Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    BATTERY STATE OF CHARGE ESTIMATION BASED ON DATA-DRIVEN MODELS WITH MOVING WINDOW FILTERS AND PHYSICS-BASED MODELS WITH EFFICIENT SOLID-PHASE DIFFUSION PDES SOLVED BY THE OPTIMIZED PROJECTION METHOD

    View/Open
    He_umd_0117E_19000.pdf (2.616Mb)
    (RESTRICTED ACCESS)
    No. of downloads: 0

    Date
    2018
    Author
    He, Wei
    Advisor
    Pecht, Michael
    DRUM DOI
    https://doi.org/10.13016/M2G15TF3F
    Metadata
    Show full item record
    Abstract
    State of charge (SOC) estimation is one of the most important functions of battery management systems (BMSs), which is defined as the percentage of the remaining charge inside the battery to its maximum capacity. SOC indicates when the battery needs to be recharged. It is necessary for many battery management applications, for example, charge/discharge control, remaining useful time/ driving range predictions, and battery power capability estimations. Inaccurate SOC estimations can lead to user dissatisfaction, mission failures, and premature battery failures. This thesis focuses on the development of advanced battery models and algorithms for SOC estimations. Two SOC estimation approaches are investigated, including electrochemical models and data-driven models. Electrochemical models have intrinsic advantages for SOC estimation since it can relate battery internal physical parameters, e.g. lithium concentrations, to SOC. However, the computational complexity of the electrochemical model is the major obstacle for its application in a real-time BMS. To address this problem, an efficient solution for the solid phase diffusion equations in the electrochemical model is developed based on projection with optimized basis functions. The developed method generates 20 times fewer equations compared with finite difference-based methods, without losing accuracy. The results also show that the developed method is three times more efficient compared with the conventional projection-based method. Then, a novel moving window filter (MWF) algorithm is developed to infer SOC based on the electrochemical model. MWF converges to true values nearly 15 times faster compared with unscented Kalman filter in experimental test cases. This work also develops a data-driven SOC estimation approach. Traditional data-driven approaches, e.g. neural network, have generalization problems. For example, the model over-fits to training data and generate erroneous results in the testing data. This thesis investigates algorithms to improve the generalization capability of the data-driven model. An algorithm is developed to select optimal neural network structure and training data inputs. Then, a hybrid approach is developed by combining the neural network and MWF to provide stable SOC estimations. The results show that the SOC estimation error can be reduced from 8% to less 4% compared with the original neural network approach.
    URI
    http://hdl.handle.net/1903/21118
    Collections
    • Mechanical Engineering Theses and Dissertations
    • UMD Theses and Dissertations

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility