• Login
    • Submit
    View Item 
    •   Repository Home
    • UT Electronic Theses and Dissertations
    • UT Electronic Theses and Dissertations
    • View Item
    • Repository Home
    • UT Electronic Theses and Dissertations
    • UT Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Integrated network-based models for evaluating and optimizing the impact of electric vehicles on the transportation system

    Icon
    View/Open
    ZHANG-DISSERTATION.pdf (3.302Mb)
    Date
    2012-08
    Author
    Zhang, Ti
    Share
     Facebook
     Twitter
     LinkedIn
    Metadata
    Show full item record
    Abstract
    The adoption of plug-in electric vehicles (PEV) requires research for models and algorithms tracing the vehicle assignment incorporating PEVs in the transportation network so that the traffic pattern can be more precisely and accurately predicted. To attain this goal, this dissertation is concerned with developing new formulations for modeling travelling behavior of electric vehicle drivers in a mixed flow traffic network environment. Much of the work in this dissertation is motivated by the special features of PEVs (such as range limitation, requirement of long electricity-recharging time, etc.), and the lack of tools of understanding PEV drivers traveling behavior and learning the impacts of charging infrastructure supply and policy on the network traffic pattern. The essential issues addressed in this dissertation are: (1) modeling the spatial choice behavior of electric vehicle drivers and analyzing the impacts from electricity-charging speed and price; (2) modeling the temporal and spatial choices behavior of electric vehicle drivers and analyzing the impacts of electric vehicle range and penetration rate; (3) and designing the optimal charging infrastructure investments and policy in the perspective of revenue management. Stochastic traffic assignment that can take into account for charging cost and charging time is first examined. Further, a quasi-dynamic stochastic user equilibrium model for combined choices of departure time, duration of stay and route, which integrates the nested-Logit discrete choice model, is formulated as a variational inequality problem. An extension from this equilibrium model is the network design model to determine an optimal charging infrastructure capacity and pricing. The objective is to maximize revenue subject to equilibrium constraints that explicitly consider the electric vehicle drivers’ combined choices behavior. The proposed models and algorithms are tested on small to middle size transportation networks. Extensive numerical experiments are conducted to assess the performance of the models. The research results contain the author’s initiative insights of network equilibrium models accounting for PEVs impacted by different scenarios of charging infrastructure supply, electric vehicles characteristics and penetration rates. The analytical tools developed in this dissertation, and the resulting insights obtained should offer an important first step to areas of travel demand modeling and policy making incorporating PEVs.
    Department
    Civil, Architectural, and Environmental Engineering
    Description
    text
    Subject
    Electric vehicles
    Integrated model
    Stochastic traffic assignment
    Network design
    Pricing and capacity design
    Variational inequality
    Sensitivity analysis based optimization
    URI
    http://hdl.handle.net/2152/ETD-UT-2012-08-5960
    Collections
    • UT Electronic Theses and Dissertations
    University of Texas at Austin Libraries
    • facebook
    • twitter
    • instagram
    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
    • Site Policies
    • Web Accessibility Policy
    • Web Privacy Policy
    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin

    Browse

    Entire RepositoryCommunities & CollectionsDate IssuedAuthorsTitlesSubjectsDepartmentThis CollectionDate IssuedAuthorsTitlesSubjectsDepartment

    My Account

    Login

    Information

    AboutContactPoliciesGetting StartedGlossaryHelpFAQs

    Statistics

    View Usage Statistics
    University of Texas at Austin Libraries
    • facebook
    • twitter
    • instagram
    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
    • Site Policies
    • Web Accessibility Policy
    • Web Privacy Policy
    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin