Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control [electronic resource] : Preprint
- 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
United States. Department of Energy. Office of Scientific and Technical Information
- 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.
- Published through SciTech Connect.
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:
View MARC record | catkey: 23761191