Sensitivity analysis methodology for battery degradation models

Accurate degradation models are crucial to battery design and management. However, the time and resources required to improve the accuracy of the model input factors that the output is sensitive to, which is essential for elucidating the inherent dominant mechanism in the model, remain a challenge. Here we present a sensitivity analysis of a pseudo-two-dimensional battery model coupled with a capacity fade model based on solid electrolyte interphase formation and the corresponding irreversible charge loss for Li-ion batteries. The proposed method is based on training an inexpensive differentiable surrogate Gaussian process regression model on observed input-output pairs and analysing the surrogate model to learn about the global and local sensitivities of the original system. With this method, the relevant global sensitive parameters can be identified, and an in-depth analysis of electrochemical phenomena such as the correlation between the thickness of the solid electrolyte interphase and the irreversible charge loss can be explored. This proposed method will provide key insight into how the sensitivity analysis of the physics-based degradation model must be conducted for effective integration into battery management systems.