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Data-driven prediction of battery cycle life before capacity degradation

Author

Listed:
  • Kristen A. Severson

    (Massachusetts Institute of Technology)

  • Peter M. Attia

    (Stanford University)

  • Norman Jin

    (Stanford University)

  • Nicholas Perkins

    (Stanford University)

  • Benben Jiang

    (Massachusetts Institute of Technology)

  • Zi Yang

    (Stanford University)

  • Michael H. Chen

    (Stanford University)

  • Muratahan Aykol

    (Toyota Research Institute)

  • Patrick K. Herring

    (Toyota Research Institute)

  • Dimitrios Fraggedakis

    (Massachusetts Institute of Technology)

  • Martin Z. Bazant

    (Massachusetts Institute of Technology)

  • Stephen J. Harris

    (Stanford University
    Materials Science Division, Lawrence Berkeley National Lab)

  • William C. Chueh

    (Stanford University)

  • Richard D. Braatz

    (Massachusetts Institute of Technology)

Abstract

Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems.

Suggested Citation

  • Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    • Handle: RePEc:nat:natene:v:4:y:2019:i:5:d:10.1038_s41560-019-0356-8
    DOI: 10.1038/s41560-019-0356-8
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