• Login
    View Item 
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Uncertainty management in prognosis of electric vehicle energy system

    Thumbnail
    View/Open
    CHO-DISSERTATION-2018.pdf (7.070Mb)
    Date
    2018-11-15
    Author
    Cho, HwanJune
    Metadata
    Show full item record
    Abstract
    The body of work described here seeks to understand uncertainties that are inherent in the system prognosis procedure, to represent and propagate them, and to manage or shrink uncertainty distribution bounds under long-term and usage-based prognosis for accurate and precise results. Uncertainty is an inherent attribute of prognostic technologies, in which we estimate the End-Of-Life (EOL) and Remaining-Useful-Life (RUL) of a failing component or system, with the time evolution of the incipient failure increasing the uncertainty bounds as the fault horizon also increases. In the given testbed case, the life of the electric vehicle energy system is not measurable. It is only estimated, thereby increasing the importance of uncertainty management. Therefore, methods are needed to handle this uncertainty appropriately in order to improve the accuracy and precision of prognosis via shrinking the uncertainty bounds. To this end, this thesis introduces novel methodologies for the RUL prognosis then the enabling technologies build upon a three-tiered architecture that aims to shrink EOL/RUL bounds: uncertainty representation, uncertainty propagation, and uncertainty management.
    URI
    http://hdl.handle.net/1853/60797
    Collections
    • Georgia Tech Theses and Dissertations [23877]
    • School of Electrical and Computer Engineering Theses and Dissertations [3381]

    Browse

    All of SMARTechCommunities & CollectionsDatesAuthorsTitlesSubjectsTypesThis CollectionDatesAuthorsTitlesSubjectsTypes

    My SMARTech

    Login

    Statistics

    View Usage StatisticsView Google Analytics Statistics
    facebook instagram twitter youtube
    • My Account
    • Contact us
    • Directory
    • Campus Map
    • Support/Give
    • Library Accessibility
      • About SMARTech
      • SMARTech Terms of Use
    Georgia Tech Library266 4th Street NW, Atlanta, GA 30332
    404.894.4500
    • Emergency Information
    • Legal and Privacy Information
    • Human Trafficking Notice
    • Accessibility
    • Accountability
    • Accreditation
    • Employment
    © 2020 Georgia Institute of Technology