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As emission regulations become increasingly strict, the need for more accurate sampling systems becomes essential. When calculating emissions from a dilution system, a correction is made to remove the effects of contaminants in the dilution air. The dilution air correction was explored to determine why this correction is needed, when this correction is important, and what methods are available for calculating the dilution factor (DF). An experimental and error analysis investigation into the standard and recently proposed methods for calculating the DF was conducted. Five steady state modes were run on a 1992 Detroit Diesel engine series 60 and the DF from eleven different equations were investigated. The effects of an inaccurate dilution air correction on calculated fuel flow from a carbon balance and the mass emissions was analyzed. The dilution air correction was shown to be important only for hydrocarbons, particulate matter (PM), and CO2.

Heavy-Duty Diesel (HDD) engines' particulate matter (PM) emissions are most often measured quantitatively by weighing filters that collect diluted exhaust samples pre- and post-test. PM sampling systems that dilute exhaust gas and collect PM samples have different effects on measured PM data. Those effects usually contribute to inter-laboratory variance. The U.S. Environmental Protection Agency (EPA)'s 2007 PM emission measurement regulations for the test of HDD engines should reduce variability, but must also cope with PM mass that is an order of magnitude lower than legacy engine testing. To support the design of a 2007 US standard HDD PM emission sampling system, a parametric study based on a systematic Simulink® model was performed. This model acted as an auxiliary design tool when setting up a new 2007 HDD PM emission sampling system in a heavy-duty test cell at West Virginia University (WVU). It was also designed to provide assistance in post-test data processing.

West Virginia University has conducted research to characterize the emissions from medium-duty vehicles operating on Fischer-Tropsch synthetic gas-to-liquid compression ignition fuel. The West Virginia University Transportable Heavy Vehicle Emissions Testing Laboratory was used to collect data for gaseous emissions (carbon dioxide, carbon monoxide, oxides of nitrogen, and total hydrocarbon) while the vehicles were exercised through a representative driving schedule, the New York City Bus Cycle (NYCB). Artificial neural networks were used to model emissions to enhance the capabilities of computer-based vehicle operation simulators. This modeling process is presented in this paper. Vehicle velocity, acceleration, torque at rear axel, and exhaust temperature were used as inputs to the neural networks. For each of the four gaseous emissions considered, one set of training data and one set of validating data were used, both based on the New York City Bus Cycle.

Biodiesel fuels benefit both from being a renewable energy source and from decreasing in carbon monoxide (CO), total hydrocarbons (THC), and particulate matter (PM) emissions relative to petroleum diesel. The oxides of nitrogen (NOx) emissions from biodiesel blended fuels reported in the literature vary relative to baseline diesel NOx, with no NOx change or a NOx decrease found by some to an increase in NOx found by others. To explore differences in NOx, two Cummins ISM engines (1999 and 2004) were operated on 20% biodiesel blends during the heavy-duty transient FTP cycle and the steady state Supplemental Emissions Test. For the 2004 Cummins ISM engine, in-cylinder pressure data were collected during the steady state and transient tests. Three types of biodiesel fuels were used in the blends: soy, tallow (animal fat), and cottonseed. The FTP integrated emissions of the B20 blends produced a 20-35% reduction in PM and no change or up to a 4.3% increase in NOx over the neat diesel.

The California Air Resources Board (ARB) developed a Medium Heavy-Duty Truck (MHDT) schedule by selecting and joining microtrips from real-world MHDT. The MHDT consisted of three modes; namely, a Lower Speed Transient, a Higher Speed Transient, and a Cruise mode. The maximum speeds of these modes were 28.9, 58.2 and 66.0 mph, respectively. Each mode represented statistically selected truck behavior patterns in California. The MHDT is intended to be applied to emissions characterization of trucks (14,001 to 33,000lb gross vehicle weight) exercised on a chassis dynamometer. This paper presents the creation of the MHDT and an examination of repeatability of emissions data from MHDT driven through this schedule. Two trucks were procured to acquire data using the MHDT schedule. The first, a GMC truck with an 8.2-liter Isuzu engine and a standard transmission, was tested at laden weight (90% GVW, 17,550lb) and at unladen weight (50% GVW, 9,750lb).

When preparing inventory models, it is desirable to obtain representative distance-specific emissions factors that truthfully represent the vehicle activity on a particular road (facility) type. Unfortunately, emissions values are often measured using only one test schedule, which represents a single average speed and a specific type of activity. This paper investigated the accuracy of predicting the emissions for a test schedule based on measurements from a different test schedule for the case of a medium heavy-duty truck. First, the traditional Speed Correction Factor (SCF) approach was examined, followed by the use of a power-based model derived from continuous data, followed by an artificial neural network (ANN) approach. The SCF modeling used distance-averaged emissions and cycle-averaged vehicle speed to predict distance-averaged NOx. The power-based modeling was based on linear and polynomial correlations between continuous axle power and NOx.

