Indiana University-Purdue University Indianapolis (IUPUI) / Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.

Energy utilization in Sweden has been on a rise since the 1980’s and the industrial sector has been its highest consumer. The construction industry plays a central role in building and developing cities for a population which is increasing rapidly year by year. Environmental awareness has given incentives to reduce carbon dioxide emissions and to make operations more sustainable. The construction industry faces three main challenges in regards to sustainability which are, material usage, vehicle fleets, and machine parks. Vehicles and machines are integral parts within the construction industry, however, how to reduce their environmental impact is a relatively new research area with many challenges. The conclusion that carbon dioxide emissions must be reduced is one that has been reached by the construction industry in Sweden. One way to study this aspect is by collecting and managing data on their fuel consumption since the vehicles and machines operate almost exclusively on fossil fuels. Data collection on fuel consumption by vehicles and machinery will give insight to what factors increase or decrease it. Analyzing these factors will allow for reductions to be made in terms ofcarbon dioxide emissions and costs. The aim of this report is therefore to investigate the aspect of data collection and management on fuel consumption in vehicles and machinery. The report is the result of work conducted in cooperation with the Nordic construction and development company NCC. NCC has set a goal for reducing climate impacts from direct operations by 50% between the years 2015 and 2020. In order for this to be achieved, carbon dioxide emissions resulting from fuel consumption have to be accounted for. As this is a new research area, this report is to serve as a baseline for NCC to get an overview of what challenges and possibilities there are with efficient data collection and management on fuel consumption. The study is initiated by analyzing the three main aspects which are taken into consideration within this study. The first aspect is authoritative requirements which are demands from authorities such as municipalities or the Swedish Transport Administration. The second is the contractors such as NCC which have to meet these requirements. The final aspect is the suppliers who contractors hire for projects. Furthermore, interviews are carried out to gain insight on experiences of persons within the field and the challenges they have faced. A study on Norrtälje Harbor, an old industrial harbor turning into a new city district, is also conducted as there is available data from the vehicles and machines in this project. Finally, a gap-analysis is constructed in order to gain an overview of NCC’s present standings, future goals, andlimitations in terms of data collection and management from vehicles and machinery. The findings of this report conclude that a lack of standard is the biggest challenge which theindustry is facing. Authorities face challenges on how to set standards while the lack of standardsleads to different methods of data collection from contractors and suppliers. It is possible tocollect data from vehicles and machines but calculations are currently based on patterns and donot give a true view of the fuel consumption. Factors, such as driving habits and environment can affect the fuel consumption, therefore the data collected should take all these factors into consideration. Benefits that a company may gain by having this data include increase incompetitiveness due to environmental awareness and transparency as well as also lower costsas less fuel will be purchased. Reducing fuel consumption will ultimately reduce carbon dioxideemissions, which is the industry’s and NCC overall goal. / Energianvändningen i Sverige har stigit sedan 1980-talet och industrisektorn har bidragit mest till detta. Byggbranschen står för utformningen av städer för en snabbt växande befolkning. Miljömedvetenhet har gett organisationer incitament att minska koldioxidutsläppen ochutveckla mer hållbara verksamheter. Materialanvändning, fordonsflottor och maskinparker utgör de tre största utmaningarna inom byggbranschens klimatarbete. Fordon och maskiner är grundläggande delar inom byggbranschens verksamhet, men minskningen av denna miljöpåverkan är ett relativt nytt forskningsområde med många utmaningar. Aktörerna inom byggbranschen har utvecklat klimatstrategier för att minska koldioxidutsläppen där man bland annat vill samla in data om bränsleförbrukningen hos fordon och maskiner. Fordon och maskiner drivs huvudsakligen med fossila bränslen och genom att kartlägga denna förbrukning kan koldioxidutsläppen minskas. Datainsamling om fordon och maskiners bränsleförbrukning ger förståelse gällande vilka faktorersom ökar eller minskar förbrukningen. Genom att analysera dessa faktorer är det möjligt att minska koldioxidutsläpp och kostnader. Syftet med denna rapport är därför att undersöka datainsamling och hantering angående bränsleförbrukning för fordon och maskiner. Rapporten är resultatet av ett arbete som bedrivits i samarbete med det Nordiska bygg- och utvecklingsbolaget NCC. NCC har satt upp ett mål för att minska klimatpåverkan fråndirektverksamheten med 50% mellan åren 2015 och 2020. För att detta ska kunna uppnås måstekoldioxidutsläpp från bränsleförbrukningen redovisas. Eftersom detta är ett nyttforskningsområde, är rapport en utgångspunkt för att NCC ska få en överblick över vilka utmaningar och möjligheter det finns med effektiv datainsamling och hantering avbränsleförbrukning. Studien initieras genom att analysera de tre huvudaspekter som måste beaktas inom detta ämnesområde. Första aspekten är kraven som ställs av myndigheter som