Semester

Spring

Date of Graduation

2013

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

W. Scott Wayne

Committee Co-Chair

Nigel N. Clark

Committee Member

Hailin Li

Committee Member

Mario G. Perhinschi

Committee Member

John W. Zondlo

Abstract

The primary objective of this study was to investigate emissions and fuel economy, and develop an Integrated Bus Information System (IBIS) for the Federal Transit Administration (FTA). IBIS included the development of transit fleet emissions models to assist transit agencies in evaluating the emissions implications of new transit vehicle procurements. Compared with existing models, the IBIS prediction model was intended to be less complicated but have sufficient accuracy to achieve its task as a vehicle procurement analysis tool.;Fuel economy (FE) and distance specific emissions (g/mile) were evaluated and predicted by the IBIS model, including carbon monoxide (CO), carbon dioxide (CO2), oxides of nitrogen (NOx), hydrocarbons (HC), and particulate matter (PM). Most data used in this study were based on chassis dynamometer testing conducted by West Virginia University (WVU), considering that chassis dynamometer test cycles could reflect the actual vehicle operations.;Many factors affect emissions and fuel economy, including vehicle parameters, fuel type, engine parameters, road conditions, ambient conditions and driving characteristics. Since driving characteristics significantly affected emissions and fuel economy, to determine the model inputs, correlation and regression studies between distance specific emissions, fuel economy and driving characteristics were performed. Results showed that average speed with idle (or average speed), percentage idle, stops per mile, standard deviation of vehicle speed, and kinetic intensity were the most influential parameters in driving characteristics and should be considered as the main driving parameters for the development of the predictive fleet emissions model.;A micro-trip based method was used throughout this research. A genetic algorithm (GA) was implemented to generate numerous new virtual cycles, to expand the cycle and emission database and to investigate transit operation characteristics encountered in the real-world. Then, the cycle generation method was applied to multiple representative buses tested with different types of fuel and powertrain technologies, to acquire the emissions and fuel economy data on over 350 newly generated virtual cycles. In addition, emissions testing was conducted over selected virtual cycles and validated the cycle generation method. It suggested that fuel consumption, CO2 and NOx emissions were not sensitive to microtrip history (sequence).;Based on this expanded dataset, multiple predictive backbone models were developed in certain model year (MY) groups for different fuel or propulsion system types (conventional and hybrid). The backbone models were validated with an additional dataset. For example, in terms of average percent errors, if using three cycle parameters as IBIS model inputs, emissions and FE of a MY 2008 60-foot CNG bus were predicted within 6% for FE, 6-8% for CO 2, 16-18% for CO, and 22-29% for HC. Emissions and FE of a MY 2008 40-foot hybrid bus were predicted within 7% for FE, 8-10% for CO2, and 7-17% for NOx. Multiple correction factors were developed to improve the models by introducing additional non-cycle parameters including vehicle weight, MY groups, and after-treatment technologies.;A case study compared the IBIS model with the Emission FACtors (EMFAC) model developed by California Air Resources Board (CARB). Comparison results agreed well for CO, NOx and PM for MY 2000 diesel buses and agreed well for CO for MY 2006 diesel buses. On average the IBIS model agreed well with the EMFAC model in terms of CO2 and fuel economy. In addition, both models showed that emissions and fuel economy did not change as the vehicle aged.

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