 fuel surrogate. The approach examines the agreement of both physical and chemical properties of surrogate and target fuels during validation within a real compression-ignition engine environment during four sequential but distinct combustion phases. In-cylinder Mie Scattering measurements are applied to evaporating sprays to compare the behavior of the surrogate, its target fuel, and for reference, n-heptane. Early mixture formation and low temperature reaction behavior were investigated using 2-D broadband chemiluminescence imaging, while high temperature ignition and combustion chemistry were studied using OH chemiluminescence imaging. The optical measurements were combined with cylinder pressure-based combustion analysis, including ignition delay and premixed burn duration, to validate the global behavior of the surrogate.

Internal combustion engines are operated under conditions of high exhaust gas recirculation (EGR) to reduce NOx emissions and promote enhanced combustion modes such as HCCI. However, high EGR under certain conditions also promotes nonlinear feedback between cycles, leading to the development of combustion instabilities and cyclic variability. We employ a two-zone phenomenological combustion model to simulate the onset of combustion instabilities under highly dilute conditions and to illustrate the impact of these instabilities on emissions and fuel efficiency. The two-zone in-cylinder combustion model is coupled to a WAVE engine-simulation code through a Simulink interface, allowing rapid simulation of several hundred successive engine cycles with many external engine parametric effects included.

Striving for sustainable transportation solutions, hydrogen is often identified as a promising energy carrier and internal combustion engines are seen as a cost effective consumer of hydrogen to facilitate the development of a large-scale hydrogen infrastructure. Driven by efficiency and emissions targets defined by the U.S. Department of Energy, a research team at Argonne National Laboratory has worked on optimizing a spark-ignited direct injection engine for hydrogen. Using direct injection improves volumetric efficiency and provides the opportunity to properly stratify the fuel-air mixture in-cylinder. Collaborative 3D-CFD and experimental efforts have focused on optimizing the mixture stratification and have demonstrated the potential for high engine efficiency with low NOx emissions. Performance of the hydrogen engine is evaluated in this paper over a speed range from 1000 to 3000 RPM and a load range from 1.7 to 14.3 bar BMEP.

This project team conducted a life-cycle-based environmental evaluation of new, lightweight materials (e.g., titanium, magnesium) used in two concept 3XVs -- i.e., automobiles that are three times more fuel efficient than today's automobiles -- that are being designed and developed in support of the Partnership for a New Generation of Vehicles (PNGV) program. The two concept vehicles studied were the DaimlerChrysler ESX2 and the Ford P2000. Data for this research were drawn from a wide range of sources, including: the two automobile manufacturers; automobile industry reports; government and proprietary databases; past life-cycle assessments; interviews with industry experts; and models.

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).

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

The Modular Automotive Technology Testbed (MATT) is a flexible platform built to test different technology components in a vehicle environment. This testbed is composed of physical component modules, such as the engine and the transmission, and emulated components, such as the energy storage system and the traction motor. The instrumentation on the tool enables the energy balance for individual components on drive cycles. Using MATT, a single set of hardware can operate as a conventional vehicle, a hybrid vehicle and a plug-in hybrid vehicle, enabling direct comparison of petroleum displacement for the different modes. The engine provides measured fuel economy and emissions. The losses of components which vary with temperature are also measured.

A new friction testing system has been designed and built to simulate the actual engine conditions in friction and wear test of piston-ring and cylinder liner assembly. Experimental data has been developed as Friction Coefficient / Crank Angle Degree diagrams including the effects of running speed (500 and 700 rpm) and ring normal load. Surface roughness profilocorder traces were obtained for tested samples. Mixed lubrication regime observed in the most part of the test range. New cylinder bore materials and lubricants can be screened easily and more reliable simulated engine friction data can be collected using this technique.

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

Road vehicles can expend a significant amount of energy in undesirable vertical motions that are induced by road bumps, and much of that is dissipated in conventional shock absorbers as they dampen the vertical motions. Presented in this paper are some of the results of a study aimed at determining the effectiveness of efficiently transforming that energy into electrical power by using optimally designed regenerative electromagnetic shock absorbers. In turn, the electrical power can be used to recharge batteries or other efficient energy storage devices (e.g., flywheels) rather than be dissipated. The results of the study are encouraging - they suggest that a significant amount of the vertical motion energy can be recovered and stored.

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.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.