kommuner och Trafikverket. Den andra aspekten är NCC entreprenörer som måste uppfylla dessa krav. Sista aspekten är leverantörerna som anställs av entreprenörerna inom projekt. Det övergripandesynsättet i studien är därför att analysera varje aspekt separat för en djupare förståelse för derasrespektive samband inom detta ämne. Vidare genomförs intervjuer för att få insikt omerfarenheter från personer inom studieområdet och de utmaningar de har mött. En studie av Norrtälje hamn, en gammal industrihamn som omvandlas till ett nytt stadsdelsområde,genomförs, eftersom det finns tillgängliga data från fordon och maskiner i detta projekt. Slutligen konstrueras en gapanalys för att få en överblick över NCC:s nuvarande läge, framtidamål och begränsningar när det gäller datainsamling och hantering från fordon och maskiner. Slutsatsen som dras är att brist på standard är den största utmaningen som industrin står inför. Myndigheterna står inför utmaningar om hur man kan ställa krav, medan bristen på dem leder till olika metoder för datainsamling från entreprenörer och leverantörer. Det går att samla in data från fordon och maskiner, men beräkningarna är för närvarande baserade på schabloner och ger inte en sann bild av bränsleförbrukningen. Faktorer som körvanor och miljö kan påverka bränsleförbrukningen, därför bör de insamlade uppgifterna ta hänsyn till alla dessa faktorer. Fördelarna med att ha dessa data tillgängliga är att det öka konkurrensen på grund avmiljömedvetenhet och öppenhet, samt minska kostnader för inköp av bränsle. Att minska bränsleförbrukningen kommer i slutändan att minska koldioxidutsläppen, vilket är branschens och NCC:s övergripande mål.

This dissertation develops a framework for modeling vehicle emissions microscopically. In addition, the framework is utilized to develop the VT-Micro model using a number of data sources. Key input variables to the VT-Micro model include instantaneous vehicle speed and acceleration levels. Estimating accurate mobile source emissions is becoming more and more critical as a result of increasing environmental problems in large metropolitan urban areas. Current emission inventory models, such as MOBILE and EMPAC, are designed for developing large scale inventories, but are unable to estimate emissions from specific corridors and intersections. Alternatively, microscopic emission models are capable of assessing the impact of transportation scenarios and performing project-level analyses. The VT-Micro model was developed using data collected at the Oak Ridge National Laboratory (ORNL) that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light-duty vehicles (LDVs) and three light-duty trucks (LDTs) as a function of the vehicle's instantaneous speed and acceleration levels. The hybrid regression models predict hot stabilized vehicle fuel consumption and emission rates for LDVs and LDTs. The model is found to be highly accurate compared to the ORNL data with coefficients of determination ranging from 0.92 to 0.99. The study compares fuel consumption and emission results from MOBILE5a, VT-Micro, and CMEM models. The dissertation presents that the proposed VT-Micro model appears to be good enough in terms of absolute light-duty hot stabilized normal vehicle tailpipe emissions. Specifically, the emission estimates were found to be within the 95 percent confidence limits of field data and within the same level of magnitude as the MOBILE5a model estimates. Furthermore, the proposed VT-Micro model was found to reflect differences in drive cycles in a fashion that was consistent with field observations. Specifically, the model accurately captures the increase in emissions for aggressive acceleration drive cycles in comparison with other drive cycles. The dissertation also presents a framework for developing microscopic emission models. The framework develops emission models by aggregating data using vehicle and operational variables. Specifically, statistical techniques for aggregating vehicles into homogenous categories are utilized as part of the framework. In addition, the framework accounts for temporal lags between vehicle operational variables and vehicle emissions. Finally, the framework is utilized to develop the VT-Micro model version 2.0 utilizing second-by-second chassis dynamometer emission data for a total of 60 light duty vehicles and trucks. Also, the dissertation introduces a procedure for estimating second-by-second high emitter emissions. This research initially investigates high emitter emission cut-points to verify clear definitions of high emitter vehicles (HEVs) and derives multiplicative factors for newly developed EPA driving cycles. Same model structure with the VT-Micro model is utilized to estimate instantaneous emissions for a total of 36 light duty vehicles and trucks. Finally, the dissertation develops a microscopic framework for estimating instantaneous vehicle start emissions for LDVs and LDTs. The framework assumes a linear decay in instantaneous start emissions over a 200-second time horizon. The initial vehicle start emission rate is computed based on MOBILE6's soak time function assuming a 200-second decay time interval. The validity of the model was demonstrated using independent trips that involved cold start and hot start impacts with vehicle emissions estimated to within 10 percent of the field data. The ultimate expansion of this model is its implementation within a microscopic traffic simulation environment in order to evaluate the environmental impacts of alternative ITS and non-ITS strategies. Also, the model can be applied to estimate vehicle emissions using instantaneous GPS speed measurements. Currently, the VT-Micro model has been implemented in the INTEGRATION software for the environmental assessment of operational-level transportation projects. / Ph. D.

