Data Mining for Early Cycle Life Prediction in Lithium-Ion Battery Production

The efficient prediction of product quality is a major challenge in lithium-ion battery production, as conventional measures such as aging are time-consuming and costly. This study presents a comprehensive data mining approach to predict the quality of lithium-ion batteries using linear and non-line...

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Bibliographic Details
Published in:Procedia CIRP 2024, Vol.126, p.835-840
Main Authors: Stock, Sandro, Ahmed, Mahmoud, Konwitschny, Fabian, Daub, Rüdiger
Format: Article
Language:English
Subjects:
Battery production
Cycle life prediction
Incremental capacity analysis
Machine learning
Support vector regression
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Summary:The efficient prediction of product quality is a major challenge in lithium-ion battery production, as conventional measures such as aging are time-consuming and costly. This study presents a comprehensive data mining approach to predict the quality of lithium-ion batteries using linear and non-linear support vector machines. A methodology for extracting and selecting features from data sources within production is presented, and several feature selection algorithms – as well as models – are compared with regard to their predictive power. A minimum test error of 8.8 % for the early cycle life prediction was achieved, along with a classification accuracy of 96.6 %, when dividing the lithium-ion batteries into two quality grades with high and low cycle life.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2024.08.267