A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model

Marcelo A. Xavier, Aloisio K. De Souza, Kiana Karami, Gregory L. Plett, M. Scott Trimboli

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

    Abstract

    Most state-of-the art battery-control strategies rely on voltage-based design limits to address performance and lifetime concerns. Such approaches are inherently conservative. However, by exploiting internal electrochemical quantities, it is possible to control battery performance right up to true physical bounds. This paper develops an extensible framework that combines model predictive control (MPC) with computationally efficient realization algorithm (xRA)-generated reduced-order electrochemical models for the advanced control of lithium-ion batteries. The approach is demonstrated on the fast-charge problem where hard constraints are imposed on problem variables to avoid lithium plating induced performance degradation. This work establishes a general mathematical foundation for the incorporation of electrochemically rich reduced-order models directly into an MPC framework.

    Original languageEnglish (US)
    Title of host publication2021 American Control Conference, ACC 2021
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages614-619
    Number of pages6
    ISBN (Electronic)9781665441971
    DOIs
    StatePublished - May 25 2021
    Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
    Duration: May 25 2021May 28 2021

    Publication series

    NameProceedings of the American Control Conference
    Volume2021-May
    ISSN (Print)0743-1619

    Conference

    Conference2021 American Control Conference, ACC 2021
    Country/TerritoryUnited States
    CityVirtual, New Orleans
    Period5/25/215/28/21

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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