Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science

Abstract

Numerical physics-based models are useful in understanding battery performance and developing optimal battery design architectures. Data science developments have enabled software algorithms to perform data analysis and decision making that traditionally could only be performed by technical experts. Traditional workflows of model development - manual parameter estimation through visual comparison ofsimulations to experimental observations, and model discrimination through expert intuition - are time-consuming, and difficult to justify. This paper compares the conclusions derived from computationally scalable algorithms to conclusions developed by expert researchers. This paper illustrates how data science techniques, such as cross-validation and lasso regression, can be used to augment physics-based simulations to perform data analysis such as parameter estimation, model selection, variable selection, and model-guided design of experiment. The validation of these algorithms is that they produce results similar to those of the expert modeler. The algorithms outlined are well-established and the approaches are general, so can be applied to a variety of battery chemistries and architectures. The conclusions reached using these approaches are in agreement with expert analysis (literature results), were reached with minimal human intervention, and provide quantitative justification. By minimizing the amount of expert time,