An analysis of the hydrogen energy knowledge base with a focus on metal hydride-based solid-state hydrogen storage application

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Authors: Douw Gerbrand Faurie, Mikateko Manganyi, Lawrence Koech, Kasturie Premlall, A. L. Kolesnikov, Mykhaylo Lototskyy
Cited by: 6
References: 82

Abstract

The world’s natural gas and crude oil resources are diminishing to the point that gas and petroleum prices are lowering the common man’s standard of living worldwide. Likewise, there is a need for the world to move towards a carbonless energy future to curb the influence of climate change, with increasingly intense climate phenomena observed every year. Subsequently, the search continues for increasingly practical renewable energy sources and effective energy storage methods. Hydrogen, as a clean and abundant energy source, has the highest energy density in terms of mass when excluding nuclear fuels. In terms of the United Nations' Sustainable Development Goals, hydrogen will play a crucial role, specifically in achieving Goal 7, Affordable and Clean Energy, and Goal 13, Climate Action. This study seeks to lay out the current knowledge base of hydrogen energy, focusing on solid-state hydrogen storage. The focus on solid-state hydrogen storage is due to the need for long-term safe storage, which is one of the challenges experienced by hydrogen energy and the hydrogen economy. Moreover, solid-state hydrogen storage offers a potential solution to this challenge. The analysis outlined the current state of hydrogen energy as well as the need for further investigation into metal-hydride-based hydrogen storage while considering the benefits that simulation and machine learning have shown in the field of research. In so doing, it justifies further experimental and simulation-based research into solid-state hydrogen storage using hydride-forming metals. This paper shows that there is not enough published work on large-scale solid-phase hydrogen storage units, as well as the lack of operational machine learning modelling of hydrogen systems. • Deep analysis of metal hydride mechanism • Analysis of optimisation literature. • Analysis of simulation studies • Outlining knowledge gap

Fields of Study & Subjects

Hydrogen Storage and Materials Hybrid Renewable Energy Systems Machine Learning in Materials Science

Publication details

Journal / source
Journal of Alloys and Compounds
Publisher
Elsevier BV
Volume
1057
Pages
186908–186908
Publication date
21 February 2026
Language
EN