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...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 possible.Li-ion cells exhibit a slow capacity degradation up to a knee-point,after which the degradation ac-celerates rapidly until the cell’s End-of-Life.Using capacity degradation data,we propose a robust method to identify the knee-point within capacity fade curves.In 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 it.We link cycle life,knee-point and knee-onset,where predicting/identifying one promptly reveals the others.On 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 apart.After the critical identification step,we employ machine learning(ML)techniques for early prediction of the knee-point and knee-onset.Our models predict knee-point and knee-onset quantitatively with 9.4% error using only information from the first 50 cycles of the cells’life.Our 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 cycles.Our 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 prediction.All estimations are enriched with confidence/credibility metrics.The uncertainty regarding the ML model’s estimations is quantified through prediction intervals.These yield risk-criteria insurers and manufacturers of energy storage applications can use for battery warranties.Our classification model provides a tool for cell man-ufacturers to speed up the validation of cell production techniques.展开更多
文摘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 possible.Li-ion cells exhibit a slow capacity degradation up to a knee-point,after which the degradation ac-celerates rapidly until the cell’s End-of-Life.Using capacity degradation data,we propose a robust method to identify the knee-point within capacity fade curves.In 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 it.We link cycle life,knee-point and knee-onset,where predicting/identifying one promptly reveals the others.On 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 apart.After the critical identification step,we employ machine learning(ML)techniques for early prediction of the knee-point and knee-onset.Our models predict knee-point and knee-onset quantitatively with 9.4% error using only information from the first 50 cycles of the cells’life.Our 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 cycles.Our 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 prediction.All estimations are enriched with confidence/credibility metrics.The uncertainty regarding the ML model’s estimations is quantified through prediction intervals.These yield risk-criteria insurers and manufacturers of energy storage applications can use for battery warranties.Our classification model provides a tool for cell man-ufacturers to speed up the validation of cell production techniques.
