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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.

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.

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.

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.

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.

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.

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).

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 trucks and buses equipped with Caterpillar dual-fuel natural gas (DFNG) engines were measured at two chassis dynamometer facilities: the West Virginia University (WVU) Transportable Emissions Laboratory and the Los Angeles Metropolitan Transportation Authority (LA MTA). Emissions were measured over four different driving cycles. The average emissions from the trucks and buses using DFNG engines operating in dual-fuel mode showed the same trends in all tests - reduced oxides of nitrogen (NOx) and particulate matter (PM) emissions and increased hydrocarbon and carbon monoxide (CO) emissions - when compared to similar diesel trucks and buses. The extent of NOx reduction was dependent on the type of test cycle used.

Emissions of six 32 passenger transit buses were characterized using one of the West Virginia University (WVU) Transportable Heavy Duty Emissions Testing Laboratories, and the fixed base chassis dynamometer at the Colorado Institute for Fuels and High Altitude Engine Research (CIFER). Three of the buses were powered with 1997 ISB 5.9 liter Cummins diesel engines, and three were powered with the 1997 5.9 liter Cummins natural gas (NG) counterpart. The NG engines were LEV certified. Objectives were to contrast the emissions performance of the diesel and NG units, and to compare results from the two laboratories. Both laboratories found that oxides of nitrogen and particulate matter (PM) emissions were substantially lower for the natural gas buses than for the diesel buses. It was observed that by varying the rapidity of pedal movement during accelerations in the Central Business District cycle (CBD), CO and PM emissions from the diesel buses could be varied by a factor of three or more.

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.

Hybrid-electric transit buses offer benefits over conventional transit buses of comparable capacity. These benefits include reduced fuel consumption, reduced emissions and the utilization of smaller engines. Factors allowing for these benefits are the use of regenerative braking and reductions in engine transient operation through sophisticated power management systems. However, characterization of emissions from these buses represents new territory: the whole vehicle must be tested to estimate real world tailpipe emissions levels and fuel economy. The West Virginia University Transportable Heavy Duty Emissions Testing Laboratories were used to characterize emissions from diesel hybrid-electric powered as well as diesel and natural gas powered transit buses in Boston, MA and New York City.

Biodiesel may be derived from either plant or animal sources, and is usually employed as a compression ignition fuel in a blend with petroleum diesel (PD). Emissions differences between vehicles operated on biodiesel blends and on diesel have been published previously, but data do not cover the latest engine technologies. Prior studies have shown that biodiesel offers advantages in reducing particulate matter, with either no advantage or a slight disadvantage for oxides of nitrogen emissions. This paper describes a recent study on the emissions impact of two biodiesel blends B20A, made from 20% animal fat (tallow) biodiesel and 80% PD, and B20B, obtained from 20% soybean biodiesel and 80% PD. These blends used the same PD fuel for blending and were contrasted with the same PD fuel as a reference. The research was conducted on a 2007 medium heavy-duty diesel truck (MHDDT), with an engine equipped with Exhaust Gas Recirculation (EGR) and a Diesel Particulate Filter (DPF).

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.