Advances in numerical modeling and experimental insights for hydrogen storage systems: A comprehensive and critical review

Abstract

Hydrogen storage plays a pivotal role in enabling the transition to low-carbon energy systems, supporting renewable integration, transportation, and industrial decarbonization. While numerous studies have examined specific storage technologies, a comprehensive and structured synthesis across numerical and experimental approaches remains lacking. This review systematically categorizes and analyzes over 90 recent contributions, covering gaseous (30 MPa to 70 MPa), liquid (cryogenic at −253 °C), and solid (e.g., LaNi₅, Mg-based alloys) panning gaseous, liquid, and solid hydrogen storage. Emphasis is placed on advanced modeling methods such as computational fluid dynamics (CFD), finite element analysis (FEA), and artificial intelligence (AI) as well as experimental strategies employed to validate and optimize these technologies. The review highlights key parameters influencing storage performance, including thermal management, material behavior, structural integrity, and system integration. It also outlines the growing role of AI in predictive maintenance and real-time optimization through digital twin frameworks. By critically comparing modeling tools and experimental findings, this paper identifies existing research gaps and proposes integrated, multi-scale approaches for future hydrogen storage development.