Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control
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.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1375119
- Report Number(s):
- NREL/CP-5D00-69037
- Resource Relation:
- Conference: Presented at the 2017 IEEE Power and Energy Conference (PECI), 23-24 February 2017, Champaign, Illinois
- Country of Publication:
- United States
- Language:
- English
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