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| 摘要： 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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图1 High performance of features based on voltage curves from the first 100 cycles. a, Discharge capacity curves for 100th and 10th cycles for a representative cell. b, Difference of the discharge capacity curves as a function of voltage between the 100th and 10th cycles, ΔQ100-10(V), for 124 cells. c, Cycle life plotted as a function of the variance of ΔQ100-10(V) on a log–log axis, with a correlation coefficient of ?0.93. | 
图2 Observed and predicted cycle lives for several implementations of the feature-based model. a, ‘Variance’ model using only the log variance of ΔQ100-10(V). b, ‘Discharge’ model using six features based only on discharge cycle information, described in Supplementary Table 1. c, ‘Full’ model using the nine features described in Supplementary Table 1. | 引文信息： 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,Richard D. Braatz. Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy,2019,4(5). (下载链接) | 其他相关论文： 1. Liu,Aoife M. Foley,Alana Zülke,Maitane Berecibar,Elise Nanini-Maury,Joeri Van Mierlo,Harry E. Hoster. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review[J]. Renewable and Sustainable Energy Reviews,2019,113.（下载链接） 2. Cheng Chen,Rui Xiong,Ruixin Yang,Weixiang Shen,Fengchun Sun. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter[J].(下载链接)
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