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.

翻译：Doyle-Fuller-Newman模型可谓是锂离子电池研究中应用最广泛的电化学模型。由于该模型高度非线性，其输入-输出关系仍未被充分理解。因此，研究人员常采用敏感性分析来阐明特定使用场景下参数的相对重要性。然而，部分方法难以适配该模型的复杂性，而适用的方法往往面临仅能处理标量感兴趣量的局限。我们实现了一种针对时间依赖模型输出的全局敏感性分析新框架，并将其应用于驾驶循环仿真。通过全参数与子组敏感性分析，我们筛选出低敏感性参数，并探究了将非重要参数设为任意值时产生的模型误差。研究结果表明，该方法能够识别出不敏感参数，这些参数的改变仅会导致模型电压响应的微小偏差。通过提供该分析框架，我们希望与时间依赖感兴趣量（如电压响应）相关的参数敏感性研究问题，能够在电池仿真及相关领域中得到更简便充分的解决。