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arXiv 2408.03469cond-mat.mtrl-scieess.SP
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基于机器学习与早期循环电化学阻抗谱测量的锂电池性能分类及剩余使用寿命预测

Performance Classification and Remaining Useful Life Prediction of Lithium Batteries Using Machine Learning and Early Cycle Electrochemical Impedance Spectroscopy Measurements

Christian Parsons, Adil Amin, Prasenjit Guptasarma

AI总结:

本文利用早期循环EIS特定频率结合机器学习,实现锂电池性能100%准确分类和R²>0.96的剩余寿命预测,并揭示分类与频率无关。

摘要:

本文提出了一种在早期循环中对锂离子电池进行高、低性能类别分类的方法,并结合线性lasso技术预测其剩余使用寿命(RUL)。传统方法通常依赖大量循环测试和测量大量电化学阻抗谱(EIS)频率来评估电池性能,这既耗时又耗费资源。在本研究中,我们提出了一种利用特定EIS频率的方法,在电池运行的最初几个循环内实现准确分类和RUL预测。值得注意的是,仅给定20 kHz阻抗响应时,我们的支持向量机(SVM)模型对电池进行分类的准确率达到100%。此外,我们的研究结果表明,在从高频(<20 kHz)到低频(32 mHz)的范围内,电池性能分类与频率无关。我们的模型还基于单个高频(20 kHz)和单个中频(8.8 Hz)的异相阻抗响应,并结合温度数据,展示了准确的RUL预测,R²>0.96。本研究强调了中频阻抗响应作为决定电池性能的若干关键特征之一的重要性,从而拓宽了对影响电池行为因素的理解。

英文摘要:

We presents an approach for early cycle classification of lithium-ion batteries into high and low-performing categories, coupled with the prediction of their remaining useful life (RUL) using a linear lasso technique. Traditional methods often rely on extensive cycling and the measurement of a large number of electrochemical impedance spectroscopy (EIS) frequencies to assess battery performance, which can be time and resource consuming. In this study, we propose a methodology that leverages specific EIS frequencies to achieve accurate classification and RUL prediction within the first few cycles of battery operation. Notably, given only the 20 kHz impedance response, our support vector machine (SVM) model classifies batteries with 100\% accuracy. Additionally, our findings reveal that battery performance classification is frequency agnostic within the high frequency ($<20$ kHz) to low-frequency (32 mHz) range. Our model also demonstrates accurate RUL predictions with $R^2>0.96$ based on the out of phase impedance response at a single high (20 kHz) and a single mid-frequency (8.8 Hz), in conjunction with temperature data. This research underscores the significance of the mid-frequency impedance response as merely one among several crucial features in determining battery performance, thereby broadening the understanding of factors influencing battery behavior.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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