Machine Learning in Lithium-Sulfur Battery Modeling and Control: Key Challenges and Opportunities

Abstract

This talk is motivated by the potential of both solid-state and liquid electrolyte lithium-sulfur batteries to provide significant performance advantages over state-of-the-art lithium-ion batteries, especially in terms of specific energy. The talk provides a brief introduction to the lithium-sulfur chemistry, focusing on the modeling, estimation, and control challenges associated with this chemistry. The talk then surveys some of the key challenges and opportunities associated with the application of machine learning methods, especially the deep learning of battery dynamics, to this chemistry. Areas of overlap and potential synergy between machine learning and physics-based modeling approaches are particularly emphasized in this discussion.