Progress of machine learning in materials design for Li-Ion battery

Abstract

The widespread adoption of lithium-ion batteries has ushered in a transformative era across industries, powering an array of devices from portable electronics to electric vehicles. This review explores recent advancements in machine learning tools tailored for improving battery materials, management strategies, and system-level optimization. It provides a comprehensive overview of the current landscape, emphasizing the less-explored evolution of machine learning algorithms in battery materials. Machine learning integration enhances our understanding of material properties, accelerates the discovery of efficient compositions, and contributes to the development of more durable lithium-ion batteries. The article also delves into machine learnings role in predicting State of Health and remaining useful life, crucial for proactive battery maintenance. This review also highlights how integrating machine learning into the field of lithium-ion batteries has the potential to revolutionize battery design and accelerate advancements in energy storage technology, promising a more sustainable and technologically advanced future.

Keywords

• State of health
• Clustering
• Neural networks
• HOMO-LUMO