• Login
    View Item 
    •   DSpace Home
    • AUS Theses & Dissertations
    • Masters Theses
    • View Item
    •   DSpace Home
    • AUS Theses & Dissertations
    • Masters Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Adaptive Estimation of Li-ion Battery Model Parameters

    Thumbnail
    View/ Open
    35.232-2016.19 Daniyal Ali.pdf (17.16Mb)
    35.232-2016.19 Daniyal Ali_Compressed.pdf (1.756Mb)
    Date
    2016-05
    Author
    Ali, Daniyal
    Advisor(s)
    Mukhopadhyay, Shayok
    Rehman, Habib-ur
    Type
    Thesis
    Metadata
    Show full item record
    Description
    A Master of Science thesis in Electrical Engineering by Daniyal Ali entitled, "Adaptive Estimation of Li-ion Battery Model Parameters," submitted in May 2016. Thesis advisor is Dr. Shayok Mukhopadhyay and thesis co-advisor is Dr. Habib-ur Rehman. Soft and hard copy available.
    Abstract
    This work presents a novel application of a high gain adaptive observer-based technique for Lithium-ion (Li-ion) battery modeling. The model used in this work was originally developed by Chen and Mora. However, in Chen and Mora's original work, the parameters required for the battery model were estimated through intensive experimentation. In contrast, this work presents an adaptive observer for estimating the battery model parameters. This results in the reduction of experimental effort required to estimate battery model parameters. The selected model (Chen and Mora's model) requires twenty one parameters to accurately model a Li-ion battery. This work initially proposes three variations of a high gain adaptive observer-based technique to adaptively tune fifteen of the required parameters accurately. The remaining six parameters related to the shape of the no-load electromotive-force (EMF) curve are obtained via a voltage relaxation test. Based on observations made during simulations of the above proposed techniques, an improved estimation technique is proposed in the latter half of this document, and experimental results validating the proposed technique are presented. Experiments show that the model obtained through this technique is independent of the magnitude and type of load. The improved parameter estimation technique is justified using rigorous mathematical analysis. The proposed improved technique can be used either online or offline for estimating battery model parameters. This may be valuable for automatically updating battery models parameters on-board future smart vehicles in real time.
    DSpace URI
    http://hdl.handle.net/11073/8327
    Collections
    • Masters Theses

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsCollege/DeptArchive ReferenceSeriesThis CollectionBy Issue DateAuthorsTitlesSubjectsCollege/DeptArchive ReferenceSeries

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    DSpace software copyright © 2002-2016  DuraSpace
    Submission Policies | Terms of Use | Takedown Policy | Privacy Policy | About Us | Contact Us | Send Feedback

    Return to AUS
    Theme by 
    Atmire NV