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

20 Citations (Scopus)
197 Downloads (Pure)


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
Issue number3
Early online date30 Jan 2021
Publication statusPublished - 1 Feb 2021


  • Accelerated testing
  • C-rate
  • Cycle life
  • Lithium-ion batteries
  • Machine learning
  • Temperature

ASJC Scopus subject areas

  • Control and Optimization
  • Energy (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment


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