Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS)

Kolluri, Suryanarayana; Aduru, Sai Varun; Pathak, Manan; Braatz, Richard D.; Subramanian, Venkat R.

doi:10.1149/1945-7111/ab7bd7

Title: Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS)

Journal Article · Mon Apr 06 00:00:00 EDT 2020 · Journal of the Electrochemical Society

DOI:https://doi.org/10.1149/1945-7111/ab7bd7· OSTI ID:1608383

; Aduru, Sai Varun; Pathak, Manan;

;

Optimal operation of lithium-ion batteries requires robust battery models for advanced battery management systems (ABMS). A nonlinear model predictive control strategy is proposed that directly employs the pseudo-two-dimensional (P2D) model for making predictions. Using robust and efficient model simulation algorithms developed previously, the computational time of the nonlinear model predictive control algorithm is quantified, and the ability to use such models for nonlinear model predictive control for ABMS is established.

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Research Organization:: Univ. of Washington, Seattle, WA (United States); Battelle Memorial Institute, Columbus, OH (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0000275; AC05-76RL01830

OSTI ID:: 1608383

Alternate ID(s):: OSTI ID: 1798986

Journal Information:: Journal of the Electrochemical Society, Journal Name: Journal of the Electrochemical Society Vol. 167 Journal Issue: 6; ISSN 0013-4651

Publisher:: The Electrochemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 31 works

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