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Natural Gas engines are viewed as an alternative to diesel power in the quest to reduce heavy duty vehicle emissions in polluted urban areas. In particular, it is acknowledged that natural gas has the potential to reduce the inventory of particulate matter, and this has encouraged the use of natural gas engines in transit bus applications. Extensive data on natural gas and diesel bus emissions have been gathered using two Transportable Heavy Duty Vehicle Emissions Testing Laboratories, that employ chassis dynamometers to simulate bus inertia and road load. Most of the natural gas buses tested prior to 1997 were powered by Cummins L-10 engines, which were lean-burn and employed a mechanical mixer for fuel introduction. The Central Business District (CBD) cycle was used as the test schedule.

The objective of this project, which is supported by the U.S. Department of Energy (DOE) through the National Renewable Energy Laboratory (NREL), is to provide a comprehensive comparison of heavy-duty trucks operating on alternative fuels and diesel fuel. Data collection from up to eight sites is planned. This paper summarizes the design of the project and early results from the first two sites. Data collection is planned for operations, maintenance, truck system descriptions, emissions, duty cycle, safety incidents, and capital costs and operating costs associated with the use of alternative fuels in trucking.

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 Fischer-Tropsch (F-T) catalytic conversion process can be used to synthesize diesel fuels from a variety of feedstocks, including coal, natural gas and biomass. Synthetic diesel fuels can have very low sulfur and aromatic content, and excellent autoignition characteristics. Moreover, Fischer-Tropsch diesel fuels may also be economically competitive with California diesel fuel if produced in large volumes. An overview of Fischer-Tropsch diesel fuel production and engine emissions testing is presented. Previous engine laboratory tests indicate that F-T diesel is a promising alternative fuel because it can be used in unmodified diesel engines, and substantial exhaust emissions reductions can be realized. The authors have performed preliminary tests to assess the real-world performance of F-T diesel fuels in heavy-duty trucks. Seven White-GMC Class 8 trucks equipped with Caterpillar 10.3 liter engines were tested using F-T diesel fuel.

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.

Cummins Westport Inc. (CWI) released for production the latest version of its C8.3G natural gas engine, the C Gas Plus, in July 2001. This engine has increased ratings for horsepower and torque, a full-authority engine controller, wide tolerance to natural gas fuel (the minimum methane number is 65), and improved diagnostics capability. The C Gas Plus also meets the California Air Resources Board optional low-NOx (2.0 g/bhp-h) emission standard for automotive and urban buses. Two pre-production C Gas Plus engines were operated in a Viking Freight fleet for 12 months as part of the U.S. Department of Energy's Fuels Utilization Program. In-use exhaust emissions, fuel economy, and fuel cost were collected and compared with similar 1997 Cummins C8.3 diesel tractors. CWI and the West Virginia University developed an ad-hoc test cycle to simulate the Viking Freight fleet duty cycle from in-service data collected with data loggers.

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).

The recent oil crisis has once again emphasized the need to develop both fuel efficient engines and alternately fueled engines, particularly for automotive applications. Engines which burn coal or coal pyrolysis products are attractive, but ignition delay and metal erosion problems continue to limit high speed operation of such engines. Further, the throttled spark ignition engine often used with methanol and natural gas does not prove an efficient or tolerant device for the combustion of a wide range of fuel. Therefore, an novel approach must be taken in order to achieve the efficient and flexible operation of such an engine. A novel design of a fuel tolerant engine suitable for burning coal fuels separates the combustion from the piston in order to have more careful flame control and to exclude the particulate matter from the engine's piston rings.

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.

With continuously increasing concern for the emissions from two-stroke engines including regulated hydrocarbon (HC) and oxides of nitrogen (NOx) emissions, non-road engines are implementing proven technologies from the on-road market. For example, four stroke diesel generators now include additional internal exhaust gas recirculation (EGR) via an intake/exhaust valve passage. EGR can offer benefits of reduced HC, NOx, and may even improve combustion stability and fuel efficiency. In addition, there is particular interest in use of natural gas as fuel for home power generation. This paper examines exhaust throttling applied to the Helmholtz resonator of a two-stroke, port injected, natural gas engine. The 34 cc engine was air cooled and operated at wide-open throttle (WOT) conditions at an engine speed of 5400 RPM with fueling adjusted to achieve maximum brake torque. Exhaust throttling served as a method to decrease the effective diameter of the outlet of the convergent cone.

Both field research and certification data show that the lean burn natural gas powered spark ignition engines offer particulate matter (PM) reduction with respect to equivalent diesel power plants. Concerns over PM inventory make these engines attractive despite the loss of fuel economy associated with throttled operation. Early versions of the Cummins L-10 natural gas engines employed a mixer to establish air/fuel ratio. Emissions measurements by the West Virginia University Transportable Heavy Duty Emissions Testing Laboratories on Cummins L-10 powered transit buses revealed the potential to offer low emissions of PM and oxides of nitrogen, (NOx) but variations in the mixture could cause emissions of NOx, carbon monoxide and hydrocarbons to rise. This was readily corrected through mixer repair or readjustment. Newer versions of the L-10 engine employ a more sophisticated fueling scheme with feedback control from a wide range oxygen sensor.

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.

Techniques have been developed to sample and speciate dilute heavy duty diesel exhaust to determine the specific reactivities and the ozone forming potential. While the Auto/Oil Air Quality Improvement Research Program (AQIRP) has conducted a comprehensive investigation to develop data on potential improvements in vehicle emissions and air quality from reformulated gasoline and various other alternative fuels. However, the development of sampling protocols and speciation of heavy duty diesel exhaust is still in its infancy [1, 2, 3, 4, 5 and 6]. This paper focuses on the first phase of the heavy duty diesel speciation program, that involves the development of a unique set of sampling protocols for the gas phase, semi-volatile and particulate matter from the exhaust of engines operating on different types of diesel fuel. Effects of sampling trains, sampling temperatures, semi-volatile adsorbents and driving cycles are being investigated.

The objective of this program, which is supported by the U.S. Department of Energy (DOE) through the National Renewable Energy Laboratory (NREL), is to provide an unbiased and comprehensive comparison of transit buses operating on alternative fuels and diesel fuel. The information for this comparison was collected from eight transit bus sites. The fuels studied are natural gas (CNG and LNG), alcohol (methanol and ethanol), biodiesel (20 percent blend), propane (only projected capital costs; no sites with heavy-duty propane engines were available for studying operating experience), and diesel. Data was collected on operations, maintenance, bus equipment configurations, emissions, bus duty cycle, and safety incidents. Representative and actual capital costs were collected for alternative fuels and were used as estimates for conversion costs. This paper presents preliminary results.

Six 2001 International Class 6 trucks participated in a project to determine the impact of gas-to-liquid (GTL) fuel and catalyzed diesel particle filters (DPFs) on emissions and operations from December 2003 through August 2004. The vehicles operated in Southern California and were nominally identical. Three vehicles operated “as-is” on California Air Resources Board (CARB) specification diesel fuel and no emission control devices. Three vehicles were retrofit with Johnson Matthey CCRT® (Catalyzed Continuously Regenerating Technology) filters and fueled with Shell GTL Fuel. Two rounds of emissions tests were conducted on a chassis dynamometer over the City Suburban Heavy Vehicle Route (CSHVR) and the New York City Bus (NYCB) cycle. The CARB-fueled vehicles served as the baseline, while the GTL-fueled vehicles were tested with and without the CCRT filters. Results from the first round of testing have been reported previously (see 2004-01-2959).