Early Quality Classification and Prediction of Battery Cycle Life in Production Using Machine Learning. (June 2022)

• Record Type: Journal Article
• Authors: Stock, Sandro; Pohlmann, Sebastian; Günter, Florian J.; Hille, Lucas; Hagemeister, Jan; Reinhart, Gunther
• Is Part Of: Journal of energy storage. Volume 50(2022)
• Publication Date: 2022-06
• Subjects: Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.104144
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Extraction of 24 features for data-driven quality prediction in production. Detailed comparison of machine learning approaches and sensitivity analysis of input features. Classification of lithium-ion batteries in multiple groups with short and long cycle life. Quality grading of lithium-ion batteries in four grades according to the cycle life. Analysis of advanced production strategies. Abstract: An accurate determination of the product quality is one of the key challenges in lithium-ion battery (LIB) production. Since LIBs are complex, electrochemical systems, conventional quality control measures such as aging are time-intensive and costly. This paper presents the applicability of machine learning approaches for an early quality prediction and a classification of cells in production. Using inline measurement data of 29 NMC111/graphite pouch cells, linear regression models and artificial neural networks (ANNs) were compared regarding their prediction accuracy. From comprehensive electrochemical impedance spectroscopy (EIS) and cycling datasets, a total of 24 features were extracted, combined, and analyzed. The best ANN achieved a test error of 10.1% at an observation time of less than two days. For a classification into two cycle life groups, a maximum accuracy of 97% was reached. Moreover, a reliable classification of high-lifetime cells was achieved using only EIS measurements during wetting. The results highlight the great potential of data-driven models for the prediction of LIB quality in production as well as their implementation to increase the throughput and the overall cell quality.