AutoML-Assisted Classification of Li-Ion Cell Chemistries from Cycle Life Data: A Scalable Framework for Second-Life Sorting

Abstract

Repurposing lithium-ion (Li-ion) batteries for second-life applications, such as stationary energy storage, offers significant economic and environmental benefits as these cells reach the end of their initial service life. Accurate and scalable classification of used Li-ion cell chemistries is essential for efficient sorting and safe repurposing, especially when manufacturer metadata is unavailable. This study presents a robust, automated machine learning (AutoML) framework, implemented in MATLAB R2024b and its toolboxes, for classifying three commercial 18,650 cell chemistries (LFP, NMC, and NCA) using long-term cycle life data. The workflow integrates structured data ingestion, segmentation, and multi-tiered feature engineering, extracting over 75 diagnostic features per cycle, including statistical, cumulative, segment-specific, and differential curve metrics. Feature selection is performed using principal component analysis and sequential forward selection, while Bayesian optimisation within AutoML identifies the optimal classification model. The resulting K-Nearest Neighbours classifier achieves over 99% test accuracy, demonstrating the effectiveness of the approach. This framework enables research-grade, metadata-independent classification and provides a scalable foundation for future industrial battery sorting and second-life applications.