Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS)
Name
Kolluri_2020_J._Electrochem._Soc._167_063505.pdf
Description
Published version
Size
2.13 MB
Format
Adobe PDF
Checksum (MD5)
7c704cbcf9d3cf060e81fc7f7c3cced7
Author(s)
Kolluri, Suryanarayana
Aduru, Sai Varun
Pathak, Manan
Braatz, Richard D
Subramanian, Venkat R
Date Issued
2020
Journal
Journal of the Electrochemical Society
Publisher
The Electrochemical Society
Version
Final published version
Abstract
© 2020 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. 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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1149/1945-7111/AB7BD7