No description available.

Even though first order LWR models have many limitations, they are still widely used in many engineering applications. Second-order models, on the other hand, address many of those limitations. Among second-order models, the inhomogeneous Aw-Rascle-Zhang (ARZ) model is well-received as its structure generates characteristic waves that make physical sense. The ARZ model --- and other $2\times 2$ hyperbolic systems with a relaxation term --- possess a critical phase transition: whenever the sub-characteristic condition (SCC) is violated, uniform traffic flow is unstable, and small perturbations grow into nonlinear traveling waves, called jamitons. The case where the SCC is satisfied has been studied extensively. However, what is essentially unstudied is the question: which jamiton solutions are dynamically stable? To understand which stop-and-go traffic waves can arise through the dynamics of the model, this question is critical. This dissertation first outlines the mathematical foundations of the ARZ model and its solutions, then presents a computational study demonstrating which types of jamitons are dynamically stable, and which are not. After that, a procedure is presented that characterizes the stability of jamitons. The study reveals that a critical component of this analysis is the proper treatment of the perturbations to the shocks, and of the neighborhood of the sonic points. The insight gained from answering the question regarding the dynamical stability of jamitons has many applications. One particular application presented here is deriving an averaged model for the ARZ model. Such a model is as simple to solve (analytically and numerically) as the LWR model, but nevertheless captures the cumulative effects of jamitons regarding fuel consumption, total flow, and braking events. / Mathematics

The transportation sector is a major contributor to U.S. fuel consumption and emissions. Consequently, assessing the environmental impacts of transportation activities is essential for air-quality improvement programs. Current state-of-the-art models estimate vehicle emissions based on typical urban driving cycles. Most of these models offer simplified mathematical expressions to compute fuel consumption and emission rates based on average link speeds while ignoring transient changes in a vehicle's speed and acceleration level as it travels on a highway network. Alternatively, microscopic models capture these transient effects; however, the application of microscopic models may be costly and time consuming. Also, these tools may require a level of input data resolution that is not available. Consequently, this dissertation attempts to fill the void in energy and emission modeling by a framework for modeling vehicle fuel consumption and emissions mesoscopically. This framework is utilized to develop the VT-Meso model using a number of data sources. The model estimates average light-duty vehicle fuel consumption and emission rates on a link-by-link basis using up to three independent variables, namely: average travel speed, average number of stops per unit distance, and average stop duration. The mesoscopic model utilizes a microscopic vehicle fuel consumption and emission model that was developed at Virginia Tech to compute mode-specific fuel consumption and emission rates. This model, known as VT-Micro, predicts the instantaneous fuel consumption and emission rates of HC, CO and NOx of individual vehicles based on their instantaneous speed and acceleration levels. The mesoscopic model utilizes these link-by-link input parameters to construct a synthetic drive cycle and compute average link fuel consumption and emission rates. After constructing the drive cycle, the model estimates the proportion of time that a vehicle typically spends cruising, decelerating, idling and accelerating while traveling on a link. A series of fuel consumption and emission models are then used to estimate the amount of fuel consumed and emissions of HC, CO, CO2, and NOX emissions for each mode of operation. Subsequently, the total fuel consumed and pollutants emitted by a vehicle while traveling along a segment are estimated by summing across the different modes of operation and dividing by the distance traveled to obtain distance-based average vehicle fuel consumption and emission rates. The models are developed for normal and high emitting vehicles. The study quantifies the typical driver deceleration behavior for incorporation within the model. Since this model constructs a drive cycle which includes a deceleration mode, an accurate characterization of typical vehicle deceleration behavior is critical to the accurate modeling of vehicle emissions. The study demonstrates that while the deceleration rate typically increases as the vehicle approaches its desired final speed, the use of a constant deceleration rate over the entire deceleration maneuver is adequate for environmental modeling purposes. Finally, the study validates the model on a freeway and urban arterial network. The results demonstrate that the model provides accurate estimates of vehicle fuel consumption and emission rates and is adequate for the evaluation of transportation operational projects. / Ph. D.

