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: Contribution to journalArticlepeer-review

    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 letter 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 letter establishes a general mathematical foundation for the incorporation of electrochemically rich reduced-order models directly into an MPC framework.

    Original languageEnglish (US)
    Article number9259035
    Pages (from-to)1387-1392
    Number of pages6
    JournalIEEE Control Systems Letters
    Volume5
    Issue number4
    DOIs
    StatePublished - Oct 2021

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Control and Optimization

    Fingerprint Dive into the research topics of 'A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model'. Together they form a unique fingerprint.

    Cite this