Machine learning for battery quality classification and lifetime prediction using formation data

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-12-01 DOI:10.1016/j.egyai.2024.100451
Jiayu Zou , Yingbo Gao , Moritz H. Frieges , Martin F. Börner , Achim Kampker , Weihan Li
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Abstract

Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits. Here, we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data. We extract three classes of features from the raw formation data, considering the statistical aspects, differential analysis, and electrochemical characteristics. The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms. Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing. The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25°C and 45°C, respectively. Moreover, the lifetime prediction model is able to predict the battery end-of-life with mean percentage errors of 6.50% and 5.45% for the batteries aged at 25°C and 45°C, respectively. This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.

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使用形成数据进行电池质量分类和寿命预测的机器学习
在电池出厂前对电池质量进行准确的分类和寿命预测,将带来经济效益和安全效益。在这里,我们提出了一种数据驱动的方法,通过机器学习来对电池质量进行分类,并在使用前仅使用形成数据来预测电池寿命。我们从原始地层数据中提取了三类特征,考虑了统计方面、差分分析和电化学特征。基于老化机理,分析了提取的100多个特征与电池寿命的相关性。开发了机器学习模型,通过与电池老化高度相关的特征对电池质量进行分类并预测电池寿命。验证结果表明,对于25°C和45°C老化电池,质量分类模型的准确率分别达到89.74%和89.47%。在25°C和45°C老化条件下,寿命预测模型预测电池寿命的平均百分比误差分别为6.50%和5.45%。这项工作强调了来自生产线的电池形成数据在质量分类和寿命预测方面的潜力。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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