From data to design: A brief review of artificial intelligence for next-generation hydrogen tanks

Abstract

Hydrogen storage is a critical component in advancing sustainable energy systems, with composite tanks emerging as a key solution for efficient and safe storage. In recent years, artificial intelligence (AI) has revolutionized the design and optimization of composite hydrogen storage tanks by enabling advanced material selection, structural optimization, and performance prediction. This topical review explores the latest AI-driven innovations in the design of sustainable hydrogen storage tanks, focusing on techniques such as machine learning, deep learning, and evolutionary algorithms. Key applications include optimization of tank geometry, failure prediction, and material property enhancement, with a particular emphasis on addressing challenges like weight reduction, cost-efficiency, and safety under extreme operating conditions. Furthermore, this review examines the integration of AI with computational tools and experimental validation methods, offering insights into current trends and future research directions in the field. By highlighting the transformative potential of AI, this review provides a comprehensive understanding of how these technologies are shaping the next generation of sustainable hydrogen storage solutions.