Lithium-sulphur batteries: testing and predictive modeling of discharge curves

To address the climate change, in recent decades the global demand for advanced energy storage solutions has grown, especially that for batteries based on lithium chemistry. Lithium-sulfur batteries (Li-S) have emerged as a promising candidate for next-generation batteries due to their high theoretical specific energy, reduced costs and environmental friendliness. However, challenges such as capacity fading, volume fluctuation and poor cycle life hinder their the wide scale commercialization. This thesis investigates the performance of Li-S cells through experimental testing and proposes a predictive model to forecast discharge curves, aiming to enhance the understanding and optimization of these cells. The research begins with the assembly of Li-S cells employing materials and fabrication techniques commonly used in this field. Two types of cells are manufactured to permit a comparison on the fabrication processes. Their difference relies on the cathode manufacturing, one type consists in mechanical mixing of the cathode materials (ball milling method, BM), the other undergoes a thermal treatment (melt infusion method, MI). The cells are characterized using various electrochemical techniques as galvanostatic cycling, rate capability test, electrochemical impedance spectroscopy and cyclic voltammetry. These tests provide valuable insights into the electrochemical behavior and limitations of the cells, allowing to understand the influence on cell performance of key parameters such as sulphur loading, electrolyte composition and electrode architecture. In parallel with experimental investigations, a 1D model is developed to simulate and predict the discharge curves of Li-S cells under varying operating conditions. It was implemented in \textit{Comsol Multiphysics} ® \textit{6.1} using design parameters from cell assembly and values gathered from literature review. In conclusion, through the combination of experimental testing, computational modeling and literature review, this thesis provides a comprehensive analysis of Li-S cells performance, presenting a predictive model for discharge curves under various operation conditions. The predictive model serves as a valuable tool for accelerating the development of advanced Li-S batteries with improved energy density, cycling stability, and safety.