Mathematical models to predict vehicle fuel consumption and emission metrics are presented in this thesis. Vehicle fuel consumption and emissions are complex functions to be approximated in practice due to numerous variables affecting their outcome. Vehicle energy and emissions are particularly sensitive to changes in vehicle state variables such as speed and acceleration, ambient conditions such as temperature, and driver control inputs such as acceleration pedal position and gear shift speeds, among others. Recent empirical studies have produced large amounts of data concerning vehicle fuel consumption and emissions rates and offer a wealth of information to transportation planners. Unfortunately, unless simple relationships are found between fuel consumption and vehicle emission metrics, their application in microscopic traffic and macroscopic planning models becomes prohibitive computationally. This thesis describes the development of microscopic energy and emission models using nonlinear multiple regression and neural network techniques to approximate vehicle fuel consumption and emissions field data. The energy and emission models described in this thesis utilized data collected by the Oak Ridge National Laboratory. The data include microscopic fuel consumption and emission measurements (CO, HC, and NOx) for eight light duty vehicles as a function of vehicle speed and acceleration. The thesis describes modeling processes and the tradeoffs between model accuracy and computational efficiency. Model verification results are included for two vehicle driving cycles. The models presented estimate vehicle fuel consumption within 2.5% of their actual measured values. Vehicle emissions errors fall in the range of 3-33% with correlation coefficients ranging between 0.94 and 0.99. Future transportation planning studies could also make use of the modeling approaches presented in the thesis. The models developed in this study have been incorporated into a microscopic traffic simulation tool called INTEGRATION to further demonstrate their application and relevance to traffic engineering studies. Two sample Intelligent Transportation Systems (ITS) application results are included. In the case studies, it was found that vehicle fuel consumption and emissions are more sensitive to the level of vehicle acceleration than to the vehicle speed. Also, the study shows signalization techniques can reduce fuel consumption and emissions significantly, while incident management techniques do not affect the energy and emissions rates notably. / Master of Science

The work presented in this thesis is the result of the KTH CICERO project “Dynamic Engine Performance” in which the main objective was to develop simple models foremission formation. The demand for such models is increasing, mainly due to the tightening emission legislation for diesel engines which has lead to more complex engines and thereby more laborious development and calibration processes. Simple emission models can be a valuable tool during the development phase, e.g. when used with models for gas exchange - and after-treatment systems, and for precalibration of the engine control settings. Since engines in automotive application typically work under dynamic load, the main prerequisites were that the models should be comprehensive enough to handle the extreme conditions that can occur in engines during load transients but still simple enough to be used for calibration. Two main approaches have been used; one where the combustion and emission formation processes were modeled from the flame front and downstream using equilibrium chemistry. In the other approach, the entire mixing/entrainment process was modeled and emission formation was modeled with kinetic chemistry. Both approaches were found to meet the requirements but had different advantages; the first, simpler approach had shorter calculation time while the latter was more comprehensive and required less tuning. The latter also resulted in a model for heat release rate which can be useful as a stand-alone model and allows the emission models to be used for untested conditions. Another objective in this project was to identify techniques/instruments that can be used for emission measurements during transient operation since these are essential for understanding of emission formation in these conditions and as validation data for the emission models. / QC 20110502

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.

The transportation sector has a significant impact on the environment both nationally and globally since it is a major vehicle fuel consumption and emissions contributor. These emissions are considered a major environmental threat. Consequently, decision makers desperately need tools that can estimate vehicle emissions accurately to quantify the impact of transportation operational projects on the environment. Microscopic fuel consumption and emission models should be capable of computing vehicle emissions reliably to assist decision makers in developing emission mitigation strategies. However, the majority of current state-of-the-art models suffer from two major shortcomings, namely; they either produce a bang-bang control system because they use a linear fuel consumption versus power model or they cannot be calibrated using publicly available data and thus require expensive laboratory or field data collection. Consequently, this dissertation attempts to fill this gap in state-of-the-art emission modeling through a framework based on the Virginia Tech Comprehensive Power-Based Fuel consumption Model (VT-CPFM), which overcomes the above mentioned drawbacks. Specifically, VT-CPFM does not result in a bang-bang control and can be calibrated using publicly available vehicle and road pavement parameters. The main emphasis of this dissertation is to develop a simple and reliable emission model that is able to compute instantaneous emission rates of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) for the light-duty vehicles (LDVs) and heavy-duty diesel trucks (HDDTs). The proposed extension is entitled Virginia Tech Comprehensive Power-Based Fuel consumption and Emission Model (VT-CPFEM). The study proposes two square root models where the first model structure is a cubic polynomial function that depends on fuel estimates derived solely from VT-CPFM fuel estimates, which enhances the simplicity of the model. The second modeling framework combines the cubic function of the VT-CPFM fuel estimates with a linear speed term. The additional speed term improves the accuracy of the model and can be used as a reference for the driving condition of the vehicle. Moreover, the model is tested and compared with existing models to demonstrate the robustness of the model. Furthermore, the performance of the model was further investigated by applying the model on driving cycles based on real-world driving conditions. The results demonstrate the efficacy of the model in replicating empirical observations reliably and simply with only two parameters. / Ph. D.

