Particulate matter: A strategic vision for transportation-related research.
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Carbon Monoxide↗
Biomedical subjects
Nigel N Clark
Publications and source records attributed to Nigel N Clark.
Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles.
With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.
Air Pollutants↗
Idle emissions from heavy-duty diesel vehicles: review and recent data.
Heavy-duty diesel vehicle idling consumes fuel and reduces atmospheric quality, but its restriction cannot simply be proscribed, because cab heat or air-conditioning provides essential driver comfort. A comprehensive tailpipe emissions database to describe idling impacts is not yet available. This paper presents a substantial data set that incorporates results from the West Virginia University transient engine test cell, the E-55/59 Study and the Gasoline/Diesel PM Split Study. It covered 75 heavy-duty diesel engines and trucks, which were divided into two groups: vehicles with mechanical fuel injection (MFI) and vehicles with electronic fuel injection (EFI). Idle emissions of CO, hydrocarbon (HC), oxides of nitrogen (NOx), particulate matter (PM), and carbon dioxide (CO2) have been reported. Idle CO2 emissions allowed the projection of fuel consumption during idling. Test-to-test variations were observed for repeat idle tests on the same vehicle because of measurement variation, accessory loads, and ambient conditions. Vehicles fitted with EFI, on average, emitted approximately 20 g/hr of CO, 6 g/hr of HC, 86 g/hr of NOx, 1 g/hr of PM, and 4636 g/hr of CO2 during idle. MFI equipped vehicles emitted approximately 35 g/hr of CO, 23 g/hr of HC, 48 g/hr of NOx, 4 g/hr of PM, and 4484 g/hr of CO2, on average, during idle. Vehicles with EFI emitted less idle CO, HC, and PM, which could be attributed to the efficient combustion and superior fuel atomization in EFI systems. Idle NOx, however, increased with EFI, which corresponds with the advancing of timing to improve idle combustion. Fuel injection management did not have any effect on CO2 and, hence, fuel consumption. Use of air conditioning without increasing engine speed increased idle CO2, NOx, PM, HC, and fuel consumption by 25% on average. When the engine speed was elevated from 600 to 1100 revolutions per minute, CO2 and NOx emissions and fuel consumption increased by >150%, whereas PM and HC emissions increased by approximately 100% and 70%, respectively. Six Detroit Diesel Corp. (DDC) Series 60 engines in engine test cell were found to emit less CO, NOx, and PM emissions and consumed fuel at only 75% of the level found in the chassis dynamometer data. This is because fan and compressor loads were absent in the engine test cell.
Air Pollutants↗
Effect of truck operating weight on heavy-duty diesel emissions.
Heavy-duty diesel vehicles are substantial contributors of oxides of nitrogen (NO(x)) and particulate matter (PM) while carbon monoxide and hydrocarbon (HC) emissions from diesel vehicles receive less attention. Truck emissions inventories have traditionally employed average fuel economy and engine efficiency factors to translate certification into distance-specific (g/mi) data, so that inventories do not take into account the real effects of truck operating weight on emissions. The objective of this research was to examine weight corrections for class 7 and 8 vehicles (over 26 000 lb (11 793 kg) gross vehicle weight) from a theoretical point of view and to present a collection of original data on the topic. It was found by combining an empirical equation with theoretical truck loads that the NO(x) emissions increased by approximately 54% for a doubling of test weight. Emissions data were gathered from specific tests performed using different test weights and using various test schedules, which can consist of cycles or routes. It was found experimentally that NO(x) emissions have a nearly linear correlation with vehicle weight and did not vary much from vehicle to vehicle. NO(x) emissions were also found to be insensitive to transient operation in the test schedule. The observed trends correlate well with the theory presented, and hence, the NO(x) emissions can be predicted reasonably accurately using the theory. If NO(x) data were considered in fuel-specific (g/gal) units, they did not vary with the test weight. HC emissions were found to be insensitive to the vehicle weight. CO and PM emissions were found to be a strong function of weight during transient operation. Under transient operation, the CO emissions value increased by 36% for an increase in test weight from 42 000 (19 051 kg) to 56 000 lb (25 401 kg). However, CO and PM were found to be insensitive to the vehicle weight during nearly steady-state operation.
Carbon Monoxide↗
A predictive tool for emissions from heavy-duty diesel vehicles.
Traditional emissions inventories for trucks and buses have relied on diesel engine emissions certification data, in units of g/bhp-hr, processed to yield a value in g/mile without a detailed accounting of the vehicle activity. Research has revealed a variety of other options for inventory prediction, including the use of emissions factors based upon instantaneous engine power and instantaneous vehicle behavior. The objective of this paper is to provide tabular factors for use with vehicle activity information to describe the instantaneous emissions from each heavy-duty vehicle considered. To produce these tables, a large body of data was obtained from the research efforts of the West Virginia University-Transportable Heavy Duty Emissions Testing Laboratories (TransLabs). These data were available as continuous records of vehicle speed (hence also acceleration), vehicle power, and emissions of carbon monoxide (CO), oxides of nitrogen (NOx), and hydrocarbons (HC). Data for particulate matter (PM) were available only as a composite value for a whole vehicle test cycle, but using a best effort approach, the PM was distributed in time in proportion to the CO. Emissions values, in g/sec, were binned according to the speed and acceleration of a vehicle, and it was shown that the emissions could be predicted with reasonable accuracy by applying this table to the original speed and acceleration data. The test cycle used was found to have a significant effect on the emissions value predicted. Tables were created for vehicles grouped by type (large transit buses, small transit buses, and tractor-trailers) and by range of model year. These model year ranges were bounded by U.S. national changes in emissions standards. The result is that a suite of tables is available for application to emissions predictions for trucks and buses with known activity, or as modeled by TRANSIMS, a vehicle activity simulation model from Los Alamos National Laboratories.
Air Pollutants↗
Factors affecting heavy-duty diesel vehicle emissions.
Societal and governmental pressures to reduce diesel exhaust emissions are reflected in the existing and projected future heavy-duty certification standards of these emissions. Various factors affect the amount of emissions produced by a heterogeneous charge diesel engine in any given situation, but these are poorly quantified in the existing literature. The parameters that most heavily affect the emissions from compression ignition engine-powered vehicles include vehicle class and weight, driving cycle, vehicle vocation, fuel type, engine exhaust aftertreatment, vehicle age, and the terrain traveled. In addition, engine control effects (such as injection timing strategies) on measured emissions can be significant. Knowing the effect of each aspect of engine and vehicle operation on the emissions from diesel engines is useful in determining methods for reducing these emissions and in assessing the need for improvement in inventory models. The effects of each of these aspects have been quantified in this paper to provide an estimate of the impact each one has on the emissions of diesel engines.
Air Pollutants↗