Philipp, M. C. J., Kuhn, Y., Latz, A., & Horstmann, B. (2024). Physics-based inverse modeling of battery degradation with Bayesian methods. arXiv preprint arXiv:2410.19478.
This study aims to determine the dominant transport mechanisms responsible for continuous SEI growth in lithium-ion batteries and accurately parameterize corresponding physics-based degradation models using Bayesian machine learning methods.
The researchers employed a physics-informed battery model (Single Particle Model with electrolyte effects - SPMe) coupled with theoretical SEI growth mechanisms (electron diffusion, electron conduction, electron migration, and solvent diffusion). They utilized Bayesian optimization (EP-BOLFI) to inversely model synthetic and real storage data, quantifying parameter uncertainties and correlations. Additionally, they applied Bayesian model selection (BASQ) to identify the most probable SEI growth mechanism based on model evidence.
This research highlights the power of Bayesian machine learning for analyzing complex battery degradation processes. By combining physics-based models with advanced statistical methods, the study provides a robust framework for identifying dominant degradation mechanisms, quantifying uncertainties, and ultimately guiding the development of longer-lasting batteries.
This work contributes significantly to the field of battery degradation modeling by demonstrating a practical and efficient approach for analyzing complex, coupled physico-chemical processes. The findings have implications for battery management strategies and the development of more accurate lifetime prediction models.
The study primarily focused on SEI growth during storage. Future research should investigate the interplay of multiple degradation mechanisms during cycling and explore the applicability of these methods to other battery chemistries and aging processes.
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by Micha C. J. ... pada arxiv.org 10-28-2024
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