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A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries

Journal of Energy Chemistry 2022年第5期31卷 548-555页

作者： 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 ChinaChengdu 611731SichuanChina Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical EngineeringTsinghua UniversityBeijing 100084China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China

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 predictive 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 compromised 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 provide 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-seriesbased techniques in forecasting battery cycling performance, as well as the huge potential of datadriven methods in unveiling hidden correlations in complicated energy chemistries such as lithium metal batteries.

关键词： Battery prognosis Machine learning Time series forecasting online prediction Lithium metal batteries

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