Uncertainty Quantification of Silicon Anode Capacity Fading with Multiple Interactive Degradation Mechanisms Using Physics-Informed Gaussian Process Modeling

The surging demand for Li-ion batteries (LIBs) has started a quest for innovations in their design and technology. A notable improvement in this regard involves the use of Silicon (Si) as the active anode material in LIBs. However, a major challenge stopping its widespread adoption is the considerable volume change experienced by Si during the lithiation-delithiation process, leading to volumetric stress-induced capacity degradation. This study identifies three primary capacity fade mechanisms in these LIBs: volumetric-stress-induced cracking and delamination, along with the growth of the solid electrolyte interface (SEI) during charging and discharging cycles. These mechanisms are influenced by battery design and operating conditions, such as Si anode thickness, ambient working temperature, and charging rate, introducing uncertainty into the battery’s degradation rate. In this study, multiple finite element (FE) models are constructed to simulate capacity degradation resulting from these three capacity fade mechanisms and their predictions are validated against experimental data. To address the computational demands of multiple FE models simulating capacity degradation from these fade mechanisms, a Gaussian Process Regression (GPR) surrogate model is developed. This GPR model efficiently predicts capacity fade and is validated for accuracy. Subsequently, the GPR model is used in an uncertainty quantification study that is focused on the battery’s design and operating conditions. The objective is to pinpoint the factors that exert the most significant influence on capacity degradation in Si anode-based LIBs.