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

Saurabh Saxena, Darius Roman, Valentin Robu, David Flynn, Michael Pecht

Research output: Contribution to journalArticlepeer-review

26 Downloads (Pure)

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.
Original languageEnglish
Article number723
JournalEnergies
Volume14
Issue number3
DOIs
Publication statusPublished - 30 Jan 2021

Fingerprint Dive into the research topics of 'Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning'. Together they form a unique fingerprint.

Cite this