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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 fuel cell bus program at Georgetown University (GU) has directed the operational development and testing of three hybrid fuel cell powered buses for transit operation. These are the world's first liquid-fueled, fuel cell powered road vehicles. This paper describes the emissions testing of one of these buses on a heavy duty chassis dynamometer at West Virginia University (WVU). The tested bus was driven by a 120 kW DC motor and utilized a 50 kW phosphoric acid fuel cell (PAFC) as an energy source with a 100 kW battery for supplemental power. A methanol/water fuel mixture was converted by a steam reformer to a hydrogen rich gas mixture for use in a fuel cell stack. Emissions from the reformer, fuel cell stack and startup burner were monitored for both transient and steady-state operation.

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.

New York City Department of Sanitation has operated natural gas fueled refuse haulers in a pilot study: a major goal of this study was to compare the emissions from these natural gas vehicles with their diesel counterparts. The vehicles were tandem axle trucks with GVW (gross vehicle weight) rating of 69,897 pounds. The primary use of these vehicles was for street collection and transporting the collected refuse to a landfill. West Virginia University Transportable Heavy Duty Emissions Testing Laboratories have been engaged in monitoring the tailpipe emissions from these trucks for seven-years. In the later years of testing the hydrocarbons were speciated for non-methane and methane components. Six of these vehicles employed the older technology (mechanical mixer) Cummins L-10 lean burn natural gas engines.

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.

Diesel particulate matter emissions, because they do not disperse as readily gaseous emissions, have a very localized effect and eventually settle to the ground not far from where they were emitted. One subset of heavy-duty diesel vehicles that warrant further attention for controlling particulate emissions matter is sanitation trucks. Cummins Inc. and West Virginia University investigated particulate emissions reduction technologies for New York City Department of Sanitation refuse trucks under the EPA Consent Decree program. Regulated emissions were measured on four retrofitted sanitation trucks with and without the DPF installed. Cummins engines powered all of the retrofitted trucks. The Engelhard DPX reduced PM emissions by 97% and 84% on the New York Garbage Truck Cycle (NYGTC) and Orange County Refuse Truck Cycle (OCRTC) respectively. The Johnson-Matthey CRT system reduced PM emissions by 81% and 87% over the NYGTC and OCRTC respectively.

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.

Concern over atmospheric pollution has led to the development of testing procedures to evaluate the hydrocarbon, carbon dioxide, carbon monoxide and oxides of nitrogen emissions from internal combustion engines. In order to perform emissions testing on vehicles, a chassis dynamometer capable of simulating expected driving conditions must be employed. West Virginia University has developed a Heavy Duty Transportable Emissions Testing Laboratory to perform chassis testing on trucks and buses. Emissions from the vehicle are monitored and recorded over the duration of a testing schedule. Usually the vehicle emissions from the whole test are reported as mass of emissions per unit distance driven. However, there is interest in relating the instantaneous emissions to the immediate conditions at specific points in the test, and in determining the emissions for discrete segments of the test (modal analysis).

A Saturn 1.9 liter engine has been converted for operation on either compressed natural gas or gasoline. A bi-fuel controller (BFC) that uses closed-loop control methods for both fuel delivery and spark advance has been developed. The performance and emissions during operation on each fuel have been investigated with the BFC, as well as the performance and emissions with the stock original equipment manufacturer (OEM) controller using gasoline. In-cylinder pressure was measured at a rate of 1024 points per revolution with piezoelectric pressure transducers flush-mounted in the cylinder head. The in-cylinder pressure was used in real time for ignition timing control purposes, and was stored by a data acquisition system for the investigation of engine stability and differences in the combustion properties of the fuels.

Emissions from light duty vehicles have traditionally been measured using a chassis dynamometer, while heavy duty testing has been based on engine dynamometers. However, the need for in-use vehicle emissions data has led to the development of two transportable heavy duty chassis dynamometers capable of testing buses and heavy trucks. A test cycle has been developed for Class 8 trucks, which typically have unsyncronized transmissions. This test cycle has five peaks, each consisting of an acceleration, cruise period, and deceleration, with speeds and acceleration requirements that can be met by virtually all vehicles in common service. Termed the “WVU 5 peak truck test”, this 8 km (5 mile) cycle has been used to evaluate the emissions from diesel and ethanol powered over-the-road tractors and from diesel and ethanol powered snow plows, all with Detroit Diesel 6V92 engines.

Data has been gathered using the West Virginia University Heavy Duty Transportable Emissions Laboratories from buses operating on diesel and a variety of alternate fuels in the field. Typically, the transportable chassis dynamo meter is set up at a local transit agency and the selected buses are tested using the fuel in the vehicle at the time of the test. The dynamometer may be set up to operate indoors or outdoors depending on the space available at the site. Samples of the fuels being used at the site are collected and sent to the laboratory for analysis and this information is then sent together with emissions data to the Alternate Fuels Data Center at the National Renewable Energy Laboratory. Emissions data are acquired from buses using the Central Business District cycle reported in SAE Standard J1376; this cycle has 14 ramps with 20 mph (32.2 km/h) peaks, separated by idle periods.

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).