This thesis investigates how various driver signals, signals that a truck driver can interact with, influences fuel consumption and what are the optimal values of these signals in various traffic conditions. More specifically, the objective is to estimate good driver behavior in various traffic conditions and compare bad driver behavior in similar situations to see how performing a specific driver action, changing a driver signal from the bad driver value to the corresponding good driver value impacts the fuel consumption. The result is an AI-based algorithm that utilizes the transformer model architecture to estimate good driver behavior, based on environmental describing signals, as well as fuel consumption. Utilizing these, causal inference is used to estimate how much fuel can be saved by switching a driver signal from a bad driver value to a good driver value.

Automotive engines are becoming increasingly technically complex and associated legal emissions standards more restrictive, making the task of identifying optimum actuator settings to use significantly more difficult. Given these challenges, this research aims to develop a process for engine calibration optimisation by exploiting advanced mathematical methods. Validation of this work is based upon a case study describing a steady-state Diesel engine calibration problem. The calibration optimisation problem seeks an optimal combination of actuator settings that minimises fuel consumption, while simultaneously meeting or exceeding the legal emissions constraints over a specified drive cycle. As another engineering target, the engine control maps are required as smooth as possible. The Multidisciplinary Design Optimisation (MDO) Frameworks have been studied to develop the optimisation process for the steady state Diesel engine calibration optimisation problem. Two MDO strategies are proposed for formulating and addressing this optimisation problem, which are All At Once (AAO), Collaborative Optimisation. An innovative MDO formulation has been developed based on the Collaborative Optimisation application for Diesel engine calibration. Form the MDO implementations, the fuel consumption have been significantly improved, while keep the emission at same level compare with the bench mark solution provided by sponsoring company. More importantly, this research has shown the ability of MDO methodologies that manage and organize the Diesel engine calibration optimisation problem more effectively. / Jaguar Land Rover

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No description available.

Defining key parameters for a charge sustaining supervisory (torque split) control strategy as well as an engine and catalyst warm-up strategy for a Split Parallel Architecture Extended-Range Electric Vehicle (SPA E-REV) is accomplished through empirically and experimentally measuring vehicle tailpipe emissions and energy consumption for two distinct control strategies. The results of the experimental testing and analysis define how the vehicle reduces fuel consumption, petroleum energy use and greenhouse gas emissions while maintaining low tailpipe emissions. For a SPA E-REV operating in charge sustaining mode with the engine providing net propulsive energy, simply operating the engine in regions of highest efficiency does not equate to the most efficient operation of the vehicle as a system and can have adverse effects on tailpipe emissions. Engine and catalyst warm-up during the transition from all-electric charge depleting to engine-dominant charge sustaining modes is experimentally analyzed to evaluate tailpipe emissions. The results presented are meant to define key parameters for a high-level torque-split strategy and to provide an understanding of the tradeoffs between low energy consumption and low tailpipe emissions. The literature review gives a background of hybrid and plug-in hybrid vehicle control publications including tailpipe emissions studies, but does not include experimental results and comparisons of supervisory strategies designed for low fuel consumption and low tailpipe emissions the SPA E-REV architecture. This paper details the high-level control strategy chosen for balancing low energy consumption and low tailpipe emissions while the engine is operating. Vehicle testing data from a chassis dynamometer is presented in support of the research. / Master of Science

This thesis discusses enhancements to the Global Demand Model (GDM). The model addresses the need to predict: a) number of flights Worldwide by Origin-Destination (OD) airport pair, b) the number of seats (surrogate of demand) by OD airport pair, c) the fleet evolution over time, d) fuel consumption by OD pair and aircraft type, and emissions by OD pair and aircraft type. The model has developed an airline fleet assignment module to predict changes to the airline fleet in the future. Specifically, the model has the capability to examine the fuel and emission benefits of next generation N+1 aircraft and advanced NASA's N+2 aircraft are adopted in the future. / Master of Science / This thesis discusses enhancements to a model, Global Demand Model (GDM), developed at Air Transportation Systems Laboratory at Virginia Tech. The model addresses the need to predict: a) number of flights Worldwide by Origin-Destination (OD) airport pair, b) the number of seats (surrogate of demand) by OD airport pair, c) the fleet evolution over time, d) fuel consumption by OD pair and aircraft type, and emissions by OD pair and aircraft type. The model has developed an airline fleet assignment module to predict changes to the airline fleet in the future. Specifically, the model has the capability to examine the fuel and emission benefits if next generation N+1 aircraft and advanced NASA’s N+2 aircraft are adopted in the future.

