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

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. • Time-dependent global sensitivity analysis of 24 parameters of the Doyer–Fuller–Newman model for drive cycle simulations. • Use of an innovative history-aware approach, which aggregates the parameters’ effects over time to obtain accurate results. • Investigation of the model error introduced by arbitrarily valued insensitive parameters and discussion of ramifications in the context of battery model parametrization. • Open-source implementation of novel algorithms for variance-based sensitivity analysis of time-dependent model outputs.