Estimating battery degradation is vital not only to monitor battery's state-of-health but also to accelerate research on new battery chemistries.Herein,we present a data-driven approach to forecast the capacity fading trajectory of lab-assembled lithium batteries.Features with physical meanings in addition to pre-dictive abilities are extracted from discharge voltage curves,enabling online prediction for a single cell with only its historical data.The robustness and generalizability allow for the demonstration on a com-promised quality dataset consisting of batteries varying in battery architectures and cycling conditions,with superior accuracy for end of life and degradation trajectory prediction with average errors of 8.2％and 2.8％,respectively.Apart from the impressive prediction accuracy,the as-extracted features also pro-vide physical insights,the incorporation of which into material design or battery operation conditions further enlightens the development of better batteries.We highlight the effectiveness of time-series-based techniques in forecasting battery cycling performance,as well as the huge potential of data-driven methods in unveiling hidden correlations in complicated energy chemistries such as lithium metal batteries.

Xinyan Liu;Xue-Qiang Zhang;Xiang Chen;Gao-Long Zhu;Chong Yan;Jia-Qi Huang;Hong-Jie Peng

Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China;Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology,Department of Chemical Engineering,Tsinghua University,Beijing 100084,China;Advanced Research Institute of Multidisciplinary Science,Beijing Institute of Technology,Beijing 100081,China

能源化学

[1]Xinyan Liu;Xue-Qiang Zhang;Xiang Chen;Gao-Long Zhu;Chong Yan;Jia-Qi Huang;Hong-Jie Peng-.A generalizable,data-driven online approach to forecast capacity degradation trajectory of lithium batteries)[J].能源化学,2022(05):548-555

dictive,enlightens

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