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In-service 1994-2003 heavy-duty trucks were acquired by West Virginia University (WVU), equipped with the WVU Mobile Emissions Measurement System (MEMS) to measure on-road NOx, and driven on road routes near Sabraton, West Virginia, and extending up to Washington, PA to obtain real-world oxides of nitrogen (NOx) emissions data on highways and local roads. The MEMS measured 5Hz NOx, and load was obtained from the electronic control unit. Trucks were loaded to about 95% of their gross vehicle weights. Emissions in g/mi and g/bhp-hr were computed over the various road routes. In addition, some of the trucks were tested 1 to 2 years later to determine emission changes that may have occurred for these trucks. Emission results varied significantly over the different road routes due to different speeds, driving patterns, and road grades.

Heavy-duty diesel engines (HDDE), because of their widespread use and reputation of expelling excessive soot, have frequently been held responsible for excessive amounts of overall environmental particulate matter (PM). PM is a considerable contributor to air pollution, and a subject of primary concern to health and regulatory agencies worldwide. The U.S. Environmental Protection Agency (EPA) has provided PM emissions regulations and standards of measurement techniques since the 1980's. PM standards set forth by the EPA for HDDEs are based only on total mass, instead of size and/or concentration. The European Union adopted a particle number emission limit, and it may influence the U.S. EPA to adopt particle number or size limits in the future. The purpose of this research was to study the effects biodiesel blended fuel and cetane improvers have on particle size and number.

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 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.

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.

The historical lack of continuous data for PM emissions from heavy-duty diesel engines hampers advanced inventory approaches and hampers second-by-second engine control optimization. Continuos PM data can be obtained using a Tapered Element Oscillating Microbalance (TEOM), but moisture correction of data is needed to remove unwanted transient components of the mass. Reasonable correlation can be found between TEOM data integrated over the cycle and conventional PM filter data. Considerable scatter was evident when continuous TEOM data were plotted against instantaneous power, but by dispersing the power in time a clearer relationship was evident. Continuous TEOM data showed the same gross trends as PM filter mass distributed over a cycle in proportion to instantaneous CO, but it was evident that this CO proportioning technique is at best approximate. Binning of PM mass rate as a function of vehicle speed and acceleration were also evaluated for inventory purposes.

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.

A recently completed program was developed to evaluate ultra-low sulfur diesel fuels and passive diesel particle filters (DPF) in several different truck and bus fleets operating in Southern California. The primary test fuels, ECD and ECD-1, are produced by ARCO, a BP company, and have less than 15 ppm sulfur content. A test fleet comprised of heavy-duty trucks and buses were retrofitted with one of two types of catalyzed diesel particle filters, and operated for one year. As part of this program, a chemical characterization study was performed in the spring of 2001 to compare the exhaust emissions using the test fuels with and without aftertreatment. A detailed speciation of volatile organic hydrocarbons (VOC), polycyclic aromatic hydrocarbons (PAH), nitro-PAH, carbonyls, polychlorodibenzo-p-dioxins (PCDD) and polychlorodibenzo-p-furans (PCDF), inorganic ions, elements, PM10, and PM2.5 in diesel exhaust was performed for a select set of vehicles.

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).

Few real-world data exist to describe the contribution of diesel vehicles to the emissions inventory, although it is widely acknowledged that diesel vehicles are a significant contributor to oxides of nitrogen (NOx) and particulate matter (PM) in Southern California. New data were acquired during the Gasoline/Diesel PM Split Study, designed to collect emissions data for source profiling of PM emissions from diesel- and gasoline-powered engines in the South Coast (Los Angeles) Air Basin in 2001. Regulated gases, PM and carbon dioxide (CO2) were measured from 34 diesel vehicles operating in the Southern California area. Two were transit buses, 16 were trucks over 33,000 lbs. in weight, 8 were 14,001 lbs. to 33,000 lbs. in weight and 8 were under 14,001 lbs. in weight. The vehicles were also grouped by model year for recruiting and data analysis.

Reactivity controlled compression ignition (RCCI) is a form of dual-fuel combustion that exploits the reactivity difference between two fuels to control combustion phasing. This combustion approach limits the formation of oxides of nitrogen (NOX) and soot while retaining high thermal efficiency. The research presented herein was performed to determine the influences that high reactivity (diesel) fuel properties have on RCCI combustion characteristics, exhaust emissions, fuel efficiency, and the operable load range. A 4-cylinder, 1.9 liter, light-duty compression-ignition (CI) engine was converted to run on diesel fuel (high reactivity fuel) and compressed natural gas (CNG) (low reactivity fuel). The engine was operated at 2100 revolutions per minute (RPM), and at two different loads, 3.6 bar brake mean effective pressure (BMEP) and 6 bar BMEP.

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.

The Rand Cam engine is a novel design which avoids the use of pistons in favor of a cavity of varying size and shape. A set of vanes protrudes from a rotor into a circular trough in a stator. The vanes seal to the walls and base of the trough, which is of varying depth, and progress around the trough with rotation of the rotor. These vanes therefore pass through the rotor and are constrained to move parallel to the rotational axis. Intake and exhaust processes occur through ports in the stator wall which are revealed by the passing vanes. Advantages of the basic design include an absence of valves, reduction in reciprocating masses, presence of an integral flywheel in the rotor and strong fluid movement akin a swirl induced by the relative velocity between the rotor and stator.

The Rand-Cam engine is a positive displacement machine, operating on a four stroke cycle, which consists of a rotor with multiple axial vanes forming combustion chambers as the rotor and vanes rotate in a cam shaped housing. The cam housing, consisting of two “half-housings” or stators, contains a toroidal trough of varying depth machined into each stator. The two stators are phased so that the shallowest point on one trough corresponds to the deepest on the other. A set of six vanes, able to move axially through machined holes in the rotor, traverses the troughs creating six captured zones per side. These zones vary in volume with rotor rotation. Since each trough has two deep sections and two shallow sections with ramps in between, full four stroke operation is obtained between each pair of vanes in each trough, corresponding to twelve power “strokes” per revolution.

On-board emissions measurement for heavy-duty vehicles has taken on greater significance because new standards now address in-use emissions levels in the USA. Emissions compliance must be shown in a “Not-to-exceed” (NTE) zone that excludes engine operation at low power. An over-the-road 1996 Peterbilt tractor was instrumented with the West Virginia University Mobile Emissions Measurement System (MEMS). The researchers determined how often the truck entered the NTE, and the emissions from the vehicle, as it was driven over different routes and at different test weights (20,740 lb, 34,640 lb, 61,520 lb, and 79,700 lb) The MEMS interfaced with the truck ECU, while also measuring exhaust flowrate, and concentrations of carbon dioxide (CO2) and oxides of nitrogen (NOx) in the exhaust. The four test routes that were employed included varying terrain types in order to simulate a wide range of on-road driving conditions. One route (called the Bruceton route) included a sustained hill climb.