Traffic signals are used at intersections to manage the flow of vehicles by allocating right-of-way in a timely manner for different users of the intersection. Traffic signals are therefore installed at an intersection to improve overall safety and to decrease vehicular average delay. However, the variation of driving speed in response to these signals causes an increase in fuel consumption and air emission levels. One solution to this problem is Eco-Cooperative Adaptive Cruise Control (Eco-CACC), which attempts to reduce vehicle fuel consumption and emission levels by optimizing driver behavior in the vicinity of a signalized intersection. Various Eco-CACC algorithms have been proposed by researchers to address this issue. With the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, algorithms are being developed that utilize signal phasing and timing (SPaT) data together with queue information to optimize vehicle trajectories in the vicinity of signalized intersections. The research presented in this thesis constitutes the third phase of a project that entailed developing and evaluating an Eco-CACC system. Its main objective is to evaluate the benefits of the newly developed Eco-CACC algorithm that was proposed by the Center for Sustainable Mobility at the Virginia Tech Transportation Institute. This algorithm uses advanced signal information (SPaT) to compute the fuel-optimal trajectory of vehicles, and, then, send recommended speeds to drivers as an audio message or implement them directly into the subject vehicle. The objective of this study is to quantitatively quantify the fuel-efficiency of the Eco-CACC system in a real field environment. In addition, another goal of this study is to address the implementation issues and challenges with the field application of the Eco-CACC system. A dataset of 2112 trips were collected as part of this research effort using a 2014 Cadillac SRX equipped with a vehicle onboard unit for (V2V) and (V2I) communication. A total of 32 participants between the ages of 18 and 30 were randomly selected from one age group (18-30) with an equal number of males and females. The controlled experiment was conducted on the Virginia Smart Road facility during daylight hours for dry pavement conditions. The controlled field experiment included four different scenarios: normal driving, driving with red indication countdown information provided to drivers, driving with recommended speed information computed by the Eco-CACC system and delivered to drivers, and finally automated driving (automated Eco-CACC system). The controlled field experiment was conducted for four values of red indication offsets along an uphill and downhill approach. The collected data were compared with regard to fuel economy and travel time over a fixed distance upstream and downstream of the intersection (820 ft (250 m) upstream of the intersection to 590 ft (180 m) downstream for a total length of 1410 ft (430 m)). The results demonstrate that the Eco-CACC system is very efficient in reducing fuel consumption levels especially when driving downhill. The field data indicates that the automated scenario could produce fuel and travel time savings of 31% and 9% on average, respectively. In addition, the study demonstrates that driving with a red indication countdown and recommended speed information can produce fuel savings ranging from 4 to 21 percent with decreases in travel times ranging between 1 and 10 percent depending on the value of red indication offset and the direction. Split-split-plot design was used to analyze the data and test significant differences between the four scenarios with regards to fuel consumption and travel time. The analysis shows that the differences between normal driving and driving with either the manual or automated Eco-CACC systems are statistically significant for all the red indication offset values. / Master of Science

