This study presents a comprehensive exploration into enhancing the performance of lithium-sulfur (Li-S) batteries, a promising energy storage technology, through innovative material and design modifications. Initially, the research delved into the development and characterization of a novel TiC-TiO
2-TiN composite as a cathode material, aiming to leverage the unique properties of these compounds to improve battery efficiency and mitigate the polysulfide shuttle effect. However, despite successful synthesis and initial promising attributes, it became evident that the TiC-TiO
2-TiN composite required further refinement to fully meet performance expectations. In pursuit of this enhancement, it investigated the impact of various conductive agents, including multi-walled carbon nanotubes (MWCNT) and SYBAC, on the discharge capacity and stability of Li-S batteries. This investigation into conductive agents opened new avenues for augmenting battery performance, revealing the intricate balance between conductivity and stability within the battery's architecture. Building on these findings, the research progressed to a modified approach, developing a TiO
2-TiN composite via a single-step liquid phase reaction. This new composite aimed to consolidate the favorable characteristics of both titanium dioxide and titanium nitride, addressing the limitations observed in the initial TiC-TiO
2-TiN composite. The TiO
2-TiN composite was extensively evaluated for its interaction with lithium polysulfides, electrochemical performance, and theoretical underpinnings through various electrochemical tests and density functional theory calculations. This phase of research marked a significant step towards optimizing the cathode material for enhanced battery efficiency and longevity. Further, the integration of a carbon-coated separator into the Li-S battery system was investigated. This integration aimed to provide additional improvements in electron transfer, reduce polysulfide migration, and consequently, enhance the overall performance of the battery. The study employed various types of carbon coatings, assessing their impact on the battery's operational stability and efficiency. This approach not only highlighted the potential of material modifications in the separator design but also underscored the importance of comprehensive system-level optimization in battery technology. In a novel extension of this research, the thesis incorporated the use of Long Short-Term Memory (LSTM) networks, a form of artificial intelligence, to predict the aging effects on Li-S batteries. This predictive modeling provided valuable insights into the long-term behavior and performance degradation of the batteries, offering a crucial tool for understanding and improving battery longevity. This study underscores the dynamic interplay between material properties, battery design, and predictive analytics in the quest to develop more efficient, stable, and long-lasting energy storage solutions, setting the stage for future advancements in the field.
| Date of Award | 2024 |
|---|
| Original language | English |
|---|
| Awarding Institution | - The Hong Kong University of Science and Technology
|
|---|
| Supervisor | Xijun HU (Supervisor) |
|---|