Keywords: vehicle emissions, emissions modeling, road grade, response time, emissions measurements.

The main objectives of this work are to quantify and compare intra- and inter-vehicle variability in fuel use and emissions and to develop capabilities of measuring and estimating fuel use and emissions at the micro-scale. This dissertation developed methodology to achieve the objectives, including experimental design for on-road data collection using a portable emission measurement system (PEMS), road grade estimation, evaluation of measurement accuracy, quantification of intra- and inter-vehicle variability in emissions, and micro-scale emissions modeling. A Light Detection and Ranging (LIDAR)-based method for road grade estimation was shown to be accurate and reliable. Measurement accuracy on a trip or mode basis was shown to be adequate. Routes, drivers, road grade, and time of day are significant sources of intra-vehicle variability. Significant inter-vehicle variability in emissions was observed, although only a small number of vehicles were tested and all belong to the same vehicle class. Thus, for accurate emission inventory development, both intra- and inter-vehicle variability should be taken into account. Consecutive averages were used for micro-scale emissions modeling to account for the response time of the PEMS. Choice of averaging time determines the model spatial and temporal resolution of prediction. Models for all pollutants are generally accurate, and precise in fuel use and CO2 emission estimation and moderately precise for other pollutants for various averaging times. Furthermore, models are capable of capturing the micro-scale events in emissions. Thus, the modeling schemes developed here can be used for a variety of applications including identification of the hotspots in emissions, transportation improvement programs on a corridor or intersection level, and more representative and accurate regional emission inventories development.

"This book is an introduction to automotive technology, with specic reference to battery electric, hybrid electric, and fuel cell electric vehicles. It could serve electrical engineers who need to know more about automobiles or automotive engineers who need to know about electrical propulsion systems. For example, this reviewer, who is a specialist in electric machinery, could use this book to better understand the automobiles for which the reviewer is designing electric drive motors. An automotive engineer, on the other hand, might use it to better understand the nature of motors and electric storage systems for application in automobiles, trucks or motorcycles. The early chapters of the book are accessible to technically literate people who need to know something about cars. While the rst chapter is historical in nature, the second chapter is a good introduction to automobiles, including dynamics of propulsion and braking. The third chapter discusses, in some detail, spark ignition and compression ignition (Diesel) engines. The fourth chapter discusses the nature of transmission systems.” —James Kirtley, Massachusetts Institute of Technology, USA “The third edition covers extensive topics in modern electric, hybrid electric, and fuel cell vehicles, in which the profound knowledge, mathematical modeling, simulations, and control are clearly presented. Featured with design of various vehicle drivetrains, as well as a multi-objective optimization software, it is an estimable work to meet the needs of automotive industry.” —Haiyan Henry Zhang, Purdue University, USA “The extensive combined experience of the authors have produced an extensive volume covering a broad range but detailed topics on the principles, design and architectures of Modern Electric, Hybrid Electric, and Fuel Cell Vehicles in a well-structured, clear and concise manner. The volume offers a complete overview of technologies, their selection, integration & control, as well as an interesting Technical Overview of the Toyota Prius. The technical chapters are complemented with example problems and user guides to assist the reader in practical calculations through the use of common scientic computing packages. It will be of interest mainly to research postgraduates working in this eld as well as established academic researchers, industrial R&D engineers and allied professionals.” —Christopher Donaghy-Sparg, Durham University, United Kingdom The book deals with the fundamentals, theoretical bases, and design methodologies of conventional internal combustion engine (ICE) vehicles, electric vehicles (EVs), hybrid electric vehicles (HEVs), and fuel cell vehicles (FCVs). The design methodology is described in mathematical terms, step-by-step, and the topics are approached from the overall drive train system, not just individual components. Furthermore, in explaining the design methodology of each drive train, design examples are presented with simulation results. All the chapters have been updated, and two new chapters on Mild Hybrids and Optimal Sizing and Dimensioning and Control are also included • Chapters updated throughout the text. • New homework problems, solutions, and examples. • Includes two new chapters. • Features accompanying MATLABTM software.

