Data-driven prognostics of lithium-ion rechargeable battery using bilinear kernel regression

Charlie Hubbard, John Bavlsik, Chinmay Hegde, Chao Hu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Reliability of lithium-ion (Li-ion) rechargeable batteries has been recognized as of high importance from a broad range of stakeholders, including battery manufacturers, manufacturers of battery-powered devices, regulatory agencies, researchers, and the public. Assessing the current and future health of Liion batteries is essential to ensure the batteries operate safely and reliably throughout their lifetime. This paper presents a new data-driven approach for prediction of battery remaining useful life (RUL) in the presence of corruptions (or errors) in capacity features. The approach leverages bilinear kernel regression to build a nonlinear mapping between the capacity feature space and the RUL state space. Specific innovations of the approach include: i) a general framework for robust sparse prognostics that effectively incorporates sparsity into kernel regression and implicitly compensates for errors in capacity features; and ii) two numerical procedures for error estimation that efficiently derives optimal values of the regression model parameters. Results of 10 years' continuous cycling test on Li-ion prismatic cells suggest that the proposed approach achieves robust RUL prediction despite random noise in the capacity features.

    Original languageEnglish (US)
    Title of host publicationPHM 2016 - Proceedings of the Annual Conference of the Prognostics and Health Management Society
    EditorsMatthew J. Daigle, Anibal Bregon
    PublisherPrognostics and Health Management Society
    Pages200-208
    Number of pages9
    ISBN (Electronic)9781936263059
    StatePublished - 2016
    Event2016 Annual Conference of the Prognostics and Health Management Society, PHM 2016 - Denver, United States
    Duration: Oct 3 2016Oct 6 2016

    Publication series

    NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
    Volume2016-October
    ISSN (Print)2325-0178

    Conference

    Conference2016 Annual Conference of the Prognostics and Health Management Society, PHM 2016
    CountryUnited States
    CityDenver
    Period10/3/1610/6/16

    Keywords

    • Bilinear kernel regression
    • Lithium-ion battery
    • Prognostics
    • Remaining useful life

    ASJC Scopus subject areas

    • Information Systems
    • Electrical and Electronic Engineering
    • Health Information Management
    • Computer Science Applications

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