Artificial intelligence in hydrogen energy transitions: A comprehensive survey and future directions

A.Z. Arsad, M.A. Hannan, H.C. Ong, Pin Jern Ker, Richard TK. Wong, R.A. Begum, Gilsoo Jang and T M Indra Mahlia

Renewable and Sustainable Energy Reviews, 2025, vol. 224, issue C

Abstract: The urgent need to transition to sustainable energy sources has made hydrogen technology an essential part of achieving low-carbon goals. However, the shift to hydrogen is hindered by challenges such as low energy conversion efficiency, increasing costs, flammability concerns, and the continued reliance on fossil fuels. Implementing artificial intelligence (AI) in the hydrogen transition has been revealed to be beneficial in facilitating the monitoring, control, optimization, and management of hydrogen-driven systems. This work offers a thorough review of AI methods, including machine learning and optimization techniques, applied to hydrogen production, storage solutions, and utilization frameworks. Key findings highlight the ability of AI to improve system monitoring, fault detection, operational control, and energy flow optimization. AI-driven frameworks exhibit significant potential for improving energy flow, operational efficiency, detection capabilities, and safety. Important areas include AI-driven hydrogen management systems, material science, and hydrogen safety are discussed. Every AI method has merits and cons, yet hydrogen transition aspects require an efficient approach. The purpose is to promote hydrogen technology adoption and overcome AI implementation difficulties with hydrogen systems. The primary findings focus on constructing resilient AI-driven controllers that improve hydrogen production, storage, and use efficiency, dependability, stability, and safety. This work emphasizes the significance of intelligent, robust AI-based controllers and provides guidelines for surmounting technical challenges to expedite the transition to sustainable hydrogen solution research.

Keywords: Artificial intelligence; Hydrogen energy; Operational efficiency; Optimization and control; Sustainable development (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2025.116121

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