In 2009, Cobb County School District (CCSD) and Georgia Institute of Technology (Georgia Tech) received a competitive federal grant to implement an idle and tailpipe emission reduction program in the CCSD bus fleet. The project is designed to reduce school bus idling by installing GPS and idle detection systems in the bus, providing bus dispatchers with a web system to track vehicle activity and idling in real-time, and to automatically shut off the engine when idle thresholds at specific locations are exceeded. A team of Georgia Tech researchers is implementing the anti-idle program and estimating the emissions and fuel savings from the project using approved modeling methods. This thesis presents the results of the emission modeling process, as well as an analysis of baseline school bus idling activity. EPA's MOVES mobile source emission model was used to develop emission rates for school buses for each operating mode, which are defined by the instantaneous vehicle speed, acceleration and scaled tractive power. Local data for Cobb County and Atlanta were collected and input into the MOVES model. The pollutants modeled include carbon dioxide, carbon monoxide, particulate matter (coarse and fine), oxides of nitrogen, and gaseous hydrocarbons. The vehicle activity data collected through the GPS and communications equipment installed in the buses were classified into the operating mode bins for each second of recorded data, and multiplied by the corresponding emission rate to determine the total modal emissions before and after project implementation. Preliminary results suggest that thousands of gallons of diesel fuel and thousands of dollars can be saved with the project, improving overall fleet fuel efficiency by 2%, as well as reducing emissions in some categories by as much as 38%.

Emission and fuel consumption are among the key parameters when designing a combustion system. Combustion CFD can assist in this task only if good enough accuracy is achieved regarding combustion and emission predictions. The aim of this master thesis is to evaluate the use of detailed reaction mechanisms (as a substitute for standard combustion model) in terms of computational time and result accuracy. Several mechanisms for n-heptane are tested. Lund University optical engine experimental case is used for this evaluation.Results showed that detailed chemistry can predict ignition accurately but differences are observed in the peak cylinder pressure. The computational time also increased significantly as size and complexity of the mechanism increased. Recommendations are given to improve predictions using detailed chemistry approach which is found to be an interesting approach especially for lift-off length predictions.

