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Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells

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Energy and AI 2020年第1期1卷 64-73页

作者： Paula Fermin-Cueto Euan McTurk Michael Allerhand Encarni Medina-Lopez Miguel F.Anjos Joel Sylvester Goncalo dos Reis School of Mathematics University of EdinburghThe King’s BuildingsEdinburghUK dukosi ltd. Quantum CourtResearch Avenue SouthHeriot Watt University Research ParkEdinburghUK Institute for Infrastructure and Environment School of EngineeringUniversity of EdinburghThe King’s BuildingsEdinburghUK Centro de Matematica e Aplicações(CMA) FCTUNLPortugal

High-performance batteries greatly benefit from accurate,early predictions of future capacity loss,to advance the management of the battery and sustain desirable application-specific performance characteristics for as long as ***-ion cells exhibit a slow capacity degradation up to a knee-point,after which the degradation ac-celerates rapidly until the cell’s *** capacity degradation data,we propose a robust method to identify the knee-point within capacity fade *** a new approach to knee research,we propose the concept‘knee-onset’,marking the beginning of the nonlinear degradation,and provide a simple and robust identifica-tion mechanism for *** link cycle life,knee-point and knee-onset,where predicting/identifying one promptly reveals the *** data featuring continuous high C-rate cycling(1C–8C),we show that,on average,the knee-point occurs at 95%capacity under these conditions and the knee-onset at 97.1%capacity,with knee and its onset on average 108 cycles *** the critical identification step,we employ machine learning(ML)techniques for early prediction of the knee-point and *** models predict knee-point and knee-onset quantitatively with 9.4% error using only information from the first 50 cycles of the cells’*** models use the knee-point predictions to classify the cells’expected cycle lives as short,medium or long with 88–90% accuracy using only information from the first 3–5 *** accuracy levels are on par with existing literature for End-of-Life prediction(requiring information from 100-cycles),nonetheless,we address the more complex problem of knee *** estimations are enriched with confidence/credibility *** uncertainty regarding the ML model’s estimations is quantified through prediction *** yield risk-criteria insurers and manufacturers of energy storage applications can use for battery *** classification model provides a tool for cell man-ufacturers

关键词： Lithium-ion battery Degradation Knee-point Knee-onset Machine learning Uncertainty quantification

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