In the past few years, the price of petroleum based fuels, especially vehicle fuels such as gasoline and diesel, has been increasing at a significant rate. Consequently, there is much more consumer interest related to reducing fuel consumption for conventional vehicles and hybrid electric vehicles (HEVs) than in the past. The goal of many competitions and challenges held in North America and Europe is to achieve extremely low fuel consumption. A possible strategy to reduce fuel consumption is to use the vehicle's fuel converter such as an engine to accelerate the vehicle to a high speed and coast to a lower speed with the engine off. This method will reduce fuel flow to zero during the coast phase. Also, the vehicle uses higher power engine load to accelerate to the upper vehicle speed in a limited time, thus increasing the engine brake thermal efficiency. This strategy is known as "pulse and glide" or "burn and coast" in some references. In this study, the "pulse and glide" (PnG) method is first applied to a conventional vehicle to quantify the fuel consumption benefits when compared to steady speed conditions over the same distance. After that, an HEV is used as well to investigate if a hybrid system can further reduce fuel consumption with the proposed strategy. Note that the HEV used in this study has the advantage that the engine can be automatically shut off below a certain speed (~40 mph) at low loads, however a driver must shut off the engine manually in a conventional vehicle to apply this driving strategy. In this document, three preliminary results of the PnG driving strategy are presented; (1) improved fuel economy for a conventional vehicle from a simple spread sheet model, (2) improved fuel economy for an HEV from a dynamic vehicle simulation model (the Powertrain Analysis Toolkit (PSAT)) and (3) improved fuel economy for an HEV from vehicle testing at Argonne National Laboratory (ANL), all compared to steady speed conditions. The preliminary results show that the impact of engine load and kinetic energy stored in vehicle inertia is significant for fuel consumption using a PnG driving strategy compared to steady speed driving at the same average speed case. Especially, fuel economy can be improved at low speed range and higher acceleration because the aerodynamic drag force is smaller at low speed and the engine is running in a more efficient region for a short period of time respectively. In the last section, proposed directions of research are addressed based on the preliminary results. / Ph. D.

