Design and performance modulation of V-based hydrogen storage alloys: a review

Abstract

Under the global energy transition background, large-scale hydrogen energy application represents a crucial initiative for implementing national strategic demands. The development of effective and safe solid-state H₂ storage technologies serves as a key enabler for such large-scale implementation. Vanadium-based alloys with body-centered cubic structure have emerged as prime candidate materials for solid-state H₂ supply in fuel cells, owing to their theoretical hydrogen storage capacity of 3.8 wt% during near-room-temperature hydrogen absorption/desorption processes. This review systematically examines recent advances in various V-based hydrogen storage alloy systems, including the hydrogen absorption/desorption mechanisms of vanadium-based alloys; performance modulation strategies and underlying mechanisms for ternary (V-Ti-Cr), quaternary (V-Ti-Cr-Zr), quinary and higher-order (V-Ti-Cr-Fe-Al) systems; the effects of crucial factors, including chemical composition and lattice parameters, on thermodynamic and kinetic properties during hydrogenation/dehydrogenation cycles; and comprehensive performance optimization strategies. Addressing current limitations including compositional complexity and high design costs, this review highlights the importance of employing machine learning models (e.g., random forest, deep neural networks) to establish composition-property relationships, combined with optimization algorithms for efficient screening of V-based compositions to guide performance modulation. Lastly, a comprehensive analysis is provided on the coupling interaction mechanisms of engineering factors in complex service environments and their significant effects on the long-term service performance of alloys. This study offers valuable insights for the design and industrial application of V-based alloys, thereby contributing to the advancement of solid-state H₂ storage within the H₂ energy industry chain.

Graphical abstract

Keywords

vanadium-based hydrogen storage alloys / machine learning / performance modulation / hydrogen storage mechanism

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