Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control

Kyri Baker, Ying Shi, Dane Christensen, Emma Raszmann

Research output: Contribution to conferencePaperpeer-review

46 Scopus Citations

Abstract

Accurately modeling stationary battery storage behavior is crucial to pursuing cost-effective distributed energy resource opportunities. In this paper, a lithium-ion battery model was derived for building-integrated battery use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. To achieve these goals, a mixed modeling approach incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. The proposed battery model is validated through comparison to manufacturer data. Additionally, a dynamic test case demonstrates the effects of using regression models to represent cycling losses and capacity fading. A proof-of-concept optimization test case with time-of-use pricing is performed to demonstrate how the battery model could be included in an optimization framework.

Original languageAmerican English
Number of pages7
DOIs
StatePublished - 30 May 2017
Event2017 IEEE Power and Energy Conference at Illinois, PECI 2017 - Urbana, United States
Duration: 23 Feb 201724 Feb 2017

Conference

Conference2017 IEEE Power and Energy Conference at Illinois, PECI 2017
Country/TerritoryUnited States
CityUrbana
Period23/02/1724/02/17

Bibliographical note

See NREL/CP-5D00-67809 for preprint

NREL Publication Number

  • NREL/CP-5D00-69037

Keywords

  • Analytical Models
  • Batteries
  • Buildings
  • Energy Storage
  • Lithium-Ion
  • Modeling
  • Optimization
  • System Integration

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