skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Pittsburgh
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1375119
Report Number(s):
NREL/CP-5D00-69037
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
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
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; batteries; lithium-ion; modeling; analytical models; system integration; buildings; optimization

Citation Formats

Baker, Kyri A, Shi, Ying, Christensen, Dane T, and Raszmann, Emma. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control. United States: N. p., 2017. Web. doi:10.1109/PECI.2017.7935755.
Baker, Kyri A, Shi, Ying, Christensen, Dane T, & Raszmann, Emma. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control. United States. doi:10.1109/PECI.2017.7935755.
Baker, Kyri A, Shi, Ying, Christensen, Dane T, and Raszmann, Emma. Thu . "Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control". United States. doi:10.1109/PECI.2017.7935755.
@article{osti_1375119,
title = {Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control},
author = {Baker, Kyri A and Shi, Ying and Christensen, Dane T and Raszmann, Emma},
abstractNote = {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.},
doi = {10.1109/PECI.2017.7935755},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jun 01 00:00:00 EDT 2017},
month = {Thu Jun 01 00:00:00 EDT 2017}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: