Search Results

A spark-assist homogeneous charge compression ignition (SA-HCCI) operating strategy is presented here that allows for stoichiometric combustion from 1000-3000 rpm, and at loads as high as 750 kPa net IMEP. A single cylinder gasoline engine equipped with direct fuel injection and fully variable hydraulic valve actuation (HVA) is used for this experimental study. The HVA system enables negative valve overlap (NVO) valve timing for hot internal EGR. Spark-assist stabilizes combustion over a wide range of engine speeds and loads, and allows for stoichiometric operation at all conditions. Characteristics of both spark-ignited combustion and HCCI are present during the SA-HCCI operating mode, with combustion analysis showing a distinctive spark ignited phase of combustion, followed by a much more rapid HCCI combustion phase. At high load, the maximum cylinder pressure rise rate is controlled by a combination of spark timing and retarding the intake valve closing angle.

A transient, one-dimensional lean NOx trap (LNT) model is described and implemented for vehicle systems simulations. The model accounts for conservation of chemical species and thermal energy, and includes the effects of O₂ storage and NOx storage (in the form of nitrites and nitrates). Nitrites and nitrates are formed by diffusion of NO and NO₂, respectively, into sorbent particles, and reaction rates are controlled by chemical kinetics and solid-phase diffusion. The model also accounts for thermal aging and sulfation by means of empirical correlations, which have been derived from laboratory experiments. Example simulation results using the Powertrain Systems Analysis Toolkit (PSAT) are presented.

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.

Lignin by-products from biorefineries has the potential to provide a low-cost alternative to petroleum-based precursors to manufacture carbon fiber, which can be combined with a binding matrix to produce a structural material with much greater specific strength and specific stiffness than conventional materials such as steel and aluminum. The market for carbon fiber is universally projected to grow exponentially to fill the needs of clean energy technologies such as wind turbines and to improve the fuel economies in vehicles through lightweighting. In addition to cellulosic biofuel production, lignin-based carbon fiber production coupled with biorefineries may provide $2,400 to $3,600 added value dry Mg−1 of biomass for vehicle applications. Compared to producing ethanol alone, the addition of lignin-derived carbon fiber could increase biorefinery gross revenue by 30% to 300%.

A commercial three-way catalyst (TWC) was evaluated for ammonia (NH3) generation on a 2.0-liter BMW lean burn gasoline direct injection engine as a component in a passive ammonia selective catalytic reduction (SCR) system. The passive NH3 SCR system is a potential low cost approach for controlling nitrogen oxides (NOX) emissions from lean burn gasoline engines. In this system, NH3 is generated over a close-coupled TWC during periodic slightly rich engine operation and subsequently stored on an underfloor SCR catalyst. Upon switching to lean, NOX passes through the TWC and is reduced by the stored NH3 on the SCR catalyst. NH3 generation was evaluated at different air-fuel equivalence ratios at multiple engine speed and load conditions. Near complete conversion of NOX to NH3 was achieved at λ=0.96 for nearly all conditions studied. At the λ=0.96 condition, HC emissions were relatively minimal, but CO emissions were significant.

Two hybrid powertrain configurations, including parallel and series hybrids, were simulated for fuel economy, component energy loss, and emissions control in Class 8 trucks over both city and highway driving conditions. A comprehensive set of component models describing engine fuel consumption, emissions control, battery energy, and accessory power demand interactions was developed and integrated with the simulated hybrid trucks to identify heavy-duty (HD) hybrid technology barriers. The results show that series hybrid is absolutely negative for fuel-economy improvement of long-haul trucks due to an efficiency penalty associated with the dual-step conversions of energy (i.e. mechanical to electric to mechanical).

Accurately predicting the fuel savings that can be achieved with the implementation of various technologies developed for fuel efficiency can be very challenging, particularly when considering combinations of technologies. Differences in the usage of highway vehicles can strongly influence the benefits realized with any given technology, which makes generalizations about fuel savings inappropriate for different vehicle applications. A model has been developed to estimate the potential for reducing fuel consumption when advanced efficiency technologies, or combinations of these technologies, are employed on highway vehicles, particularly medium- and heavy-duty trucks. The approach is based on a tractive energy analysis applied to drive cycles representative of the vehicle usage, and the analysis specifically accounts for individual energy loss factors that characterize the technologies of interest.

