Back to Search Start Over

Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning

Authors :
Saurabh Saxena
Darius Roman
Valentin Robu
David Flynn
Michael Pecht
Source :
Energies, Vol 14, Iss 3, p 723 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.

Details

Language :
English
ISSN :
19961073
Volume :
14
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Energies
Publication Type :
Academic Journal
Accession number :
edsdoj.8fcbe70150a044d598f32e88635e327c
Document Type :
article
Full Text :
https://doi.org/10.3390/en14030723