Abstract

Growth in portable electronics and electric vehicles has increased the diversity of battery types and chemistries, intensifying demands for fast and accurate assessment within End-of-Life recycling systems. Conventional sorting and disassembly rely heavily on manual or slow diagnostics, poorly suited to high-throughput recycling environments. This study presents an Automated Machine Learning (AutoML) framework for non-destructive battery cell chemistry classification using short-measurement data from a publicly available dataset comprising 109 cells spanning six battery chemistries. The data include 20 raw descriptors capturing physical properties, DC load-response behaviour, and impedance measurements, from which three ageing-informed features are engineered. The framework integrates leakage-safe preprocessing, Minimum Redundancy Maximum Relevance (MRMR) feature selection, and automated learner optimisation within a modelling pipeline. Six experimental cases benchmark deterministic and AutoML-selected classifiers, assess feature representations, identify a compact MRMR Top-4 subset, and evaluate robustness under repeated resampling and constrained training availability. The best configuration achieves 92.9% accuracy and Macro-F1 of 0.923 under deterministic evaluation, and a mean accuracy of 97.6% and Macro-F1 of 0.964 with narrow empirical variability intervals under repeated cross-validation. Overall, within the scope of the dataset examined, the proposed workflow provides an interpretable and reproducible methodological foundation for battery sorting and recycling applications.

IPC Classification

Keywords