Lithium sulfur (LiS) batteries are a promising option for next-generation energy storage due to their high energy density. Applications where weight is especially important like electric flight and electric vehicles have driven the study of this chemistry because lithium ion batteries are approaching their practical limits. For these applications, the demand for packing more energy into a battery can lead to higher resistive batteries. For lithium sulfur batteries in particular, the resistance is seen to increase with higher capacities per cell (1). As the required energy and subsequently battery packs increase, the importance of heat dissipation for safety and performance also grows, which requires an accurate thermal model for design and control.

Physics-based models enable a look into the internal states of the battery, which correspond to physical phenomena that can contribute to decreased performance under certain conditions. The mathematical model for lithium sulfur batteries developed by Kumaresan et al (2) was the first one-dimensional physics-based model for lithium sulfur batteries. This discharge model was developed with porous electrode theory and considers transport, kinetics, thermodynamics, and morphological changes. The LiS chemistry includes a reduction of solid sulfur to different order polysulfides and finally a precipitation of Li₂S, which leads to complexity due to multiple charge carriers and dynamic electrode morphology. The corresponding physics-based model results in a numerically stiff set of equations with variables evolving across many orders of magnitude. To alleviate the computational footprint and still maintain a high degree of accuracy, we have previously developed a mass-conserving lumped model through volume-averaging, called the Lithium Sulfur Tank-in-Series Model. This model, with average quantities in each region, eliminates the spatial dependence for increased computational efficiency and fewer model parameters that expedites model use in estimation, control, and optimization.

In this work, the LiS tank-in-series model is coupled with a thermal model (3). The thermal model includes heat generation due to internal resistance and entropic contributions, and Stroe et al. used the thermal model predictions with a simple empirical model. Coupling the thermal model with a more accurate predictive model allows further insight into battery performance. The model predictions are compared with experimental data from 19.5 Ah pouch cells.

Acknowledgments

The authors are thankful for financial support from the Battery500 Consortium, BAE Systems, and the Joint Center for Aerospace Technology Innovation.

References

1. J. Offer and M. Wild, Lithium Sulfur Batteries, p. 149, John Wiley & Sons, New Jersey (2019).
2. Kumaresan, Y. Mikhaylik, and R. E. White, J. Electrochem. Soc., 155, A576 (2008).
3. I. Stroe, V. Knap, M. Swierczynski, and E. Schaltz, ECS Trans., 77, 467–476 (2017).