scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Effect on emissions of multiple driving test schedules performed on two heavy-duty vehicles

16 Oct 2000-SAE transactions (Society of Automotive Engineers)-Vol. 109, Iss: 4, pp 2387-2397
About: This article is published in SAE transactions.The article was published on 2000-10-16. It has received 18 citations till now. The article focuses on the topics: Driving test.
Citations
More filters
Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

133 citations

Journal ArticleDOI
TL;DR: Different driving cycles for the hybrid electric bus and varying weights of the conventional truck may affect emissions during different driving cycles and underpredicted emissions of CO2 and NOx in the case of a class-8 truck but were more accurate as the truck weight increased.
Abstract: 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.

24 citations

References
More filters
Journal ArticleDOI
01 Jun 1999
TL;DR: In this paper, the authors developed a city/suburban heavy vehicle route (referred to as the CSHVR) for Class 7 (11 794-14969 kg gross vehicle weight) and Class 8 (14969-36287 kg) delivery trucks.
Abstract: There is presently a lack of realistic driving cycles or schedules for the chassis dynamometer emissions testing of heavy-duty trucks. This research effort was motivated by the need for representative emissions measurement techniques to compare alternatively fuelled trucks with their diesel or gasoline counterparts operating in heavy-duty truck applications. Speed versus time and video recording data gathered from trucks in local delivery use were used to develop a city/suburban heavy vehicle route (referred to as the CSHVR) for Class 7 (11 794-14969 kg gross vehicle weight) and Class 8 (14969-36287 kg) delivery trucks. Statistical data were gathered on actual truck driving behaviour, as well as 60 h of actual speed versus time driving information. A cycle was then developed by joining microtrips from the actual truck operation and verifying that it was representative of the whole database. A driving route was derived from this cycle with the help of the video data, and with the vehicle decelerati...

43 citations