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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.

Research at West Virginia University has led to the development of a novel crankless reciprocating internal combustion engine. This paper presents a time-based model used to investigate the performance of two-stroke direct injection compression ignition linear engines. The two-stroke linear engine consists of two pistons, linked by a connecting rod, that are allowed to move freely in response to changes in the engine's fueling and load across the full operating cycle of the engine. The computer model uses a combination of a series of dynamic and thermodynamic numerical equations, which have been solved to provide a detailed analysis of the two-stroke direct injection linear engine operation. Parameters such as rate of combustion, convection heat transferred inside the cylinders, friction forces, external loads, acceleration, velocity profile, compression ratio, and in-cylinder pressures were modeled.

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.

In characterizing the emissions from mobile sources, it is essential that the vehicle be exercised in a way that reasonably represents typical in-use behavior. A heavy-heavy duty diesel truck (HHDDT) test schedule was developed from speed-time data gathered during two Air Resources Board-sponsored truck activity programs. The data were divided into four modes, termed Idle, Creep, Transient and Cruise Modes, in order of increasing speed. For the last three modes, speed-time schedules were created that represented all the data in that mode. Statistical parameters such as average speed, stops per unit distance, kinetic energy, maximum speed and acceleration and deceleration values were considered in arriving at these schedules. The schedules were evaluated using two Class 8 over-the-road tractors on a chassis dynamometer. Emissions were measured using a full-scale dilution tunnel, filtration for particulate matter (PM), and research grade analyzers for the gases.

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.

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 goal of the Diesel Emissions Control-Sulfur Effects (DECSE) program was to determine the impact of diesel fuel sulfur levels on emissions control devices that could lower emissions of oxides of nitrogen (NOX) and particulate matter (PM) from on-highway trucks and buses. West Virginia University (WVU) performed evaluations of lean-NOx catalysts to determine the effects of fuel sulfur content on emissions reduction efficiency and catalyst durability in the first 250 hours of operation. A Cummins ISM370 engine (10.8 liter, 370 horsepower), typical of heavy -duty truck applications, was utilized to evaluate high-temperature lean-NOX catalyst while a Navistar T444E (7.3 liter, 210 horsepower), typical of medium-duty applications, was used to evaluate low-temperature catalyst. Catalysts were evaluated periodically during the first 250 hours of exposure to exhaust from engines operated on 3ppm, 30ppm, 150ppm and 350ppm sulfur content diesel fuel.

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.

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.

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).

Increased concerns about the emissions produced from mobile sources have placed an emphasis on the in-use monitoring of on- and off-road vehicles. Measuring the emissions emitted from an in-use vehicle during its operation provides for a rich dataset that is generally too expensive and too time consuming to reproduce in a laboratory setting. Many portable systems have been developed and implemented in the past to acquire in-use emissions data for spark ignited and compression ignited engines. However, the majority of these systems only measured the concentration levels of the exhaust constituents and or reported the results in time-specific (g/s) and or distance-specific (g/km) mass units through knowledge of the exhaust flow. For heavy-duty engines, it is desirable to report the in-use emission levels in brake-specific mass units (g/kW-hr) since that is how the emission levels are reported from engine dynamometer certification testing.

West Virginia University redesigned a 2002 Ford Explorer and created a diesel electric hybrid vehicle to satisfy the goals of the 2002 FutureTruck competition. These goals were to demonstrate a 25% improvement in fuel economy, to reduce greenhouse gas emissions, to achieve California ULEV emissions, to demonstrate 1/8-mile acceleration of 11.5 seconds or less, and to maintain vehicular comforts and performance. West Virginia University's 2002 hybrid sport utility vehicle (SUV), the Exclaim!, meets or exceeds these goals. Using a post-transmission parallel configuration, WVU integrated a 2.5L Detroit Diesel Corporation engine along with a Unique Mobility 75kW electric motor to replace the stock drivetrain. With an emphasis on maintaining performance, WVU strived to improve areas where SUVs have traditionally performed poorly: fuel economy and emissions. Using regenerative braking, fuel economy has been significantly improved.

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.

The recent oil crisis has once again emphasized the need to develop both fuel efficient engines and alternately fueled engines, particularly for automotive applications. Engines which burn coal or coal pyrolysis products are attractive, but ignition delay and metal erosion problems continue to limit high speed operation of such engines. Further, the throttled spark ignition engine often used with methanol and natural gas does not prove an efficient or tolerant device for the combustion of a wide range of fuel. Therefore, an novel approach must be taken in order to achieve the efficient and flexible operation of such an engine. A novel design of a fuel tolerant engine suitable for burning coal fuels separates the combustion from the piston in order to have more careful flame control and to exclude the particulate matter from the engine's piston rings.

The 1998 Consent Decrees between the United States Government and the settling heavy-duty diesel engine manufacturers require in-use emissions testing from post 2000 model year engines. The emissions gathered from these engines must be reported on a brake-specific mass basis. To report brake-specific mass emissions, three primary parameters must be measured. These are the concentration of each emission constituent, the exhaust mass flow rate, and the engine power output. The measurement of the concentration level and exhaust mass flow rate can be (and are generally) measured directly with instrumentation installed in the exhaust transfer tube. However, engine power cannot be measured directly for in-use emissions testing due to the direct coupling of the engine output shaft to the vehicle's transmission. Engine power can be inferred from the electronic control unit (ECU) broadcast of engine speed and engine torque.

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.