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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:
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  • 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 modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less
  • NREL's Energy Storage team is exploring the effect of mechanical crush of lithium ion cells on their thermal and electrical safety. PHEV cells, fresh as well as ones aged over 8 months under different temperatures, voltage windows, and charging rates, were subjected to destructive physical analysis. Constitutive relationship and failure criteria were developed for the electrodes, separator as well as packaging material. The mechanical models capture well, the various modes of failure across different cell components. Cell level validation is being conducted by Sandia National Laboratories.
  • Utilizing an existing macro-homogeneous porous electrode model developed by John Newman, this talk presents the potential energy density gains that can be realized in lithium-ion battery electrodes fabricated with co-extrusion (CoEx) technology. CoEx uses carefully engineered fluidic channels to cause multiple streams of dissimilar fluids to impart shape to one another. The result is a high-speed, continuous deposition process that can create fine linear structures much smaller than the smallest physical feature within the printhead. By eliminating the small channels necessary for conventional extrusion and injection processes, CoEx is able to deposit highly loaded and viscous pastes at high linemore » speeds under reasonable operating pressures. The CoEx process is capable of direct deposition of features as small as 10 μm with aspect ratios of 5 or greater, and print speeds > 80 ft/min. We conduct an analysis on two-dimensional cathode cross-sections in COMSOL and present the electrochemical performance results, including calculated volumetric energy capacity for Lithium Nickel Manganese Cobalt Oxide (NMC) co-extruded cathodes, in the presence of a lithium metal anode, polymer separator and ethylene carbonate–diethyl carbonate (EC:DEC) liquid electrolyte. The impact of structured electrodes on cell performance is investigated by varying the physical distribution of a fixed amount of cathode mass over a space of dimensions which can be fabricated by CoEx. By systematically varying the thickness and aspect ratio of the electrode structures, we present an optimal subset of geometries and design rules for co-extruded geometries. Modeling results demonstrate that NMC CoEx cathodes, on the order of 125-200 µm thick, can garner an improvement in material utilization and in turn capacity through the addition of fine width electrolyte channels or highly conductive electrode regions. We also present initial experimental results on CoEx NMC cathode structures.« less