In the last few years, scientific consensus is that emission of greenhouse gases (GHGs) into the atmosphere is contributing to changes in the earth's climate. While uncertainty remains over the pace and dimensions of the change, a consensus on the need for action has grown among the public and elected officials. In part, this shift has been accelerated by concern over energy security and rising fuel prices. The new political landscape has led many cities, states, and regions to institute policies aimed at reducing GHG emissions. These policies and emerging initiatives have significant implications for the transportation planning process. The transportation sector accounts for approximately 27% of GHG production in the U.S. (as of 2003) and while the U.S. accounts for only roughly 5% of the world's population, it is estimated that it produces over 20% of the world's GHG emissions. Note that this does not include "lifecycle" emissions that result from the processes undertaken to extract, manufacture, and transport fuel. Carbon dioxide represents approximately 96% of the transportation sector's radiative forcing effects. Unlike conventional air pollutants, carbon dioxide emissions are directly tied to the amount of fuel consumed and its carbon intensity. Therefore, emissions reductions can be achieved by increasing the use of low-carbon fuels, improving fuel economy, or reducing total vehicle miles of travel - often called the three legged stool. (A fourth leg is congestion reduction, at certain optimal speeds). These same factors are related to our use of imported oil, so actions taken to reduce GHG emissions may actually produce benefits in both policy areas. The climatic risks of additional emissions associated with capacity projects must be balanced against the mobility, safety, and economic needs of a community or region. Consequently, this dissertation attempts to quantify the impacts of high-emitting vehicles on the environment and to propose solutions to enhance the currently-used high-emitting vehicle detection procedures. In addition, fuel consumption and emission models for high-speed vehicles are developed in order to provide more reliable estimates of vehicle emissions and study the impact of vehicle speeds on vehicle emissions. The dissertation extends the state-of-the-art analysis of high emitting vehicles (HEVs) by quantifying the network-wide environmental impact of HEVs. The literature reports that 7% to 12% of HEVs account for somewhere between 41% to 63% of the total CO emissions, and 10% are responsible for 47% to 65% of HC emissions, and 10% are responsible for 32% of NOx emissions. These studies, however, are based on spot measurements and do not necessarily reflect network-wide impacts. Consequently, the research presented in this dissertation extends the state-of-knowledge by quantifying HEV contributions on a network level. The study uses microscopic vehicle emission models (CMEM and VT-Micro model) along with pre-defined drive cycles (under the assumption that the composite HEV and VT-LDV3 represent HEVs and NEVs, respectively) in addition to the simulation of two transportation networks (freeway and arterial) to quantify the contributions of HEVs. The study demonstrates that HEVs are responsible for 67% to 87% of HC emissions, 51% to 78% of CO emissions, and 32% to 62% of the NOX emissions for HEV percentages ranging from 5% to 20%. Additionally, the traffic simulation results demonstrate that 10% of the HEVs are responsible for 50% to 66% of the I-81 HC and 59% to 78% of the Columbia Pike HC emissions, 35% to 67% of the I-81 CO and 38% to 69% of the Columbia Pike CO emissions, and 35% to 44% of the I-81 NOX and 35% to 60% of the Columbia Pike NOX emissions depending on the percentage of the normal-emitting LDTs to the total NEVs. HEV emission contributions to total HC and CO emissions appear to be consistent with what is reported in the literature. However, the contribution of NOX emissions is greater than what is reported in the literature. The study demonstrates that the contribution of HEVs to the total vehicle emissions is dependent on the type of roadway facility (arterials vs. highways), the background normal vehicle composition, and the composition of HEVs. Consequently, these results are network and roadway specific. Finally, considering that emission control technologies in new vehicles are advancing, the contribution of HEVs will increase given that the background emission contribution will decrease. Given that HEVs are responsible for a large portion of on-road vehicle emissions, the dissertation proposes solutions to the HEV screening procedures. First, a new approach is proposed for estimating vehicle mass emissions from concentration remote sensing emission measurements using the carbon balance equation in conjunction with either the VT-Micro or PERE fuel consumption rates for the enhancement of current state-of-the-art HEV screening procedures using RSD technology. The study demonstrates that the proposed approach produces reliable mass emission estimates for different vehicle types including sedans, station wagons, full size vans, mini vans, pickup trucks, and SUVs. Second, a procedure is proposed for constructing on-road RS emission standards sensitive to vehicle speed and acceleration levels. The proposed procedure is broadly divided into three sub-processes. In the first process, HE cut points in grams per second are developed as a function of a vehicle's speed and acceleration levels using the VT-Micro and CMEM emission models. Subsequently, the HE cut points in grams per second are converted to concentration emissions cut points in parts per million using the carbon balance equation. Finally, the scale factors are computed using either ASM ETW- and model-year-based standards or engine-displacement-based standards. Given the RS emissions standards, the study demonstrated that the use of on-road RS cut points sensitive to speed and acceleration levels is required in order to enhance the effectiveness of RS. Finally, the dissertation conducted a study to develop fuel consumption and emissions models for high-speed vehicles to overcome the shortcomings of state-of-practice models. The research effort gathered field data and developed models for the estimation of fuel consumption, CO₂, CO, NO, NO2, NOx, HC, and PM emissions at high speeds. A total of nine vehicles including three semi-trucks, three pick-up trucks, and three passenger cars were tested on a nine-mile test track in Pecos, Texas. The fuel consumption and emission rates were measured using two portable emission measurement systems. Models were developed using these data producing minimum errors for fuel consumption, CO₂, NO2, HC, and PM emissions. Alternatively, the NO and NOx emission models produced the highest errors with a least degree of correlation. Given the models, the study demonstrated that the newly constructed models overcome the shortcomings of the state-of-practice models and can be utilized to evaluate the environmental impacts of high speed driving. / Ph. D.