Dans le contexte du réchauffement climatique global, les institutions académiques et internationales comme GIEC et de nombreux pays ont proposé des objectifs de réduction des émissions de CO2. L'objectif de cette thèse est d'évaluer ces objectifs gouvernementaux en les comparants avec les objectifs globaux à l'aide de différentes méthodes d'allocations lesquelles correspondent à différents principes d'équité en matière d'émissions carbones.Afin d'évaluer les feuilles de route technologique permettant d'obtenir les réductions d'émission de CO2 nécessaires, un modèle qualifié de flexible est proposé à destination des décideurs. Notre modèle permet d'éviter les opérations informatiques complexes et peut être personnalisé en fonction de différents besoins. Les simulations sont réalisées jusqu'à l'horizon 2050 lequel est souvent considéré comme un pivot dans les habitudes de consommation d'énergies notamment. Dans cette thèse, des feuilles de route technologique pour les différents objectifs gouvernementaux en matières d'émissions de CO2 sont étudiées pour trois pays : la Chine, la France et les États-Unis. Le modèle couvre les principaux secteurs responsables des émissions de CO2 et étudie l'influence de différentes technologies sur le mix énergétique. Diverses méthodes et approches sont utilisées dans notre modélisation. L'identité IPAT est utilisée pour la décomposition des émissions dans les secteurs de l'énergie. Le modèle STIRPAT permet quant à lui d'évaluer l'évolution des émissions de CO2 dans les scénarios Business-as-Usual. Le modèle SVR est utilisé dans le cadre des projections de production d'électricité. Enfin, l'indice de Theil est employé pour mesurer les inégalités d'émissions de CO2 par tête. A la différence des modèles plus classiques en économie de l'énergie, notre modèle propose des feuilles de route technologiques selon différents critères, comme par exemple avec le développement « équilibré » de la technologie entre les secteurs, ou le critère de disponibilité des ressources énergétiques. Par ailleurs, l'équité carbone, avec la convergence des technologies dans les secteurs à long terme, peut être mise en œuvre dans notre modèle et joue, dans ce cas, comme une contrainte supplémentaire dans l'optimisation multi-objectifs.Nos résultats montrent que les objectifs gouvernementaux en France et aux États-Unis sont « très stricts » car, pour les atteindre, tous les secteurs doivent réaliser des efforts importants de réduction de CO2. En revanche, l'objectif gouvernemental de la Chine s'avère « plus facile » à réaliser car les progrès dans les technologies qui sont nécessaires sont moins exigeants.Plus précisément, si on prévoit que le mix énergétique reste inchangé en Chine et aux États-Unis, le CSC deviendra indispensable dans le secteur de l'énergie. Pour la France, 80% des voitures devront être remplacées par des véhicules électriques afin d'atteindre son objectif en matière de CO2.Toutefois, en considérant l'équité carbone entre secteurs, la combustion du charbon est censée être réduite de deux tiers en Chine et devra être pratiquement éliminée aux États-Unis. Par contre, le gaz peut être encouragé dans son utilisation dans le secteur de l'énergie en particulier aux États-Unis. Concernant le secteur du transport, plus de 60 % des véhicules doivent être remplacés par des véhicules électriques en Chine. Cette part serait d'environ 90 % en France et aux États-Unis.Enfin, la sensibilité des paramètres du modèle a été testée pour simulations, à chaque étape du travail, et pour toutes les roadmaps technologiques. Les résultats des tests de sensibilité montrent que la production d'électricité et l'intensité d'émissions sont les deux paramètres dont l'influence est la plus importante sur les émissions futures de CO2. Ainsi l'amélioration de l'efficacité de la combustion du charbon et de l'efficacité énergétique de l'électricité joueront un rôle central dans la réductions des émissions de CO2. / In the context of global warming, academic institutes, international institutions such as the IPCC, and governments of numerous countries have proposed global objectives of reducing CO2 emissions and announced national targets. The purpose of this thesis is to assess the governmental targets in comparing with the global objectives of various allocation methods, which correspond to different carbon equity principles.In order to evaluate the technology roadmaps which are necessary to achieve these reductions of CO2 emissions, a flexible modeling framework is proposed for policy makers. Our sectoral model avoids the complex computing operations. It can be customized according to different requirements and situations. We simulate the model up to the horizon 2050, which is often seen as a turning point in energy use patterns worldwide – forced by the probable decline in hydrocarbons extraction.In the thesis, the technology roadmaps for the governmental targets on CO2 emissions are studied for three typical countries: China, France, and the United States. The model covers the sectors responsible for the greatest part of CO2 emissions: power, transport, residence and industry sector, in studying the impacts of the principle energy technologies, such as energy mix, Carbon Capture and Storage (CCS), electric vehicles and energy efficiency.Various methods and approaches are used in our modeling. IPAT identity - which assumes the environment Impact is the results of Population, Affluence and Technology - is employed in the power sector emission decomposition. Besides STIRPAT - for Stochastic Impacts by Regression on Population, Affluence and Technology - model is used for the projection of CO2 emissions in the Business-as-Usual scenario. Then SVR - for Support Vector Regression - is used to forecast electricity production. Finally, the Theil index is employed as the measurement of per capita CO2 emission inequality. Different from classic cost-effective energy system models, our model provides the technology pathways for different criteria, such as balanced development of energy technology across sectors, availability of energy resources, etc. Besides, the carbon equity is employed as one of the constraints in the multi-objective optimization, under the consideration of the convergence of technologies in sectors in the long-term.Our results show that the governmental targets in France and the United States prove very strict, as they require all sectors to make large efforts in reducing CO2 emissions. In contrast, the governmental target in China seems more easily achievable, as the necessary advances of technologies are less demanding. More precisely: if the energy mix is expected to be kept unchanged in China and in the United States of America, the CCS prove indispensable in the power sector. In France, 80% of automobiles are required to be changed into electric vehicles, in order to get the target of CO2 emissions.However, under the sectoral carbon equity consideration, coal combustion is projected to be reduced by two thirds in China, and it will have to be almost eliminated in the United States to achieve their CO2 reduction target. But gas is encouraged to be used in the power sector, especially in the United States. Regarding the transport sector, more than 60% of vehicles should be replaced to electric vehicles in China, and this share will be about up to 90% in France and the United States.Finally the sensitivity of parameters in the model is tested for a robust simulation, at each step of the work, and for all technology roadmaps. The results of the sensitivity tests show that electricity production and the emission intensity of production are the two parameters with the most important influence on CO2 emissions. Thus improving the efficiency of coal combustion and the energy efficiency of electricity will play an important role in the CO2 emission reductions.

