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<p>This review paper delves into the advancements in hydrogen (H<sub>2</sub>) storage technology, a key area in the quest for sustainable energy solutions. The paper, aided by artificial intelligence, evaluates these advancements, identifies challenges, and suggests future directions in this multidisciplinary field. With the global shift towards clean energy, H<sub>2</sub> is increasingly recognized as a versatile, eco-friendly fuel. AI, a game-changer, offers new possibilities for improving the efficiency and reliability of H<sub>2</sub> storage systems. Technologies like solid-state storage materials, cryogenic liquefaction, and chemical storage have seen significant progress. Additionally, AI methods such as machine learning and neural networks are being employed to address complex issues in H<sub>2</sub> storage, including material discovery and system optimization. Despite advancements, challenges, and opportunities remain in merging H<sub>2</sub> storage technology and AI. Future research should focus on developing new storage materials, implementing advanced AI algorithms for real-time monitoring, and integrating renewable energy sources.</p>

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Hydrogen energy storage with artificial intelligent-powered strategies for a sustainable future: A review

  • 期刊论文

  • 发表日期：

    2025年3月3日

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  • Ravikumar Jayabal

摘要

This review paper delves into the advancements in hydrogen (H2) storage technology, a key area in the quest for sustainable energy solutions. The paper, aided by artificial intelligence, evaluates these advancements, identifies challenges, and suggests future directions in this multidisciplinary field. With the global shift towards clean energy, H2 is increasingly recognized as a versatile, eco-friendly fuel. AI, a game-changer, offers new possibilities for improving the efficiency and reliability of H2 storage systems. Technologies like solid-state storage materials, cryogenic liquefaction, and chemical storage have seen significant progress. Additionally, AI methods such as machine learning and neural networks are being employed to address complex issues in H2 storage, including material discovery and system optimization. Despite advancements, challenges, and opportunities remain in merging H2 storage technology and AI. Future research should focus on developing new storage materials, implementing advanced AI algorithms for real-time monitoring, and integrating renewable energy sources.