This research leverages Bayesian machine learning algorithms to analyze and parameterize physics-based models of battery degradation, specifically focusing on solid-electrolyte interphase (SEI) growth mechanisms, demonstrating the effectiveness of this approach in identifying dominant degradation processes and quantifying uncertainties.
This paper introduces a novel stereological approach to reconstruct the 3D structure of cracked NMC particles using readily available 2D SEM data, enabling quantitative analysis of crack morphology and its impact on Li-ion battery performance.
BatteryML is a comprehensive open-source platform designed to unify data preprocessing, feature extraction, and model implementation for enhancing battery research applications.
Machine learning meets battery science to enhance research efficiency and practicality.