期刊
NATURE ENERGY
卷 4, 期 5, 页码 383-391出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41560-019-0356-8
关键词
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资金
- Toyota Research Institute through the Accelerated Materials Design and Discovery programme
- Thomas V. Jones Stanford Graduate Fellowship
- National Science Foundation [DGE-114747]
- SAIC Innovation Center through Stanford Energy 3.0 industry affiliates programme
- Vehicle Technologies Office of the US Department of Energy under the Advanced Battery Materials Research Program
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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