Date of Graduation
2014
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Mechanical and Aerospace Engineering
Committee Chair
Arvind Thiruvengadam
Committee Co-Chair
Hailin Li
Committee Member
Radhey Sharma
Abstract
Characterization of fuel consumption is of critical importance for framing or modifying federal regulations for trucking industry. Due to its complexity, fuel consumption is often only known for a few test cycles which generally represent limited types of vehicle activity. It is known that vehicle fuel consumption strongly depends on the vehicle activity, chassis design and engine model year (MY), and hence poses a significant challenge while predicting fuel consumption of heavy-duty vehicles over real-world vehicle activity.;Upcoming Greenhouse Gas (GHG) regulation for 2017, engine manufacturers are required to assess heavy-duty engine fuel economy using vehicle simulation tools. With recent focus on fuel economy and GHG emissions, regulatory agencies are progressively relying on vehicle simulation tools that allow prediction of the fuel consumption for a variety of vehicles over different test cycles.;Autonomie simulation tool developed by Argonne National Laboratory was used in this study to predict the fuel consumption over different cycles and then the prediction of simulation tool was compared with chassis and engine dynamometer data to check the accuracy of the simulation tool.;Autonomie simulation results were compared with the chassis dynamometer test data and the results showed a 5.93% and 11.53% difference in engine work and brake-specific fuel consumption (bsfc) respectively. When Autonomie simulation results were compared with engine dynamometer test data, the difference in work done, integrated fuel consumption and bsfc were found to be 13.21%, 4.92%, and 8.32% respectively.;Autonomie generated fuel consumption simulation data was compared with a dynamic vehicle simulator, Greenhouse Gas Emissions Model (GEM). The method was able to predict ARB transient cycle within 10% error, with an absolute error of 6.38%.
Recommended Citation
Rana, Nitin, "Characterization of heavy duty engine fuel maps used for model based simulation tools" (2014). Graduate Theses, Dissertations, and Problem Reports. 6472.
https://researchrepository.wvu.edu/etd/6472