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.

This study investigated the effects of soybean- and coconut-derived biodiesel fuels on combustion characteristics in a 1.7-liter direct injection, common rail diesel engine. Five sets of fuels were studied: 2007 ultra low sulfur diesel (ULSD), 5% and 20% volumetric blends of soybean biodiesel with ULSD (soybean B5 and B20), and 5% and 20% volumetric blends of coconut biodiesel with ULSD (coconut B5 and B20). In conventional diesel combustion mode, particulate matter (PM) and nitrogen oxides (NOx) emissions were similar for all fuels studied except soybean B20. Soybean B20 produced the lowest PM but the highest NOx emissions. Compared with conventional diesel combustion mode, high efficiency clean combustion (HECC) mode, achieved by increased EGR and combustion phasing, significantly reduced both PM and NOx emissions for all fuels studied at the expense of higher hydrocarbon (HC) and carbon monoxide (CO) emissions and an increase in fuel consumption (less than 4%).

Greenhouse gas regulations and global economic growth are expected to drive a future demand shift towards diesel fuel in the transportation sector. This may create a market opportunity for cost-effective fuels in the light distillate range if they can be burned as efficiently and cleanly as diesel fuel. In this study, the emission performance of a low cetane number, low research octane number naphtha (CN 34, RON 56) was examined on a production 6-cylinder heavy-duty on-highway truck engine and aftertreatment system. Using only production hardware, both the engine-out and tailpipe emissions were examined during the heavy-duty emission testing cycles using naphtha and ultra-low-sulfur diesel (ULSD) fuels. Without any modifications to the hardware and software, the tailpipe emissions were comparable when using either naphtha or ULSD on the heavy duty test cycles.

Lean Gasoline Direct Injection (LGDI) combustion is a promising technical path for achieving significant improvements in fuel efficiency while meeting future emissions requirements. Though Stoichiometric Gasoline Direct Injection (SGDI) technology is commercially available in a few vehicles on the American market, LGDI vehicles are not, but can be found in Europe. Oak Ridge National Laboratory (ORNL) obtained a European BMW 1-series fitted with a 2.01 LGDI engine. The vehicle was instrumented and commissioned on a chassis dynamometer. The engine and after-treatment performance and emissions were characterized over US drive cycles (Federal Test Procedure (FTP), the Highway Fuel Economy Test (HFET), and US06 Supplemental Federal Test Procedure (US06)) and steady state mappings. The vehicle micro hybrid features (engine stop-start and intelligent alternator) were benchmarked as well during the course of that study.

It is advantageous to increase the specific power output of diesel engines and to operate them at higher load for a greater portion of a driving cycle to achieve better thermal efficiency and thus reduce vehicle fuel consumption. Such operation is limited by excessive smoke formation at retarded injection timing and high rates of cylinder pressure rise at more advanced timing. Given this window of operation, it is desired to understand the influence of fuel properties such that optimum combustion performance and emissions can be retained over the range of fuels commonly available in the marketplace. Data are examined from a direct-injection single-cylinder research engine for eight common diesel fuels including soy-based biodiesel blends at two high load operating points with no exhaust gas recirculation (EGR) and at a moderate load with four levels of EGR.

We utilize physically-based models for diesel exhaust catalytic oxidation and urea-based selective catalytic NOx reduction to study their impact on drive cycle performance of hypothetical light-duty diesel-powered hybrid and plug-in hybrid vehicles (HEVs and PHEVs). The models have been implemented as highly flexible SIMULINK block modules that can be used to study multiple engine-aftertreatment system configurations. The parameters of the NOx reduction model have been adjusted to reflect the characteristics of commercially available Cu-zeolite catalysts, which are of widespread current interest. We demonstrate application of these models using the Powertrain System Analysis Toolkit (PSAT) software for vehicle simulations, along with a previously published methodology that accounts for emissions and temperature transients in the engine exhaust.

