An Open-Access Benchmark of Statistical and Machine-Learning Anomaly Detection Methods for Battery Applications

Overview

OSBAD benchmarks statistical and unsupervised anomaly detectors across battery datasets and chemistries. The study examines physics-informed feature transformations and proxy-based Bayesian hyperparameter tuning to support reproducible comparison with incomplete anomaly labels.