Artificial intelligence and machine learning in battery materials and their applications

Abstract

The fast-depleting fossil fuels and other environmental impacts necessitate rapid development and deployment of efficient, smart, intelligent, and future-ready energy storage solutions. Gone are the days of only trial-and-error-based research and development protocols that take a long time to mature and yield meaningful results, say, in discovering new structures/functional materials (nano to microstructure) for batteries or the development of new battery systems. As the existing computational power is increasing rapidly, coupled with the rapidly falling cost of computation, artificial intelligence (AI) and machine learning (ML) have proved their potential in discovering new battery materials in a short period. This chapter begins with a brief introduction to various AI and ML methods used in the development and deployment of battery material and their applications. Then we focus on the AI and ML methods used in different stages of battery production, from the material selection stage to the manufacturing, state of charge, and state of health prediction, understanding and controlling degrading and aging mechanisms and testing of battery performance, as well as some emerging AI and ML-assisted battery technologies. © 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies.