Micha C. J. Philipp | Searcharxiv

arXiv2026

Physics-based modeling of cyclic and calendar aging of LIBs with Si-Gr composite anodes

Higher energy density and longer lifetime are the requirements for next-generation lithium-ion batteries. A promising anode material is silicon, which offers high specific capacity, but its significant volume change during lithiation and delithiation enormously reduces battery lifetime. A physical understanding of the processes degrading the battery is key to mitigate this effect and advance in the field. We develop a physics-based model to describe degradation during battery cycling under various protocols and storage conditions, with varying check-up (CU) frequencies. The model can disentangle basic degradation mechanisms, such as the growth of the Solid-Electrolyte Interphase (SEI), from silicon mechanisms, such as particle cracking, SEI growth on cracks, and loss of active material (LAM). We investigate the impact of CUs on the observed storage degradation and the reason behind the increased degradation in batteries, including silicon in the anode. Additionally, we relate the observed degradation to operating conditions, enabling future optimization of battery use and design.

physics.chem-ph↗

arXiv2024

Physics-based inverse modeling of battery degradation with Bayesian methods

To further improve Lithium-ion batteries (LiBs), a profound understanding of complex battery processes is crucial. Physical models offer understanding but are difficult to validate and parameterize. Therefore, automated machine-learning methods (ML) are necessary to evaluate models with experimental data. Bayesian methods, e.g., Bayesian optimization for likelihood-free inference (EP-BOLFI), stand out as they capture uncertainties in models and data while granting meaningful parameterization. An important topic is prolonging battery lifetime, which is limited by degradation, such as the solid-electrolyte interphase (SEI) growth. As a case study, we apply EP-BOLFI to parametrize SEI growth models with synthetic and real degradation data. EP-BOLFI allows for incorporating human expertise in the form of suitable feature selection, which improves the parametrization. We show that even under impeded conditions, we achieve correct parameterization with reasonable uncertainty quantification, needing less computational effort than standard Markov chain Monte Carlo methods. Additionally, the physically reliable summary statistics show if parameters are strongly correlated and not unambiguously identifiable. Further, we investigate Bayesian alternately subsampled quadrature (BASQ), which calculates model probabilities, to confirm electron diffusion as the best theoretical model to describe SEI growth during battery storage.

physics.chem-ph↗