Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS)
Journal Article
·
· Journal of the Electrochemical Society (Online)
- Univ. of Texas, Austin, TX (United States). Texas Materials Inst. Walker Department of Mechanical Engineering & Material Science Engineering; BattGenie Inc., Seattle, WA (United States)
- BattGenie Inc., Seattle, WA (United States)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
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.
- 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 (Online), Vol. 167, Issue 6; ISSN 1945-7111
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 31 works
Citation information provided by
Web of Science
Web of Science
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