Leveraging Efficient Physics-based Models for Insights into Lithium Sulfur Batteries

Abstract

Energy storage is critical to adoption of renewables and combating climate change. As the burgeoning requirements for electric transportation outpace the current lithium-ion technology, the race to commercialize the next-generation battery is still ongoing. Lithium sulfur (LiS) is a promising candidate with a practical energy density predicted to be 2-3 times that of lithium-ion battery systems. However, LiS cells suffer from safety issues, poor cycle life, and low coulombic efficiency. Alongside materials science and cell engineering, physics-based modeling can be a powerful tool to analyze and interpret experimental data and guide design and identify optimal conditions. The power of physics-based models is the prediction of internal states that can contribute to degradation or performance. The prediction power of these models comes at high computational cost. The development of efficient yet accurate models for whole-cell predictions can aid experimental efforts through model-based design or parameter estimation. Optimization calls of the model can number into the 1000s for each experiment, underpinning the importance of computational efficiency and speed. This work details a novel physics-based model that considers the battery as a series of connected tanks, resulting in a significant upgrade in speed compared to the conventional one-dimensional (1D) model for LiS batteries with similar predictive power. This suggests real potential for applications such as optimal charging, cell-balancing, and estimation. As new materials, coatings, and electrolyte systems are considered and tested, standardized cell conditions for LiS batteries are yet to be determined. This presents an exciting opportunity for modeling efforts to collaborate with experimentalists, in analyzing the effect of novel experiments and identifying important measurements to do next. The application of efficient models that incorporate experimental insights is the other focus of this work. The implications of radical anion formation on the current-voltage behavior are explored for the first time; parameters describing the dissociation reaction equilibrium and kinetics are shown to alter the electrolyte speciation in ways that can be linked to observations from LiS electrolyte engineering experiments. The efficient models are also utilized in characterizing pouch cells for electric flight in collaboration with BAE Systems and predicting the thermal behavior of an LiS sandwich cell. The culmination of this work is the ongoing collaboration with Pacific Northwest National Laboratory and Brookhaven National Laboratory to identify unknown XRD spectra through speciation insights gleaned from parameter estimation. These contributions represent a step forward in efforts to incorporate detailed and accurate electrochemical models for experimental insights and advanced Battery Management Systems for LiS batteries.