We summarize results from comparative simulations of hybrid electric vehicles with either stoichiometric gasoline or diesel engines. Our simulations utilize previously published models of transient engine-out emissions and models of aftertreatment devices for both stoichiometric and lean exhaust. Fuel consumption and emissions were estimated for comparable gasoline and diesel light-duty hybrid electric vehicles operating over single and multiple urban drive cycles. Comparisons between the gasoline and diesel vehicle fuel consumptions and emissions were used to identify potential advantages and technical barriers for diesel hybrids.

As part of a multi-agency study concerning emissions and fuel consumption from heavy-duty diesel truck idling, Oak Ridge National Laboratory personnel measured CO, HC, NOx, CO2, O2, particulate matter (PM), aldehyde and ketone emissions from truck idle exhaust. Two methods of quantifying PM were employed: conventional filters and a Tapered Element Oscillating Microbalance (TEOM). A partial flow micro-dilution tunnel was used to dilute the sampled exhaust to make the PM and aldehyde measurements. The work was performed at the U.S. Army's Aberdeen Test Center's (ATC) climate controlled chamber. ATC performed 37 tests on five class-8 trucks (model years ranging from 1992 to 2001). One was equipped with an 11 hp diesel auxiliary power unit (APU), and another with a diesel direct-fired heater (DFH). The APU powers electrical accessories, heating, and air conditioning, whereas a DFH heats the cab in cold weather. Both devices offer an alternative to extended truck-engine idling.

In this study, the compression ratio of a commercial 15L heavy-duty diesel engine was lowered and a split injection strategy was developed to promote partially premixed compression ignition (PPCI) combustion. Various low reactivity gasoline-range fuels were compared with ultra-low-sulfur diesel fuel (ULSD) for steady-state engine performance and emissions. Specially, particulate matter (PM) emissions were examined for their mass, size and number concentrations, and further characterized by organic/elemental carbon analysis, chemical speciation and thermogravimetric analysis. As more fuel-efficient PPCI combustion was promoted, a slight reduction in fuel consumption was observed for all gasoline-range fuels, which also had higher heating values than ULSD. Since mixing-controlled combustion dominated the latter part of the combustion process, hydrocarbon (HC) and carbon monoxide (CO) emissions were only slightly increased with the gasoline-range fuels.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

It is well known that spark ignited engine performance and efficiency is closely coupled to fuel octane number. The present work combines historical and recent trends in spark ignition engines to build a database of engine design, performance, and fuel octane requirements over the past 80 years. The database consists of engine compression ratio, required fuel octane number, peak mean effective pressure, specific output, and combined unadjusted fuel economy for passenger vehicles and light trucks. Recent trends in engine performance, efficiency, and fuel octane number requirement were used to develop correlations of fuel octane number utilization, performance, specific output. The results show that historically, engine compression ratio and specific output have been strongly coupled to fuel octane number.

The variability in gasoline energy content, though most frequently not a consumer concern, is an issue of concern for vehicle manufacturers in demonstrating compliance with regulatory requirements. Advancements in both vehicle technology, test methodology, and fuel formulations have increased the level of visibility and concern with regard to the energy content of fuels used for regulatory testing. The R factor was introduced into fuel economy calculations for vehicle certification in the late 1980s as a means of addressing batch-to-batch variations in the heating value of certification fuels and the resulting variations in fuel economy results. Although previous studies have investigated values of the R factor for modern vehicles through experimentation, subsequent engine studies have made clear that it is difficult to distinguish between the confounding factors that influence engine efficiency when R is being studied experimentally.

An engine-aftertreatment computational model was developed to support in-loop performance simulations of tailpipe emissions and fuel consumption associated with a range of heavy-duty (HD) truck drive cycles. For purposes of this study, the engine-out exhaust dynamics were simulated with a combination of steady-state engine maps and dynamic correction factors that accounted for recent engine operating history. The engine correction factors were approximated as dynamic first-order lags associated with the thermal inertia of the major engine components and the rate at which engine-out exhaust temperature and composition vary as combustion heat is absorbed or lost to the surroundings. The aftertreatment model included catalytic monolith components for diesel exhaust oxidation, particulate filtration, and selective catalytic reduction of nitrogen oxides (NOx) with urea.