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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Data availability

The datasets used in this study are available at https://data.matr.io/1.

Code availability

Code for data processing is available at https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation. Code for the modelling work is available from the corresponding authors upon request.

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This work was supported by Toyota Research Institute through the Accelerated Materials Design and Discovery programme. P.M.A. was supported by the Thomas V. Jones Stanford Graduate Fellowship and the National Science Foundation Graduate Research Fellowship under Grant No. DGE-114747. N.P. was supported by SAIC Innovation Center through Stanford Energy 3.0 industry affiliates programme. S.J.H. was supported by the Assistant Secretary for Energy Efficiency, Vehicle Technologies Office of the US Department of Energy under the Advanced Battery Materials Research Program. We thank E. Reed, S. Ermon, Y. Li, C. Bauemer, A. Grover, T. Markov, D. Deng, A. Baclig and H. Thaman for discussions.

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Author notes

  1. These authors contributed equally: K. A. Severson, P. M. Attia.


  1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Kristen A. Severson
    • , Benben Jiang
    • , Dimitrios Fraggedakis
    • , Martin Z. Bazant
    •  & Richard D. Braatz
  2. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

    • Peter M. Attia
    • , Norman Jin
    • , Nicholas Perkins
    • , Zi Yang
    • , Michael H. Chen
    • , Stephen J. Harris
    •  & William C. Chueh
  3. Toyota Research Institute, Los Altos, CA, USA

    • Muratahan Aykol
    •  & Patrick K. Herring
  4. Materials Science Division, Lawrence Berkeley National Lab, Berkeley, CA, USA

    • Stephen J. Harris


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P.M.A., N.J., N.P., M.H.C. and W.C.C. conceived of and conducted the experiments. K.A.S., Z.Y. and B.J. performed the modelling. M.A., Z.Y. and P.K.H. performed data management. P.M.A., K.A.S., N.J., B.J., D.F., M.Z.B., S.J.H., W.C.C. and R.D.B. interpreted the results. All authors edited and reviewed the manuscript. W.C.C. and R.D.B. supervised the work.

Competing interests

K.A.S., R.D.B., W.C.C., P.M.A., N.J., S.J.H. and N.P. have filed a patent related to this work: US Application No. 62/575,565, dated 16 October 2018.

Corresponding authors

Correspondence to William C. Chueh or Richard D. Braatz.

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