Bayesian Methods for Physics-Based Inverse Modeling of Battery Degradation: A Case Study on SEI Growth Mechanisms
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.
Stochastic 3D Reconstruction of Cracked NMC Particles from 2D SEM Data Using a Novel Stereological Approach and Crack Network Model
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: An Open-Source Platform for Machine Learning on Battery Degradation
BatteryML is a comprehensive open-source platform designed to unify data preprocessing, feature extraction, and model implementation for enhancing battery research applications.
BatteryML: An Open-Source Platform for Machine Learning on Battery Degradation
Machine learning meets battery science to enhance research efficiency and practicality.
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