Data-driven prediction of battery cycle life before capacity degradation

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, Stephen J. Harris, William C. Chueh () and Richard D. Braatz ()
Additional contact information
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
William C. Chueh: Stanford University
Richard D. Braatz: Massachusetts Institute of Technology

Nature Energy, 2019, vol. 4, issue 5, 383-391

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41560-019-0356-8

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