Time-dependent global sensitivity analysis of the Doyle-Fuller-Newman model

TL;DR

This paper introduces a new framework for global sensitivity analysis of the Doyle-Fuller-Newman lithium-ion battery model's time-dependent outputs, improving understanding of parameter influence on voltage responses.

Contribution

It develops a novel method for analyzing parameter sensitivity over time in complex electrochemical models, applicable to non-scalar outputs.

Findings

Identifies parameters with minimal impact on voltage response.

Explores model error when unimportant parameters are varied.

Provides a methodology for sensitivity analysis of time-dependent quantities.

Abstract

The Doyle-Fuller-Newman model is arguably the most ubiquitous electrochemical model in lithium-ion battery research. Since it is a highly nonlinear model, its input-output relations are still poorly understood. Researchers therefore often employ sensitivity analyses to elucidate relative parametric importance for certain use cases. However, some methods are ill-suited for the complexity of the model and appropriate methods often face the downside of only being applicable to scalar quantities of interest. We implement a novel framework for global sensitivity analysis of time-dependent model outputs and apply it to a drive cycle simulation. We conduct a full and a subgroup sensitivity analysis to resolve lowly sensitive parameters and explore the model error when unimportant parameters are set to arbitrary values. Our findings suggest that the method identifies insensitive parameters whose variations cause only small deviations in the voltage response of the model. By providing the methodology, we hope research questions related to parametric sensitivity for time-dependent quantities of interest, such as voltage responses, can be addressed more easily and adequately in simulative battery research and beyond.