Reduction of high fidelity lithium-ion battery model via data-driven system identification

Malgorzata Sumislawska, Navneesh Phillip, M.M. Marinescu, Keith Burnham

    Research output: Chapter in Book/Report/Conference proceedingChapter

    1 Citation (Scopus)
    57 Downloads (Pure)


    The battery management system of a hybrid electric vehicle requires a computationally simple yet accurate model of the battery. In this paper a reduced order battery model is developed using a stochastic top-down approach. Firstly a pseudo2D, multi-particle electrochemical model, considered as a surrogate for the real system, is used to obtain the observational data. Then the model structure is inferred directly from the data. The dependencies between the states and the model parameters are analysed, which results in a 5th order piecewise state dependent parameter model which can describe the nonlinear relationship between the current, the voltage and the state of charge of the battery.
    Original languageEnglish
    Title of host publication: IET Hybrid and Electric Vehicles Conference 2013 (HEVC 2013)
    Place of PublicationLondon, UK
    PublisherInstitution of Engineering and Technology
    Volume621 CP
    ISBN (Print)9781849197762
    Publication statusPublished - Nov 2013

    Bibliographical note

    The paper was given at the Hybrid and Electric Vehicles Conference 2013 (HEVC 2013)] , 6-7 Nov 2013 in London, UK.
    © 2014 The Institution of Engineering and Technology (IET)


    • stochastic processes
    • electrochemical analysis
    • battery powered vehicles
    • secondary cells
    • battery management systems
    • hybrid electric vehicles


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