Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

Kyri Baker, Ying Shi, Dane Christensen, Emma Raszmann

Research output: Contribution to conferencePaper


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.
Original languageAmerican English
Number of pages9
StatePublished - 2017
EventIEEE Power and Energy Conference - Champaign, Illinois
Duration: 23 Feb 201724 Feb 2017


ConferenceIEEE Power and Energy Conference
CityChampaign, Illinois

NREL Publication Number

  • NREL/CP-5D00-67809


  • analytical models
  • batteries
  • buildings
  • lithium-ion
  • modeling
  • optimization
  • system integration


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