The transportation sector is responsible for the majority of airborne particles and global energy consumption in urban areas. Its role in generating air pollution in urban areas is even more critical, as many visitors, commuters and citizens travel there daily for various reasons. Emissions released by transport fleets have an exhaust (tailpipe) and a non-exhaust (brake wears ) origin. Both exhaust and non-exhaust airborne particles can have destructive effects on the human cardiovascular and respiratory systems and even lead to premature deaths. This dissertation aims to estimate the amount of exhaust and brake emissions in a real case study by proposing an innovative methodology. For this purpose, different levels of study have been introduced, including the subsystem level, the system level, the environmental level and the suprasystem level. To address these levels, two approaches were proposed along with a data collection process. First, a comprehensive field survey was conducted in the area of Buonconsiglio Castle and data was collected on traffic and non-traffic during peak hours. Then, in the first approach, a state-of-the-art simulation-based method was presented to estimate the amount of exhaust emissions generated and the rate of fuel consumption in the case study using the VISSIM traffic microsimulation software and Enviver emission modeler at the suprasystem level. In order to calculate the results under different mobility conditions, a total of 18 scenarios were defined based on changes in vehicle speeds and the share of heavy vehicles (HV%) in the modal split. Subsequently, the scenarios were accurately modelled in the simulation software VISSIM and repeated 30 times with a simulation runtime of three hours. The results of the first approach confirmed the simultaneous effects of considering vehicle speed and HV % on fuel consumption and the amount of exhaust emissions generated. Furthermore, the sensitivity of exhaust emissions and fuel consumption to variations in vehicle speed was found to be much higher than HV %. In other words, the production of NOx and VOC emissions can be increased by up to 20 % by increasing the maximum speed of vehicles by 10 km/h. Conversely, increasing the HV percentage at the same speed does not seem to produce a significant change. Furthermore, increasing the speed from 30 km/h to 50 km/h increased CO emissions and fuel consumption by up to 33%. Similarly, a reduction in speed of 20 or 10 km/h with a 100% increase in HV resulted in a 40% and 27% decrease in exhaust emissions per seat, respectively. In the second approach, a novel methodology was proposed to estimate the number of brake particles in the case study. To achieve this goal, a downstream approach was proposed starting from the suprasystem level (microscopic traffic simulation models in VISSIM) and using a developed mathematical vehicle dynamics model at the system level to calculate the braking torques and angular velocities of the front and rear wheels, and proposes an artificial neural network (ANN) as a brake emission model, which has been appropriately trained and validated using emission data collected through more than 1000 experimental tribological tests on a reduced-scale dynamometer at the subsystem level (braking system). Consideration of this multi-level approach, from tribological to traffic-related aspects, is necessary for a realistic estimation of brake emissions. The proposed method was implemented on a targeted vehicle, a dominant SUV family car in the case study, considering real driving conditions. The relevant dynamic quantities of the targeted vehicle (braking torques and angular velocities of the wheels) were calculated based on the vehicle trajectory data such as speed and deceleration obtained from the traffic microsimulation models and converted into braking emissions via the artificial neural network. The total number of brake emissions emitted by the targeted vehicles was predicted for 10 iterations route by route and for the entire traffic network. The results showed that a large number of brake particles (in the order of billions of particles) are released by the targeted vehicles, which significantly affect the air quality in the case study. The results of this dissertation provide important information for policy makers to gain better insight into the rate of exhaust and brake emissions and fuel consumption in metropolitan areas and to understand their acute negative impacts on the health of citizens and commuters.

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