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Transit buses contribute a meager amount to the U.S. criteria pollutant and greenhouse gas (GHG) inventory, but they attract a lot of attention from the public and from local government, due to their nature of operation. Transit bus fleets are often employed for the introduction of advanced heavy-duty vehicle technology and the formulation of new performance models. Emissions and fuel consumption data, gained using a chassis dynamometer, are often used to evaluate performance of these buses. However, the effect of road grade on fuel consumption and emissions most often is not accounted for in chassis dynamometer characterization. Grade effect on transit buses' fuel consumption was investigated using the road-load equation. It was observed that two parameters, including the type of terrain that buses traverse and the percentage of grade for that terrain, needed to be determined for this investigation.

As part of the 1998 Consent Decrees concerning alternative ignition strategies between the six settling heavy-duty diesel engine manufacturers and the United States government, the engine manufacturers agreed to perform in-use emissions measurements of their engines. As part of the Consent Decrees, pre- (Phase III, pre-2000 engines) and post- (Phase IV, 2001 to 2003 engines) Consent Decree engines used in over-the-road vehicles were tested to examine the emissions of oxides of nitrogen (NOx) and carbon dioxide (CO2). A summary of the emissions of NOx and CO2 and fuel consumption from the Phase III and Phase IV engines are presented for 30 second “Not-to-Exceed” (NTE) window brake-specific values. There were approximately 700 Phase III tests and 850 Phase IV tests evaluated in this study, incorporating over 170 different heavy duty diesel engines spanning 1994 to 2003 model years. Test vehicles were operated over city, suburban, and highway routes.

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.

Particulate emission control from diesel engines is one of the major concerns in the urban areas in California. Recently, regulations have been proposed for stringent PM emission requirements from both existing and new diesel engines. As a result, particulate emission control from urban diesel engines using advanced particulate filter technology is being evaluated at several locations in California. Although ceramic based particle filters are well known for high PM reductions, the lack of effective and durable regeneration system has limited their applications. The continuously regenerated diesel particulate filter (CRDPF) technology discussed in this presentation, solves this problem by catalytically oxidizing NO present in the diesel exhaust to NO2 which is utilized to continuously combust the engine soot under the typical diesel engine operating condition.

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).

Recent chassis testing of hybrid buses demonstrated the potential of hybrid technology to reduce emissions and raise fuel economy relative to conventional buses. However, hybrid buses represent a certification quandary because the engines must be certified using the accepted Federal Test Procedure (FTP), without regard for benefits that may arise from less transient engine operation. Actual engine operating data from series configuration hybrid buses were analyzed to determine the envelopes of torque and speeds covered by the engine. Transient engine operation was also considered in terms of rates of change of torque, power and speed. These measures did not compare closely with similar measures computed from the FTP because the series hybrid engines explored a more structured zone of operation than the FTP implied and because the FTP represented more transient operation.

Selective Catalytic Reduction (SCR), using urea injection, is being examined as a method for substantial reduction of oxides of nitrogen (NOx) for diesel engines, but the urea injection rates must be controlled to match the NOx production which may need to be predicted during open loop control. Unfortunately NOx is usually measured in the laboratory using a full-scale dilution tunnel and chemiluminescent analyzer, which cause delay and diffusion (in time) of the true manifold NOx concentration. Similarly, delay and diffusion of measurements of all emissions cause the task of creating instantaneous emissions models for vehicle simulations more difficult. Data were obtained to relate injections of carbon dioxide (CO2) into a tunnel with analyzer measurements. The analyzer response was found to match a gamma distribution of the input pulse, so that the analyzer output could be modeled from the tunnel CO2 input.

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.

A program has been completed to evaluate ultra-low sulfur diesel fuels and passive diesel particulate filters (DPFs) in truck and bus fleets operating in southern California. The fuels, ECD and ECD-1, are produced by ARCO (a BP Company) and have less than 15 ppm sulfur content. Vehicles were retrofitted with two types of catalyzed DPFs, and operated on ultra-low sulfur diesel fuel for over one year. Exhaust emissions, fuel economy and operating cost data were collected for the test vehicles, and compared with baseline control vehicles. Regulated emissions are presented from two rounds of tests. The first round emissions tests were conducted shortly after the vehicles were retrofitted with the DPFs. The second round emissions tests were conducted following approximately one year of operation. Several of the vehicles retrofitted with DPFs accumulated well over 100,000 miles of operation between test rounds.

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 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.

Hybrid diesel electric buses offer the advantage of superior fuel economy through use of regenerative braking and lowered transient emissions by reducing the need of the engine to follow load as closely as in a conventional bus. With the support of the Department of Energy (DOE), five Lockheed Martin-Orion hybrid diesel-electric buses were operated on the West Virginia University Transportable Laboratory in Brooklyn, New York. The buses were exercised through a new cycle, termed the Manhattan cycle, that was representative of today's bus use as well as the accepted Central Business District Cycle and New York Bus Cycle. Emissions data were corrected for the state of charge of the batteries. The emissions can be expressed in units of grams/mile, grams/axle hp-hr and grams/gallon fuel. The role of improved fuel economy in reducing oxides of nitrogen relative to conventional automatic buses is evident in the data.

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.

Alternative compression ignition engine fuels are of interest both to reduce emissions and to reduce U.S. petroleum fuel demand. A Malaysian Fischer-Tropsch gas-to-liquid fuel was compared with California #2 diesel by characterizing emissions from over the road Class 8 tractors with Caterpillar 3176 engines, using a chassis dynamometer and full scale dilution tunnel. The 5-Mile route was employed as the test schedule, with a test weight of 42,000 lb. Levels of oxides of nitrogen (NOx) were reduced by an average of 12% and particulate matter (PM) by 25% for the Fischer-Tropsch fuel over the California diesel fuel. Another distillate fuel produced catalytically from Fischer-Tropsch products originally derived from natural gas by Mossgas was also compared with 49-state #2 diesel by characterizing emissions from Detroit Diesel 6V-92 powered transit buses, three of them equipped with catalytic converters and rebuilt engines, and three without.

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.

Fuel economy and regulated emissions were measured from eight forty-foot transit buses operated on petroleum diesel and a “B20” blend of 80% diesel fuel and 20% biodiesel by volume. Use of biodiesel is attractive to displace petroleum fuel and reduce an operation's carbon footprint. Usually it is assumed that biodiesel will also reduce particulate matter (PM) emissions relative to those of petroleum diesel. Model years of the vehicles evaluated were newer 2007-08 Gillig low-floor buses, 2005 Gillig Phantom buses, and a 2002 Gillig Phantom bus. Engine technology represented three different emissions standards, and included buses with OEM diesel particulate filters. Each bus was evaluated using two transient speed-time schedules, the Orange County Transit Authority (OCTA) driving schedule which represents moderate speed urban/suburban operation and the Urban Dynamometer Driving Schedule (UDDS) which represents a mix of suburban and higher speed on-highway operation.

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).

West Virginia University characterized the emissions and fuel economy performance of a 30-foot 2010 transit bus equipped with urea selective catalytic reduction (u-SCR) exhaust aftertreatment. The bus was exercised over speed-time driving schedules representative of both urban and on-highway activity using a chassis dynamometer while the exhaust was routed to a full-scale dilution tunnel with research grade emissions analyzers. The Paris speed-time driving schedule was used to represent slow urban transit bus activity while the Cruise driving schedule was used to represent on-highway activity. Vehicle weights representative of both one-half and empty passenger loading were evaluated. Fuel economy observed during testing with the urban driving schedule was significantly lower (55%) than testing performed with the on-highway driving schedule.

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.