Today's computer-based vehicle operation simulators use engine speed, engine torque, and lookup tables to predict emissions during a driving simulation [1]. This approach is used primarily for light and medium-duty vehicles, with large discrepancies inherently due to the lack of transient engine emissions data and inaccurate emissions prediction methods [2]. West Virginia University (WVU) has developed an artificial neural network (ANN) based emissions model for incorporation into the ADvanced VehIcle SimulatOR (ADVISOR) software package developed by the National Renewable Energy Laboratory (NREL). Transient engine dynamometer tests were conducted to obtain training data for the ANN. The ANN was trained to predict carbon dioxide (CO2) and oxides of nitrogen (NOx) emissions based on engine speed, torque, and their representative first and second derivatives over various time ranges.

The use of Artificial Neural Networks (ANNs) as a predictive tool has been shown to have a broad range of applications. Earlier work by the authors using ANN models to predict carbon dioxide (CO2), carbon monoxide (CO), oxides of nitrogen (NOx), and particulate matter (PM) from heavy-duty diesel engines and vehicles yielded marginal to excellent results. These ANN models can be a useful tool in inventory prediction, hybrid vehicle design optimization, and incorporated into a feedback loop of an on-board, active fuel injection management system. In this research, the ANN models were trained on continuous engine and emissions data. The engine data were used as inputs to the ANN models and consisted of engine speed, torque, and their respective first and second derivatives over a one, five, and ten second time range. The continuous emissions data were the desired output that the ANN models learned to predict through an iterative training process.

A study was performed in the spring of 2001 to chemically characterize exhaust emissions from trucks and buses fueled by various test fuels and operated with and without diesel particle filters. This study was part of a multi-year technology validation program designed to evaluate the emissions impact of ultra-low sulfur diesel fuels and passive diesel particle filters (DPF) in several different heavy-duty vehicle fleets operating in Southern California. The overall study of exhaust chemical composition included organic compounds, inorganic ions, individual elements, and particulate matter in various size-cuts. Detailed descriptions of the overall technology validation program and chemical speciation methodology have been provided in previous SAE publications (2002-01-0432 and 2002-01-0433).

Heavy duty diesel vehicle (HDDV) emissions are known to affect air quality, but few studies have quantified the real-world contribution to the inventory. The objective of this study was to provide data that may enable ambient emissions investigators to m,odel the air quality more accurately. The 25 vehicles reported in this paper are from the first phase of a program to determine representative regulated emissions from Heavy Heavy-Duty Diesel Trucks (HHDDT) operating in Southern California. Emissions data were gathered using a chassis dynamometer, full flow dilution tunnel, and research grade analyzers. The subject program employed two truck test weights and four new test modes (one was idle operation), in addition to the Urban Dynamometer Driving Schedule (UDDS), and the AC50/80 cycle. The reason for such a broad test cycle scope was to determine thoroughly how HHDDT emissions are influenced by operating cycle to improve accuracy of models.

Gaseous and particulate emissions from heavy-duty vehicles are affected by fuel types, vehicle/engine parameters, driving characteristics, and environmental conditions. Transient chassis tests were conducted on twenty-six heavy-duty vehicles fueled with methanol, compressed natural gas (CNG), #1 diesel, and #2 diesel, using West Virginia University (WVU) Transportable Heavy-Duty Vehicle Emissions Testing Laboratory. The vehicles were operated on the central business district (CBD) testing cycle, and regulated emissions of carbon monoxide (CO), total hydrocarbon (HC), nitrogen oxides (NOx), and particulate matter (PM) were measured. Comparisons of regulated emissions results revealed that the vehicles powered on methanol and CNG produced much lower particulate emissions than the conventionally fueled vehicles.

The West Virginia University (WVU) Transportable Heavy-Duty Vehicle Emissions Testing Laboratory was employed to conduct chassis dynamometer tests in the field to measure the exhaust emissions from heavy-duty buses and trucks. This laboratory began operation in the field in January, 1992. During the period January, 1992 through June, 1993, over 150 city buses, trucks, and tractors operated by 18 different authorities in 11 states were tested by the facility. The tested vehicles were powered by 14 different types of engines fueled with natural gas (CNG or LNG), methanol, ethanol, liquified petroleum gas (LPG), #2 diesel, and low sulfur diesel (#1 diesel or Jet A). Some of the tested vehicles were equipped with exhaust after-treatment systems. In this paper, a total of 12 CNG-fueled and #2 diesel-fueled transit buses equipped with Cummins L-10 engines, were chosen for investigation.

