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Open Access Publications from the University of California

Data-driven prediction of battery cycle life before capacity degradation

  • Author(s): Severson, Kristen A
  • Attia, Peter M
  • Jin, Norman
  • Perkins, Nicholas
  • Jiang, Benben
  • Yang, Zi
  • Chen, Michael H
  • Aykol, Muratahan
  • Herring, Patrick K
  • Fraggedakis, Dimitrios
  • Bazant, Martin Z
  • Harris, Stephen J
  • Chueh, William C
  • Braatz, Richard D
  • et al.

© 2019, The Author(s), under exclusive licence to Springer Nature Limited. 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.

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