Search Results

This paper examines the energy pathways of a 29cc air-cooled two-stroke engine operating on natural gas with different exhaust geometries. The engine was operated at wide-open-throttle at a constant speed of 5400 RPM with ignition adjusted to yield maximum brake torque while the fueling was adjusted to examine both rich and lean combustion. The exhaust configurations examined included an off-the-shelf (OTS) model and two other custom models designed on Helmholtz resonance theory. The custom designs included both single and multi-cone features. Out of the three exhaust systems tested, the model with maximum trapping efficiency showed a higher overall efficiency due to lower fuel short-circuiting and heat transfer. The heat transfer rate was shown to be 10% lower on the new designs relative to OTS model.

This paper reviews fuel-saving technologies for commercial trailers, provides an overview of the trailer market in the U.S., and explores options for policy measures at the federal level that can promote the development and deployment of trailers with improved efficiency. For trailer aerodynamics, there are many technologies that exist and are in development to target each of the three primary areas where drag occurs: 1) the tractor-trailer gap, 2) the side and underbody of the trailer, and 3) the rear end of the trailer. In addition, there are tire technologies and weight reduction opportunities for trailers, which can lead to reduced rolling resistance and inertial loss. As with the commercial vehicle sector, the trailer market is diverse, and there are a variety of sizes and configurations that are employed to meet a wide range of freight demands.

Heavy duty tractor-trailers under freeway operations consume about 65% of the total engine shaft energy to overcome aerodynamic drag force. Vehicles are exposed to on-road crosswinds which cause change in pressure distribution with a relative wind speed and yaw angle. The objective of this study was to analyze the drag losses as a function of on-road wind conditions, on-road vehicle position and trajectory. Using coefficient of drag (CD) data available from a study conducted at NASA Ames, Geographical Information Systems model, time-varying weather data and road data, a generic model was built to identify the yaw angles and the relative magnitude of wind speed on a given route over a given time period. A region-based analysis was conducted for a study on interstate trucking operation by employing I-79 running through West Virginia as a case study by initiating a run starting at 12am, 03/03/2012 out to 12am, 03/05/2012.

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.

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.

Heavy-duty trucks are an important sector to evaluate when seeking fuel consumption savings and emissions reductions. With fuel costs on the rise and emissions regulations becoming stringent, vehicle manufacturers find themselves spending large amounts of capital improving their products in order to be compliant with regulations. The Powertrain System Analysis Toolkits (PSAT), developed by the Argonne National Laboratory (ANL), is a simulation tool that helps mitigate costs associated with research and automotive system design. While PSAT has been widely used to predict the fuel consumption and exhaust emissions of conventional and hybrid light-duty vehicles, it also may be employed to test heavy-duty vehicles. The intent of this study was to develop an accurate model that predicts emissions and fuel economy for heavy-duty vehicles for use within PSAT.

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.

Yard hostlers are tractors (switchers) used to move containers at ports and storage facilities. While many speed-time driving cycles for assessing emissions and performance from heavy-duty vehicles exist, a driving cycle representative of yard hostler activity at Port of Long Beach, CA was not available. Activity data were collected from in-use yard hostlers as they performed ship loading/unloading, rail loading/unloading and other yard routines, primarily moving containers on trailers or carts. The activity data were then used to develop four speed-time driving cycles with durations of 1200 seconds, representing light and heavy ship activities and light and heavy load rail activities. These cycles were constructed using actual speed-time data collected during activity logging and the cycles created were statistically comparable to each subset of activity data.

Transit agencies across the United States operate bus fleets primarily powered by diesel, natural gas, and hybrid drive systems. Passenger loading affects the power demanded from the engine, which in turn affects distance-specific emissions and fuel consumption. Analysis shows that the nature of bus activity, taking into account the idle time, tire rolling resistance, wind drag, and acceleration energy, influences the way in which passenger load impacts emissions. Emissions performance and fuel consumption from diesel and natural gas powered buses were characterized by the West Virginia University (WVU) Transportable Emissions Testing Laboratory. A comparison matrix for all three bus technologies included three common driving cycles (the Braunschweig Cycle, the OCTA Cycle, and the ADEME-RATP Paris Cycle). Each bus was tested at three different passenger loading conditions (empty weight, half weight, and full weight).

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.

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.

It is difficult to project the emissions performance of a vehicle on a route unless the test cycle used to gain the emissions data reasonably represents that route. A chassis dynamometer emissions measurement test schedule consisting of three modes (congested, non-congested and bus rapid transit (BRT) operation) was developed for use in a program to evaluate transit bus technologies in Mexico City. Existing buses were fitted with global positioning system (GPS) data loggers and, between September 2nd and 8th of 2004, 54 hours of speed-time data were collected while the buses were operated over several bus routes in Mexico City. The data set was then broken down into individual micro-trips, each consisting of an idle period followed by the bus traveling some distance, followed by a final deceleration to idle.

Six 2001 International Class 6 trucks participated in a project to determine the impact of gas-to-liquid (GTL) fuel and catalyzed diesel particle filters (DPFs) on emissions and operations from December 2003 through August 2004. The vehicles operated in Southern California and were nominally identical. Three vehicles operated “as-is” on California Air Resources Board (CARB) specification diesel fuel and no emission control devices. Three vehicles were retrofit with Johnson Matthey CCRT® (Catalyzed Continuously Regenerating Technology) filters and fueled with Shell GTL Fuel. Two rounds of emissions tests were conducted on a chassis dynamometer over the City Suburban Heavy Vehicle Route (CSHVR) and the New York City Bus (NYCB) cycle. The CARB-fueled vehicles served as the baseline, while the GTL-fueled vehicles were tested with and without the CCRT filters. Results from the first round of testing have been reported previously (see 2004-01-2959).

When heavy-duty truck emissions rates are expressed in distance-specific units (such as g/mile), average speed and the degree of transient behavior of the vehicle activity can affect the emissions rate. Previous one-dimensional studies have shown some correlation of distance-specific emissions rates between cycles. This paper reviews emissions data sets from the 5-mode CARB Heavy Heavy-Duty Diesel Truck (HHDDT) Schedule, the Heavy Duty Urban Dynamometer Driving Schedule (UDDS) and an inspection and maintenance cycle, known as the AC5080. A heavy-duty chassis dynamometer was used for emissions characterization along with a full-scale dilution tunnel. The vehicle test weights were simulated at 56,000 lbs. Two-dimensional correlations were used to predict the emissions rate on one mode or cycle from the rates of two other modes or cycles.

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.

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).

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).

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.