Increasingly stringent oxides of nitrogen (NOx) and particulate matter (PM) regulations worldwide have prompted considerable activity in developing emission control technology to reduce the emissions of these two constituents from heavy-duty diesel engines. NOx has come under particular scrutiny by regulators in the US and in Europe with the promulgation of very stringent regulation by both the US Environmental Protection Agency (EPA) and the European Union (EU). In response, heavy-duty engine manufacturers are considering Selective Catalytic Reduction (SCR) as a potential NOx reduction option. While SCR performance has been well established through engine dynamometer evaluation under laboratory conditions, there exists little data characterizing SCR performance under real-world operating conditions over time. This project evaluated the field performance of ten SCR units installed on heavy-duty Class 8 highway and refuse trucks.

Emissions from heavy-duty vehicles may be reduced through the introduction of clean diesel formulations, and through the use of catalyzed particulate matter filters that can enjoy increased longevity and performance if ultra-low sulfur diesel is used. Twenty over-the-road tractors with Detroit Diesel Series 60 engines were selected for this study. Five trucks were operated on California (CA) specification diesel (CARB), five were operated on ARCO (now BP Amoco) EC diesel (ECD), five were operated on ARCO ECD with a Johnson-Matthey Continuously Regenerating Technology (CRT) filter and five were operated on ARCO ECD with an Engelhard Diesel Particulate Filter (DPX). The truck emissions were characterized using a transportable chassis dynamometer, full-scale dilution tunnel, research grade gas analyzers and filters for particulate matter (PM) mass collection. Two test schedules, the 5 mile route and the city-suburban (heavy vehicle) route (CSR), were employed.

Relationships between diesel particulate matter (PM) mass and gaseous emissions mass produced by engines have been explored to determine whether any gaseous species may be used as surrogates to infer PM quantitatively. It was recognized that sulfur content of fuel might independently influence PM mass, since PM historically is composed of elemental carbon, organic carbon, sulfuric acid, ash and wear particles. Previous research has suggested that PM may be correlated with carbon monoxide (CO) for an engine that is exercised through a variety of speed and load cycles, but that the correlation does not extend to a group of engines. Large databases from the E-55/59 and Gasoline/Diesel PM Split programs were employed, along with the IBIS bus emissions database and several additional data sets for on- and off-road engines to examine possible relationships.

In 2007, the Environmental Protection Agency will begin measuring not only exhaust emissions from diesel engines, but also emissions from the crankcase if it is not vented into the engine intake. The 2007 government standards for emissions of carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NOx) and particulate matter (PM) will also become more restrictive. There is the additional concern that crankcase emissions from present day trucks and buses may impact the quality of air inside the vehicle. This paper presents data to characterize crankcase emissions and examines a crankcase emissions abatement system (CEAS), the New Condensator®, manufactured by World NCI. Rather than allowing crankcase emissions to leave via a vent tube, a CEAS re-circulates the emissions to the intake of the engine.

Transit agencies across the United States operate bus fleets primarily powered by diesel, natural gas, and hybrid drive systems. Passenger loading affects the power demanded from the engine, which in turn affects distance-specific emissions and fuel consumption. Analysis shows that the nature of bus activity, taking into account the idle time, tire rolling resistance, wind drag, and acceleration energy, influences the way in which passenger load impacts emissions. Emissions performance and fuel consumption from diesel and natural gas powered buses were characterized by the West Virginia University (WVU) Transportable Emissions Testing Laboratory. A comparison matrix for all three bus technologies included three common driving cycles (the Braunschweig Cycle, the OCTA Cycle, and the ADEME-RATP Paris Cycle). Each bus was tested at three different passenger loading conditions (empty weight, half weight, and full weight).

Typically ethanol is present in gasoline as a 10% blend by volume (E10), although E15, E85 (51 to 83%), and E0 are also available at selected stations. Numerous studies of tailpipe regulated emissions have been conducted to compare emissions from E10 and E0, and there is a growing body of literature addressing blends of E15 and higher. Isolating the effect of ethanol in a study is philosophically difficult, because the ethanol naturally displaces some hydrocarbons, because the ethanol interacts with the remaining gasoline, and because properties of mixing are often nonlinear. Some studies have used splash blending, simply mixing the ethanol with a reference gasoline to produce a blend for comparison to the reference. Others have used match blending, where the objective is to match selected properties of the blend to properties of a reference gasoline.