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dc.contributor.authorFedor, Craig Stevenen_US
dc.description.abstractAutomotive vehicle manufacturers have been facing increased pressures from legislative bodies and consumers to reduce the fuel consumption and harmful emissions of their newly produced vehicles as a result of new research showing the detrimental effects these emissions have on the environment. These pressures are encouraging manufactures and researchers to invest billions of dollars into the development of new advanced vehicle technologies. Some of these investments have resulted in substantial progress in powertrain technologies that have led to the preliminary adoption of electrified powertrain vehicles. Other areas of research are actively working to reduce the energy consumption of a vehicle, regardless of its powertrain, by influencing driver behavior and by optimizing the way a vehicle travels between an origin and destination. This intelligent vehicle routing is done by analyzing a range of possible routes and selecting the route that consumes the least amount of fuel. An accurate method for predetermining vehicle energy expenditure along a given route before it is driven is needed to effectively implement intelligent vehicle routing systems. One common method is the generation of a road network-wide database with energy use figures for each section of road. This method requires expensive experimentation trials or network simulation software. Individual-level vehicle predictive energy estimation eliminates the need for costly fuel use generation by utilizing vehicle velocity generation techniques and vehicle powertrain models. Estimation of individual vehicle energy consumption along a route is done by identifying an origin-destination pair, detecting required full-stops along the path, and synthesizing multiple stop-to-stop velocity modes between each set of stops. The resulting velocity profile is paired with a specific vehicle powertrain model to determine fuel consumption. A drawback of this route generation technique is that the vehicle path is assumed to be one-dimensional and lacks inclusion of road curves and their associated velocity changes to maintain passenger comfort. This thesis evaluates the merit of discounting road curves in predictive vehicle energy consumption analyses and presents a technique for modeling common road corners that require velocity changes to limit passenger discomfort. The resulting corner synthesis method is combined with a validated vehicle powertrain model to complete full route consumption modeling. Two routes, an urban and highway, are modeled and driven to evaluate the accuracy of the full simulation model when compared with on-road data. The results show that corners can largely be ignored during energy consumption analysis for highways. The cornering effects on a vehicle during urban driving, however, should be included in urban route analyses with multiple road curves. Inclusion of the cornering effects during an example urban route analysis decreased the error between the on-road consumption data and the simulation results.en_US
dc.publisherVirginia Techen_US
dc.rightsIn Copyrighten
dc.subjectbattery electric vehicleen_US
dc.subjectroad curvesen_US
dc.subjectenergy consumptionen_US
dc.subjectlateral accelerationen_US
dc.titleSynthesizing Vehicle Cornering Modes for Energy Consumption Analysisen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineMechanical Engineeringen_US
dc.contributor.committeechairNelson, Douglas J.en_US
dc.contributor.committeememberRakha, Hesham A.en_US
dc.contributor.committeememberLeonessa, Alexanderen_US

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