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

Title: Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System

Abstract

Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4% RMS error and resistance growth with 15% RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.

Authors:
 [1];  [1];  [1];  [1];  [2];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. SunPower Corporation
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1390498
Report Number(s):
NREL/PR-5400-68759
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 American Control Conference, 23-26 May 2017, Seattle, Washington
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; 29 ENERGY PLANNING, POLICY, AND ECONOMY; lithium-ion battery; energy storage; life; lifetime; aging; modeling; reliability; photovoltaics

Citation Formats

Smith, Kandler A, Saxon, Aron R, Keyser, Matthew A, Lundstrom, Blake R, Cao, Ziwei, and Roc, Albert. Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System. United States: N. p., 2017. Web. doi:10.23919/ACC.2017.7963578.
Smith, Kandler A, Saxon, Aron R, Keyser, Matthew A, Lundstrom, Blake R, Cao, Ziwei, & Roc, Albert. Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System. United States. doi:10.23919/ACC.2017.7963578.
Smith, Kandler A, Saxon, Aron R, Keyser, Matthew A, Lundstrom, Blake R, Cao, Ziwei, and Roc, Albert. Wed . "Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System". United States. doi:10.23919/ACC.2017.7963578. https://www.osti.gov/servlets/purl/1390498.
@article{osti_1390498,
title = {Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System},
author = {Smith, Kandler A and Saxon, Aron R and Keyser, Matthew A and Lundstrom, Blake R and Cao, Ziwei and Roc, Albert},
abstractNote = {Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4% RMS error and resistance growth with 15% RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.},
doi = {10.23919/ACC.2017.7963578},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Sep 06 00:00:00 EDT 2017},
month = {Wed Sep 06 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: