Beyond Words and Numbers: Pioneering Sodium-Ion Battery Advancements with NLP and Cost Modeling Synergy

Sodium-ion batteries (SIBs) have been increasingly gaining attention for applications like grid-scale energy storage, largely owing to the abundance of sodium and an expected favorable $/kWh figure. Consequently, the published literature in this field has been increasing exponentially, making it difficult for researchers to keep up with the latest developments and challenges. Improving the performance of SIB electrode materials will enable these batteries to compete with mature technologies like lithium-ion batteries (LIBs) at scale. SIBs can leverage the well-established manufacturing infrastructure knowledge of LIBs, but several materials and synthesis-based challenges for electrode materials need to be addressed for SIBs to mature from lab to market scale. This work aims to extract challenges in the performance and synthesis of SIB Cathode Active Materials (CAMs) and systematically review corresponding mitigation strategies from a combination of SIB and related LIB literature employing custom Natural Language Processing (NLP) tools. Finally, selected mitigation strategies are evaluated using a process-based cost model. This approach facilitates the generation of quantitative insights and enables a unique comparison among a broad set of existing lab-proposed mitigation strategies. These derived insights enable researchers and industry to navigate a large number of proposed strategies and focus on impactful mitigation strategies to accelerate the transition between laboratory-scale research and market-scale implementation.