Efficiency is a driving constraint for electrical power systems as global energy demands are ever increasing. Followed by the introduction of diesel generators, electricity has become available in more locations than ever. However, operating a diesel generator on its own is not the most energy efficient. This is because the high crest factor loads, of many applications, decrease the fuel efficiency of a hydrocarbon generator. To understand this, we need to understand how an electrical load affects a generator. Starting with a load profile, a system designer must choose a generator to meet peak demand, marking the first instance where a load profile has influence over a generator. This decision will insure that brownouts do not occur, but, this will lead to poor energy efficiency. We say this because a generator is most energy efficient under heavier loads, meaning, during lighter loads, more fuel will be consumed to produce the same amount of energy. While this may be fine if the peak load was close to the average load, however, the actual crest factor for a typical residential load profile is much higher. This gap between peak and average load means that a generator will spend most of its time operating at its most inefficient point. To compensate for this, and reduce fuel consumption, the Mechatronics Lab at Virginia Tech has developed a mobile hybrid power system (MHPS) to address this problem. The solution was to augment a diesel generator with a battery pack. This allowed us to constrain the generator so that it only operates with fixed efficiency. It is the theory behind this system that will be covered in this thesis. / Master of Science

Since the transportation sector is highly responsible for U.S. fuel consumption and emissions, assessing the environmental impacts of transportation activities is essential for air-quality improvement programs. Also, high emitting vehicles need to be considered in the modeling of mobile-source emissions, because they contribute to a large portion of the total emissions, although they comprise a small portion of the vehicle fleet. In the context of this research, the thesis quantifies the environmental impacts of roadway grades and proposes a procedure that can enhance the screening of high emitting vehicles. First, the study quantifies the environmental impacts of roadway grades. Although roadway grades are known to affect vehicle fuel consumption and emission rates, there do not appear to be any systematic evaluations of these impacts in the literature. Consequently, this study addresses this void by offering a systematic analysis of the impact of roadway grades on vehicle fuel consumption and emission rates using the INTEGRATION microscopic traffic simulation software. The energy and emission impacts are quantified for various cruising speeds, under stop and go conditions, and for various traffic signal control scenarios. The study demonstrates that the impact of roadway grade is significant with increases in fuel consumption and emission rates in excess of 9% for a 1% increase in roadway grade. Consequently, a reduction in roadway grades in the range of 1% can offer savings that are equivalent to various forms of advanced traffic management systems. Second, the study proposes a new procedure for estimating vehicle mass emissions from remote sensing device measurements that can be used to enhance HEV screening procedures. Remote Sensing Devices (RSDs) are used as supplementary tools for screening high emitting vehicles (HEVs) in the U.S. in order to achieve the National Ambient Air Quality Standards (NAAQS). However, tailpipe emissions in grams cannot be directly measured using RSDs because they use a concentration-based technique. Therefore, converting a concentration measurement to mass emissions is needed. The research combines the carbon balance equation with fuel consumption estimates to make the conversion. In estimating vehicle fuel consumption rates, the VT-Micro model and a Vehicle Specific Power (VSP)-based model (the PERE model) are considered and compared. The results of the comparison demonstrate that the VSP-based model under-estimates fuel consumption at 79% and produces significant errors (R2 = 45%), while the VT-Micro model produces a minimum systematic error of 1% and a high degree of correlation (R2 = 87%) in estimating a sample vehicle's (1993 Honda Accord with a 2.4L engine) fuel consumption. The sample vehicle was correctly identified 100%, 97%, and 89% as a normal vehicle in terms of HC, CO, NOX emissions, respectively, using its in-laboratory measured emissions. Its estimated emissions yielded 100%, 97%, and 88% of correct detection rates in terms of HC, CO, NOX emissions, respectively. The study clearly demonstrates that the proposed procedure works well in converting concentration measurements to mass emissions and can be applicable in the screening of HEVs and normal emitting vehicles for several vehicle types such as sedans, station wagons, full-size vans, mini vans, pickup trucks, and SUVs. / Master of Science