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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.

Dual primary full-flow dilution tunnels represent an integral part of a heavy-duty transportable emissions measurement laboratory designed and constructed to comply with US Code of Federal Regulations (CFR) 40 Part 1065 requirements. Few data exist to characterize the evolution of particulate matter (PM) in full scale dilution tunnels, particularly at very low PM mass levels. Size distributions of ultra-fine particles in diesel exhaust from a naturally aspirated, 2.4 liter, 40 kW ISUZU C240 diesel engine equipped with a diesel particulate filter (DPF) were studied in one set of standard primary and secondary dilution tunnels with varied dilution ratios. Particle size distribution data, during steady-state engine operation, were collected using a Cambustion DMS500 Fast Particulate Spectrometer. Measurements were made at four positions that spanned the tunnel cross section after the mixing orifice plate for the primary dilution tunnel and at the outlet of the secondary dilution tunnel.

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).

This paper summarizes the several of the studies in the Environmental Science Program being sponsored by DOE's Office of Heavy Vehicle Technologies (OHVT) through the National Renewable Energy Laboratory (NREL). The goal of the Environmental Science Program is to understand atmospheric impacts and potential health effects that may be caused by the use of petroleum-based fuels and alternative transportation fuels from mobile sources. The Program is regulatory-driven, and focuses on ozone, airborne particles, visibility and regional haze, air toxics, and health effects of air pollutants. Each project in the Program is designed to address policy-relevant objectives. Current projects in the Environmental Science Program have four areas of focus: improving technology for emissions measurements; vehicle emissions measurements; emission inventory development/improvement; ambient impacts, including health effects.

West Virginia University evaluated diesel oxidation catalysts (DOC) and lean-NOX catalysts as part of Diesel Emissions Control-Sulfur Effects (DECSE) project. In order to perform thermal aging of the DOC and lean-NOX catalysts simultaneously and economically, each catalyst was sized to accommodate half of the engine exhaust flow. Simultaneous catalyst aging was then achieved by splitting the engine exhaust into two streams such that approximately half of the total exhaust flowed through the DOC and half through the lean-NOX catalyst. This necessitated splitting the engine exhaust into two streams during emissions measurements. Throttling valves installed in each branch of the split exhaust were adjusted so that approximately half the engine exhaust passed though the active catalyst under evaluation and into a full flow dilution tunnel for emissions measurement.

Emissions tests for heavy -duty diesel-fueled engines and vehicles are normally performed using engine dynamometers and chassis dynamometers, respectively, with laboratory grade gaseous concentration measurement analyzers and supporting test equipment. However, a considerable effort has been recently expended on developing in-use, on-board tools to measure brake-specific emissions from heavy -duty vehicles with the highest degree of accuracy and precision. This alternative testing methodology would supplement the emissions data that is collected from engine and chassis dynamometer tests. The on-board emissions testing methodology entails actively recording emissions and vehicle operating parameters (engine speed and load, vehicle speed etc.) from vehicles while they are operating on the road. This paper focuses on in-use measurements of NOX with zirconium oxide sensors and other portable NOX detectors.

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.

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.

Emissions tests for heavy-duty diesel-fueled vehicles are normally performed using an engine dynamometer or a chassis dynamometer. Both of these methods generally entail the use of laboratory-grade emissions measurement instrumentation, a CVS system, an environment control system, a dynamometer, and associated data acquisition and control systems. The results obtained from such tests provide a means by which engines may be compared to the emissions standards, but may not be truly indicative of an engine's in-vehicle performance while operating on the road. An alternative to such a testing methodology would be to actively record the emissions from a vehicle while it was operating on-road. A considerable amount of discussion has been focused on the development of on-road emissions measurement systems (OREMS) that would provide for such in-use emissions data collection.

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.

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.

The emissions reduction benefits of Fischer-Tropsch (FT) diesel fuel have been shown in several recent published studies in both engine testing and in-use vehicle testing. FT diesel fuel shows significant advantages in reducing regulated engine emissions over conventional diesel fuel primarily to: its zero sulfur specification, implying reduced particulate matter (PM) emissions, its relatively lower aromaticity, and its relatively high cetane rating. However, the actual effect of FT diesel formulation on the in-cylinder combustion characteristics of unmodified modern heavy-duty diesel engines is not well documented. As a result, a Navistar T444E (V8, 7.3 liter) engine, instrumented for in-cylinder pressure measurement, was installed on an engine dynamometer and subjected to steady-state emissions measurement using both conventional Federal low sulfur pump diesel and a natural gas-derived FT fuel.

This paper summarizes current work in the Environmental Science & Health Effects (ES&HE) Program being sponsored by DOE's Office of Heavy Vehicle Technologies (OHVT) through the National Renewable Energy Laboratory (NREL). The program is regulatory-driven, and focuses on ozone, airborne particles, visibility and regional haze, air toxics, and health effects of air pollutants. The goal of the ES&HE Program is to understand atmospheric impacts and potential health effects that may be caused by the use of petroleum-based and alternative transportation fuels. Each project in the program is designed to address policy-relevant objectives. Studies in the ES&HE Program have four areas of focus: improving technology for emissions measurements; vehicle emissions measurements, emission inventory development/improvement; and ambient impacts, including health effects.

Although diesel engines still power most of the heavy-duty transit buses in the United States, many major cities are also operating fleets where a significant percentage of buses is powered by lean-burn natural gas engines. Emissions from these buses are often expressed in distance-specific units of grams per mile (g/mile) or grams per kilometer (g/km), but the driving cycle or route employed during emissions measurement has a strong influence on the reported results. A driving cycle that demands less energy per unit distance than others results in higher fuel economy and lower distance-specific oxides of nitrogen emissions. In addition to energy per unit distance, the degree to which the driving cycle is transient in nature can also affect emissions.