2024 AIChE Annual Meeting

Automated Quantification and Failure Analysis for Li-S Batteries

Lithium-sulfur (Li-S) batteries are highly promising for next-generation energy storage due to their high theoretical energy density and cost-effectiveness. These attributes make them attractive candidates for applications such as electric vehicles and grid energy storage. However, their widespread practical implementation is hindered by several critical issues, including capacity loss, limited cycle life, and reduced efficiency—all of which are driven by complex degradation mechanisms. Key challenges like polysulfide shuttling, sulfur dissolution, lithium dendrite formation, and inactive lithium accumulation significantly affect battery performance and longevity. As these degradation processes overlap, they complicate the diagnosis of failure modes, making it difficult to identify the underlying causes and develop effective mitigation strategies. Thus, a rapid, accurate diagnostic approach is essential to propel the development of high-performance, durable Li-S batteries.

To address these diagnostic challenges, we have developed an advanced analytical framework, supported by an automated software tool named Dr. HUGS. Dr. HUGS represents a major advancement in data analysis for Li-S battery research by automating the interpretation of raw data from various analytical techniques. By eliminating the traditionally time-consuming manual data processing, Dr. HUGS enables researchers to obtain comprehensive diagnostic insights within minutes. The software is designed with a user-friendly interface, allowing users to quickly input experimental data and retrieve crucial diagnostic parameters. Dr. HUGS rapidly processes raw data through peak identification, fitting, and quantification, linking peak areas to calibration curves to determine species concentration and capacity. The results are then visualized in clear formats, such as vector plots and capacity storage charts, providing actionable insights into the battery's health and performance. This automated process drastically reduces data analysis time while maintaining high accuracy and consistency, thereby allowing researchers to quickly diagnose failure mechanisms and iterate on cell designs more effectively. By cross-validating Dr. HUGS' outputs with conventional manual processing, we have demonstrated its reliability, with any discrepancies falling within negligible ranges.

Moreover, Dr. HUGS is versatile and adaptable to different battery testing conditions. It offers detailed insights into how different configurations, such as variations in pressure setups, influence the compositional uniformity and behavior of Li-S batteries. This capability enables researchers to better understand the impact of testing environments on battery performance, guiding the optimization of cell designs and operating conditions. By automating and standardizing the data analysis process, Dr. HUGS provides a powerful platform for accelerating Li-S battery research, ultimately advancing the understanding and development of next-generation energy storage technologies.