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

Saurabh Saxena, Darius Roman, Valentin Robu, David Flynn and Michael Pecht
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Saurabh Saxena: Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
Darius Roman: Smart Systems Group, School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Valentin Robu: Smart Systems Group, School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
David Flynn: Smart Systems Group, School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Michael Pecht: Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA

Energies, 2021, vol. 14, issue 3, 1-17

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.

Keywords: lithium-ion batteries; cycle life; temperature; C-rate; accelerated testing; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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