Vehicle stops and speed variations account for a large percentage of vehicle fuel losses especially at signalized intersections. Recently, researchers have attempted to develop tools that reduce these losses by capitalizing on traffic signal information received via vehicle connectivity with traffic signal controllers. Existing state-of-the-art approaches, however, only consider surrogate measures (e.g. number of vehicle stops, time spent accelerating and decelerating, and/or acceleration or deceleration levels) in the objective function and fail to explicitly optimize vehicle fuel consumption levels. Furthermore, the majority of these models do not capture vehicle acceleration and deceleration limitations in addition to vehicle-to-vehicle interactions as constraints within the mathematical program. The connectivity between vehicles and infrastructure, as achieved through Connected Vehicles technology, can provide a vehicle with information that was not possible before. For example, information on traffic signal changes, traffic slow-downs and even headway and speed of lead vehicles can be shared. The research proposed in this dissertation uses this information and advanced computational models to develop fuel-efficient vehicle trajectories, which can either be used as guidance for drivers or can be attached to an electronic throttle controlled cruise control system. This fuel-efficient cruise control system is known as an Eco-Cooperative Adaptive Cruise Control (ECACC) system. In addition to the ECACC presented here, the research also expands on some of the key eco-driving concepts such as fuel-optimizing acceleration models, which could be used in conjunction with conventional vehicles and even autonomous vehicles, or assistive systems that are being implemented in vehicles. The dissertation first presents the results from an on-line survey soliciting driver input on public perceptions of in-vehicle assistive devices. The results of the survey indicate that user-acceptance to systems that enhance safety and efficiency is ranked high. Driver–willingness to use advanced in-vehicle technology and cellphone applications is also found to be subjective on what benefits it has to offer and safety and efficiency are found to be in the top list. The dissertation then presents the algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. The modeling of the system constitutes a modified state-of-the-art path-finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The results demonstrated savings of up to 30 percent in fuel consumption within the traffic signalized intersection vicinity. The proposed system was tested in an agent-based environment developed in MATLAB using the RPA car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The results showed how multi-vehicle interaction enhances usability of the system. Simulation of a calibrated real intersection showed average fuel savings of nearly 30 percent for peak volumes. The fuel reduction was high for low volumes and decreased as the traffic volumes increased. The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS) that incorporates the conventional TEXAS model with a new web-service interface as well as connected vehicle message set dictionary. This testing was able to demonstrate model corrections required to negate the effect of system latencies as well as a demonstration of using SAE message set parsing in a connected vehicle application. Finally, the dissertation develops an integrated framework for the control of autonomous vehicle movements through intersections using a multi-objective optimization algorithm. The algorithm integrated within an existing framework that minimizes vehicle delay while ensuring vehicles do not collide. A lower-level of control is introduced that minimizes vehicle fuel consumption subject to the arrival times assigned by the upper-level controller. Results show that the eco-speed control algorithm was able to reduce the overall fuel-consumption of autonomous vehicles passing through an intersection by 15 percent while maintaining the 80 percent saving in delay when compared to a traditional signalized intersection control. / Ph. D.

