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

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  • Saurabh Saxena

    (Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
    Current address: Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL 60439, 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
    Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands)

  • 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)

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.

Suggested Citation

  • Saurabh Saxena & Darius Roman & Valentin Robu & David Flynn & Michael Pecht, 2021. "Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning," Energies, MDPI, vol. 14(3), pages 1-17, January.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:723-:d:490181
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    References listed on IDEAS

    as
    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    2. Wenshuo Tang & Darius Roman & Ross Dickie & Valentin Robu & David Flynn, 2020. "Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems," Energies, MDPI, vol. 13(18), pages 1-29, September.
    3. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
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    Cited by:

    1. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    2. Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Xiaowei Hao, 2023. "Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    3. Weiping Diao & Chetan Kulkarni & Michael Pecht, 2021. "Development of an Informative Lithium-Ion Battery Datasheet," Energies, MDPI, vol. 14(17), pages 1-19, September.
    4. Haber, Marc & Azaïs, Philippe & Genies, Sylvie & Raccurt, Olivier, 2023. "Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage," Applied Energy, Elsevier, vol. 343(C).
    5. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.

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