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Title: Predictive Models of Li-ion Battery Lifetime (Presentation)

Abstract

Predictive models of Li-ion battery reliability must consider a multiplicity of electrochemical, thermal and mechanical degradation modes experienced by batteries in application environments. Complicating matters, Li-ion batteries can experience several path dependent degradation trajectories dependent on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. Lacking accurate models and tests, lifetime uncertainty must be absorbed by overdesign and warranty costs. Degradation models are needed that predict lifetime more accurately and with less test data. Models should also provide engineering feedback for next generation battery designs. This presentation reviews both multi-dimensional physical models and simpler, lumped surrogate models of battery electrochemical and mechanical degradation. Models are compared with cell- and pack-level aging data from commercial Li-ion chemistries. The analysis elucidates the relative importance of electrochemical and mechanical stress-induced degradation mechanisms in real-world operating environments. Opportunities for extending the lifetime of commercial battery systems are explored.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
DOE/EERE Other
OSTI Identifier:
1156987
Report Number(s):
NREL/PR-5400-62813
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at IEEE Conference on Reliability Science for Advanced Materials and Devices, 7-9 September 2014, Golden, Colorado; Related Information: NREL (National Renewable Energy Laboratory)
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; 33 ADVANCED PROPULSION SYSTEMS; BATTERY; LITHIUM-ION; LI-ION; BATTERY LIFE; MULTI-DIMENSIONAL MODEL; BATTERY DEGRADATION

Citation Formats

Smith, K., Wood, E., Santhanagopalan, S., Kim, G., Shi, Y., and Pesaran, A. Predictive Models of Li-ion Battery Lifetime (Presentation). United States: N. p., 2014. Web.
Smith, K., Wood, E., Santhanagopalan, S., Kim, G., Shi, Y., & Pesaran, A. Predictive Models of Li-ion Battery Lifetime (Presentation). United States.
Smith, K., Wood, E., Santhanagopalan, S., Kim, G., Shi, Y., and Pesaran, A. Mon . "Predictive Models of Li-ion Battery Lifetime (Presentation)". United States. doi:. https://www.osti.gov/servlets/purl/1156987.
@article{osti_1156987,
title = {Predictive Models of Li-ion Battery Lifetime (Presentation)},
author = {Smith, K. and Wood, E. and Santhanagopalan, S. and Kim, G. and Shi, Y. and Pesaran, A.},
abstractNote = {Predictive models of Li-ion battery reliability must consider a multiplicity of electrochemical, thermal and mechanical degradation modes experienced by batteries in application environments. Complicating matters, Li-ion batteries can experience several path dependent degradation trajectories dependent on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. Lacking accurate models and tests, lifetime uncertainty must be absorbed by overdesign and warranty costs. Degradation models are needed that predict lifetime more accurately and with less test data. Models should also provide engineering feedback for next generation battery designs. This presentation reviews both multi-dimensional physical models and simpler, lumped surrogate models of battery electrochemical and mechanical degradation. Models are compared with cell- and pack-level aging data from commercial Li-ion chemistries. The analysis elucidates the relative importance of electrochemical and mechanical stress-induced degradation mechanisms in real-world operating environments. Opportunities for extending the lifetime of commercial battery systems are explored.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Sep 01 00:00:00 EDT 2014},
month = {Mon Sep 01 00:00:00 EDT 2014}
}

Conference:
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