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Repeatable measurement of real-world heavy-duty diesel truck emissions requires the use of a chassis dynamometer with a test schedule that reasonably represents actual truck use. A new Heavy Heavy-Duty Diesel Truck (HHDDT) schedule has been created that consists of four modes, termed Idle, Creep, Transient and Cruise. The effect of driving style on emissions from the Transient Mode was studied by driving a 400 hp Mack tractor at 56,000 lbs. test weight in fashions termed “Medium”, “Good”, “Bad”, “Casual” and “Aggressive”. Although there were noticeable differences in the actual speed vs. time trace for these five styles, emissions of the important species oxides of nitrogen (NOx) and particulate matter (PM), varied little with a coefficient of variation (COV) of 5.13% on NOX and 10.68% on PM. Typical NOx values for the HHDDT Transient mode ranged from 19.9 g/mile to 22.75 g/mile. The Transient mode which was the most difficult mode to drive, proved to be repeatable.

When heavy-duty truck emissions are expressed in distance-specific units (such as g/mile), the values may depend strongly on the nature of the test cycle or schedule. Prior studies have compared emissions gained using different schedules and have proposed techniques for translating emissions factor rates between schedules. This paper reviews emissions data from the 5-mode CARB HHDDT Schedule, UDDS Schedule, and a steady-state cycle (AC5080), with reference to each other. NOX and PM emissions are the two components of emissions which are reviewed. A heavy-duty chassis dynamometer was used for emissions characterization along with a full scale dilution tunnel. The vehicle test weights were simulated at 30,000 lbs, 56,000 lbs, and 66,000 lbs. For each vehicle, average data from one mode or cycle have been compared with average data for a different mode or cycle.

Continuously variable transmissions (CVTs) are usually used in small vehicles due to power limitations on the variable elements. Continuously variable power-split transmissions (CVPST) were developed in order to reduce the fraction of power passing through the variable elements [1,2]. The configuration presented in this paper includes a planetary gear train (PGT), which in combination with the CVT allows the power to be split and therefore increase the power envelope of the system. The PGT also provides a branch that can be used in a hybrid electric vehicle (HEV) operation through an electric motor. A conceptual design of a CVPST for a HEV is presented in this paper. The objectives are to show the different operational modes, with diagrams, perform a power analysis, develop the velocity and force equations and finally show the performance of the system with an example application.

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).

In an effort to develop engine/vehicle test methods that will reflect real-world emission characteristics, West Virginia University (WVU) designed and conducted a study on a Class-8 tractor with an electronically controlled diesel engine that was mounted on a chassis dynamometer in the Old Dominion University Langley full-scale wind tunnel. With wind speeds set at 88 km/hr in the tunnel, and the tractor operating at 88 km/hr on the chassis dynamometer, a Scanning Mobility Particle Sizer (SMPS) was employed for measuring PM size distributions and concentrations. The SMPS was housed in a container that was attached to a three-axis gantry in the wind tunnel. Background PM size-distributions were measured with another SMPS unit that was located upstream of the truck plume. Ambient temperatures were recorded at each of the sampling locations. The truck was also operated through transient tests with vehicle speeds varying from 65 to 88 km/hr, with a wind speed of 76 km/hr.

Concern over health effects associated with diesel exhaust and debate over the influence of high number counts of particles in diesel exhaust prompted research to develop a methodology for diesel particulate matter (PM) characterization. As part of this program, a tractor truck with an electronically managed diesel engine and a dynamometer were installed in the Old Dominion University (ODU) Langley full-scale wind tunnel. This arrangement permitted repeat measurements of diesel exhaust under realistic and reproducible conditions and permitted examination of the steady exhaust plume at multiple points. Background particle size distribution was characterized using a Scanning Mobility Particle Sizer (SMPS). In addition, a remote sampling system consisting of a SMPS, PM filter arrangement, and carbon dioxide (CO2) analyzer, was attached to a roving gantry allowing for exhaust plume sampling in a three dimensional grid. Raw exhaust CO2 levels and truck performance data were also measured.

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.

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.

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.

Exhaust emissions from off-highway diesel engines are a significant contributor of both oxides of nitrogen (NOx) and particulate matter (PM) to air inventories. Yet, emissions research activities aimed solely at the off-highway arena have been minimal - largely overshadowed by the extensive efforts directed toward the on-highway sector. However, with current trends indicating that the performance of these off-highway vehicles will become increasingly more scrutinized by federal regulatory agencies, augmentation of current research efforts will be necessary. The global objective for this study was to collect vehicle activity information for diesel-powered off-highway vehicles while they were operated in the field. Engine speed and raw exhaust CO2 concentrations were recorded and then used to create engine dynamometer test cycles. The engine was exercised according to these cycles in the laboratory so that the mass emissions rates of exhaust gas pollutants could be measured.

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.

Further growth of diesel engines in the light-duty and heavy-duty vehicular market is closely linked to the potential health risks of diesel exhaust. The California Air Resources Board and the Office of Environmental Health Hazard Assessment have identified diesel exhaust as a toxic air contaminant. The International Agency for Research on Cancer concluded that diesel particulate is a probable human carcinogen [1]. Cleaner burning liquid fuels, such as those derived from natural gas via the Fischer-Tropsch (FT) process, offer a potentially economically viable alternative to standard diesel fuel while providing reduced particulate emissions. Further understanding of FT operation may be realized by investigating the differences in toxicity and potential health effects between particulate matter(PM) derived from FT fuel and that derived from standard Federal diesel No. 2 (DF).

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.

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.

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.

The emissions reduction of Fischer-Tropsch (FT) diesel fuel has been demonstrated in several recent publications in both laboratory engine testing and in-use vehicle testing. Reduced emission levels have been attributed to several chemical and physical characteristics of the FT fuels including reduced density, ultra-low sulfur levels, low aromatic content and high cetane rating. Some of the effects of these attributes on the combustion characteristics in diesel engines have only recently been documented. In this study, a Ricardo Proteous, single-cylinder, 4-stroke DI engine is instrumented for in-cylinder pressure measurements. The engine was run at several steady engine states at multiple timing conditions using both federal low sulfur and natural gas derived FT fuels. The emissions and performance data for each fuel at each steady state operating conditions were compared.

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.