Machine learning for battery quality classification and lifetime prediction using formation data
Jiayu Zou, Yingbo Gao, Moritz Frieges, Martin Börner, Achim Kampker, Weihan Li
Abstract
• Battery quality classification and lifetime prediction only using formation data. • Data-driven framework with machine learning for quality control in battery production. • Over 100 features extracted from formation data for enhanced ageing analysis. • Achieved 89.74% accuracy in classification and 5.45% error in lifetime prediction. • Potential to reduce costly end-of-line testing in battery production processes. 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.