Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control [electronic resource] : Preprint
- Published:
- Washington, D.C. : United States. Office of the Assistant Secretary of Energy Efficiency and Renewable Energy, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy - Physical Description:
- 1.8 MB : digital, PDF file
- Additional Creators:
- National Renewable Energy Laboratory (U.S.), United States. Office of the Assistant Secretary of Energy Efficiency and Renewable Energy, and United States. Department of Energy. Office of Scientific and Technical Information
Access Online
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Summary:
- Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.
- Report Numbers:
- E 1.99:nrel/cp-5d00-67809
nrel/cp-5d00-67809 - Subject(s):
- Other Subject(s):
- Note:
- Published through SciTech Connect.
02/22/2017.
"nrel/cp-5d00-67809"
To be presented at the IEEE Power and Energy Conference, 23-24 February 2017, Champaign, Illinois.
Raszmann, Emma; Baker, Kyri; Shi, Ying; Christensen, Dane. - Funding Information:
- AC36-08GO28308
View MARC record | catkey: 23761191