Scaling Sodium-ion Battery Development with NLP

Sodium-ion batteries (SIBs) have been gaining attention for applications like grid-scale energy storage, largely owing to the abundance of sodium and an expected favorable $/kWh figure. 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 knowledge of LIBs, but several materials synthesis and performance challenges for electrode materials need to be addressed for SIBs to mature to an industrial scale. This work extracts challenges in the performance and synthesis of SIB cathode active materials (CAMs) and reviews corresponding mitigation strategies from a combination of SIB and related LIB literature employing custom natural language processing (NLP) tools. These NLP tools help in identifying the mitigation strategies of interest and subsequently evaluate them using a process-based cost model and other scalability metrics. This approach facilitates the generation of quantitative insights and enables a unique comparison among a broad set of lab-proposed mitigation strategies. These derived insights enable engineers in research and industry to navigate a large number of proposed strategies and focus on impactful scalability-informed mitigation strategies to accelerate the transition from lab to commercialization.