Traffic congestion increases travel times, but also results in higher energy usage and vehicular emissions. To evaluate the impact of traffic emissions on environment and human health, the accurate estimation of their rates and location is required. Traffic emission models can be used for estimating emissions, providing emission factors in grams per vehicle and kilometre. Emission factors are defined for specific traffic situations, and traffic data is necessary in order to determine these traffic situations along a traffic network. The required traffic data, which consists of average speed and flow, can be obtained either from traffic models or sensor measurements. In large urban areas, the collection of cross-sectional data from stationary sensors is a costefficient method of deriving traffic data for emission modelling. However, the traditional approaches of extrapolating this data in time and space may not accurately capture the variations of the traffic variables when congestion is high, affecting the emission estimation. Static transportation planning models, commonly used for the evaluation of infrastructure investments and policy changes, constitute an alternative efficient method of estimating the traffic data. Nevertheless, their static nature may result in an inaccurate estimation of dynamic traffic variables, such as the location of congestion, having a direct impact on emission estimation. Congestion is strongly correlated with increased emission rates, and since emissions have location specific effects, the location of congestion becomes a crucial aspect. Therefore, the derivation of traffic data for emission modelling usually relies on the simplified, traditional approaches. The aim of this thesis is to identify, quantify and finally reduce the potential errors that these traditional approaches introduce in an emission estimation analysis. According to our main findings, traditional approaches may be sufficient for analysing pollutants with global effects such as CO2, or for large-scale emission modelling applications such as emission inventories. However, for more temporally and spatially sensitive applications, such as dispersion and exposure modelling, a more detailed approach is needed. In case of cross-sectional measurements, we suggest and evaluate the use of a more detailed, but computationally more expensive, data extrapolation approach. Additionally, considering the inabilities of static models, we propose and evaluate the post-processing of their results, by applying quasi-dynamic network loading.

The Mobile Source Emissions Factor (MOBILE) model is a computer model developed by the U.S. Environmental Protection Agency (EPA) for estimating emissions from on-road motor vehicles. MOBILE is used in air-quality planning and regulation for estimating emissions of carbon monoxide (CO), volatile organic compounds (VOCs), and nitrogen oxides (NOx) and for predicting the effects of emissions-reduction programs. Because of its important role in air-quality management, the accuracy of MOBILE is critical. Possible consequences of inaccurately characterizing motor-vehicle emissions include the implementation of insufficient controls that endanger the environment and public health or the implementation of ineffective policies that impose excessive control costs. Billions of dollars per year in transportation funding are linked to air-quality attainment plans, which rely on estimates of mobile-source emissions. Transportation infrastructure decisions are also affected by emissions estimates from MOBILE. In response to a request from Congress, the National Research Council established the Committee to Review EPA's Mobile Source Emissions Factor (MOBILE) Model in October 1998. The committee was charged to evaluate MOBILE and to develop recommendations for improving the model.

Transportation accounts for much of the world's energy use and emissions. Transportation professionals are becoming more and more concerned about air quality, and there needs to be an accurate way to predict emissions on a micro-scale basis. This project used the traffic micro-simulation program, CORSIM, and it changed the way that CORSIM predicts emissions. CORSIM currently uses lookup tables for fuel use and emissions. The program looks up the speed, acceleration, and road grade for every second and assigns an emissions value to each vehicle for each second. The data for these values come from research conducted in the 1980's. Also, CORSIM does not account for cold starts, which can contribute to up to 40% of trip-based emissions. In this project, the current emissions and fuel use estimation method was replaced by the vehicle specific power (VSP) method. In this method, the VSP, which is a measure of engine load, was calculated for each second for every vehicle in the simulation. VSP was separated into 14 different modes, and CORSIM assigned emissions and fuel use rates based on which VSP mode the vehicle was in. The CORSIM code was also expanded to account for cold starts. The VSP method was verified using code written in C♯, and the cold start method was easily verifiable by hand calculations. Users of CORSIM will now be able to view emissions that are coming from data that is much more up to date, and addition of the cold start method will prevent CORSIM from underestimating emissions on networks with many cold starts.

The Mobile Source Emissions Factor (MOBILE) model is a computer model developed by the U.S. Environmental Protection Agency (EPA) for estimating emissions from on-road motor vehicles. MOBILE is used in air-quality planning and regulation for estimating emissions of carbon monoxide (CO), volatile organic compounds (VOCs), and nitrogen oxides (NOx) and for predicting the effects of emissions-reduction programs. Because of its important role in air-quality management, the accuracy of MOBILE is critical. Possible consequences of inaccurately characterizing motor-vehicle emissions include the implementation of insufficient controls that endanger the environment and public health or the implementation of ineffective policies that impose excessive control costs. Billions of dollars per year in transportation funding are linked to air-quality attainment plans, which rely on estimates of mobile-source emissions. Transportation infrastructure decisions are also affected by emissions estimates from MOBILE. In response to a request from Congress, the National Research Council established the Committee to Review EPA's Mobile Source Emissions Factor (MOBILE) Model in October 1998. The committee was charged to evaluate MOBILE and to develop recommendations for improving the model.