Most studies that tried to minimize carbon emissions from marine vessels have used the recycled waste heat generated from the propulsion engines to generate heating and minimize the electricity needed for the cooling systems. In this Master Thesis, a collaboration with Saab Kockums was conducted which had a goal to evaluate the possibilities to use waste heat from a cooling machine to create a more energy-efficient, sustainable and cost-efficient system for a surface vessel. The aim was to reduce carbon emissions and energy consumption without compromising the economic costs. But to also evaluate the advantages and disadvantages of using a cooling machine as a heating system for common areas and to examine the functionality and performance of such a system. This system would remove the current existing heating systems on a maritime platform and supplement the heating demand by recycling the waste heat from the cooling machines. The research questions posed were as follows: What structure would a system have that can integrate cooling and heating systems into a sustainable system? How can the functionality and performance of a cooling and heating system, designed with a focus on sustainability and economy, be evaluated both qualitatively and quantitatively? How will the functional flow of the concept operate under different operating conditions? What advantages and disadvantages will the concept have in different operating conditions? The research engineering methods applied in this thesis consists of interviews with system engineers, a design of a new heating battery, and a new concept generation. Furthermore, evaluation and systematic design drawing of the system’s connections to its subsystems and a functional flow diagram of the system are included. The result of this thesis shows that there is a possibility to use the water mixed with 20% glycol from the condenser side of a cooling machine with a temperature at 42℃ to provide heat to the platforms heating demands while maintaining its cooling functionality. The implementation would require some new pipelines and multiple valves between certain components so that the cooling machine would operate as a heat pump and/or as a cooling machine despite the weather conditions. Finally, a new heating battery would be needed and created. An operating year was determined to be 292 days and the system would switch between the two operating cases: summer- and winter-mode, in two geographical areas, the Mediterranean and the Baltic Sea. The new system compared to the current system of a Visby class corvette resulted in a reduction of carbon emissions up to 14%, 9% reduction in weight, and up to and 25% reduction in power usage. The fuel reduction cost is estimated up to 14%, for each operating year. / De flesta studier som har försökt minimera koldioxidutsläppen från marina fartyg har använt den återvunna restvärmen som genereras från framdrivningsmotorerna för att generera värme och minimera den elektricitet som behövs för kylsystemen. I detta arbete har ett samarbete tillsammans med Saab Kockums genomförts med syftet att undersöka möjligheterna att använda restvärmen från en kylmaskin för att skapa ett mer energieffektivt, hållbart och kostnadseffektivt system för ett ytfartyg. Målen var att minska koldioxidutsläppen och energianvändningen utan att kompromissa de ekonomiska kostnaderna, utreda för- och nackdelarna med att använda kylmaskinen som ett värmesystem för gemensamma utrymmen samt undersöka funktionaliteten och systemstrukturen för ett sådant system. Detta system skulle ersätta det existerande värmesystemet och komplettera värmebehovet genom att återvinna restvärmen från kylmaskinen. De forskningsfrågor som ställdes var följande, vilken struktur skulle ett system ha som kan integrera kyl- och värmesystemen till ett hållbart system? Hur kan funktionaliteten och prestandan hos ett kyl- och värmesystem, utformat med fokus på hållbarhet och ekonomi, utvärderas både kvalitativt och kvantitativt? Hur kommer det funktionella flödet i konceptet att fungera under olika driftfall? Vilka för- och nackdelar kommer konceptet att ha i olika driftfall? Metoderna som ingick i detta arbete var intervjuer med systemingenjörer, en design av ett nytt värmebatteri, konceptgenerering, utvärdering och systematisk designritning av systemens kopplingar till dess delsystem samt ett funktionellt flödesschema över systemet. Resultatet av detta arbete var att det är möjligt att använda en vattenblandning med 20 % glykol från kondensorsidan av en kylmaskin med en temperatur på 42 ℃ för att ge värme till plattformens värmebehov samtidigt som dess kylfunktion bibehålls. Det krävdes lite nya rörledningar och flera ventiler mellan vissa komponenter för att kylmaskinen skulle fungera som värmepump och/eller som kylmaskin trots väderförhållandena. Slutligen behövdes ett nytt värmebatteri skapas. Ett verksamhetsår fastställdes till 292 dagar och systemet kunde växla mellan två driftfall, sommar- och vinterläge i två geografiska områden, Medelhavet och Östersjön. Det nya systemet jämfört med det nuvarande systemet på en korvett, Visby-klassen, resulterade i en minskning av koldioxidutsläppen upp till 14 %, 9 % minskning av vikten, en minskning av effektförbrukningen upp till 25 % och en bränslereduktionskostnad upp till 14 % för varje verksamhetsår.

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. / The transportation sector places a huge burden on our environment and is one of the major emitters of pollutants. The resulting emissions have a negative impact on human health and could be a concern for national security. Therefore, policymakers are keen to develop models that accurately estimate the emissions from on-road vehicles. Microscopic emission models are capable of estimating the instantaneous vehicle emissions from operational-level projects, and policymakers can use them to evaluate their emission reduction plans and the environmental impact of transportation projects. However, the majority of the current existing models indicate that to achieve the optimum fuel economy, the driver should accelerate at full throttle and full braking for deceleration to minimize the acceleration and deceleration times. This assumption is obviously incorrect since it requires aggressive driving which will result in increasing the fuel consumption rate. Also, the models cannot use publicly accessible and available data to estimate the emissions which require expensive laboratory or field data collection. Consequently, this dissertation attempts to fill this gap in 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 follow the mentioned assumption of aggressive driving to minimize the fuel consumption as previously explained and can use publicly available vehicle and road pavement variables to estimate the emissions. Also, it utilizes US Environmental Protection Agency (EPA) city and highway the fuel economy ratings to calibrate its 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 PowerBased Fuel consumption and Emission Model (VT-CPFEM). The study proposes two models where the first model structure 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 VT-CPFM fuel estimates with the speed parameter. The additional speed term improves the accuracy of the model and can be used as a reference for the driving condition of the vehicle. The model framework is consistent in estimating the three emissions for LDVs and HDDTs. Moreover, the performance of the model was investigated in comparison with existing models to demonstrate the reliability of the model. Furthermore, the performance of the model was further evaluated by applying the model on driving cycles based on real-world driving conditions. The results demonstrate the capability of the model in generating accurate and reliable estimates based on the goodness of fit and error values for the three types of emissions.