Sensitivity analysis methodology for battery degradation models

Abstract

Accurate degradation models are crucial to perform efficient battery design and management. The time and resources required to improve the output accuracy of the models depends critically on the ability to assess the sensitivity to the input factors governing the inherent dominant mechanism in the model. Here, we present a sensitivity analysis of a pseudo-two-dimensional battery model coupled with a capacity fade model based on solid-electrolyte interphase (SEI) 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 SEI and the irreversible charge loss can be explored. The proposed method will provide key insight into how sensitivity analysis of the physics-based degradation model must be conducted for effective integration into battery management systems.