Measurement and modeling of the activity, energy, and emissions of conventional and alternative vehicles.

摘要

Since the transportation sector is a significant contributor of air pollution, the capabilities of estimating fuel use and emissions for various vehicles is important to air quality studies as well as the development of environmental guidelines and policy recommendations. In this thesis, a common or similar modeling approach based on second-by-second data using portable emission measurement system (PEMS) was developed to estimate energy and emission estimation for a wide variety of on-road and non-road sources with conventional and alternative technology.;Based on vehicle-specific power (VSP) and speed-acceleration modal models, two correction factors were developed to estimate fuel consumption and emissions for vehicles which were driven with high and constant speed on highway. The corrected emission factors for NOx, HC, CO, and CO2 were significantly higher for high speeds and lower for low speeds than base emission factors estimated using MOBILE6 which is based on transient test cycles with durations on the order of 10 minutes.;A similar methodology was used to estimate energy use and emissions for a plug-in hybrid diesel-electric school bus (PHSB) and conventional diesel school bus (CDSB) for typical school bus routes in NC. To quantify the reduction of fuel use and emissions between PHSB and CDSB for same driving routes, the mixed-modal models based on manifold absolute pressure and VSP versus emissions were developed. Plug-in hybrid technology showed significant emission reductions for stop-and-go driving pattern. These results could provide a support for transportation and air quality management.;This thesis also introduces a simplified emission estimation methodology for locomotives based on rail-yard measurements using PEMS. This alternative measurement method is faster and cheaper than a federal reference method (FRM). The fuel-based emission rates based on PEMS measurement were comparable to FRM. It should serve as a useful basis of comparison to data in future measurement campaigns.