Application of machine learning to thermal management of solid-state hydrogen storage: A comprehensive review

Thermal management of metal hydride (MH) hydrogen storage systems is critically important to maintain the hydrogen absorption and release rates at desired levels. Implementing thermal management arrangements introduces challenges at system level mostly related to system's overall mass, volume, energy efficiency, complexity and maintenance, long-term durability, and cost. Low effective thermal conductivity (ETC) of the MH bed (∼0.1e0.3 W/mK) is a well-known challenge for effective implementation of different thermal management techniques. This paper comprehensively reviews thermal management solutions for the MH hydrogen storage used in fuel cell systems by also focusing on heat transfer enhancement techniques and assessment of heat sources used for this purpose. The literature recommended that the ETC of the MH bed should be greater than 2 W/mK, and heat transfer coefficient with heating/cooling media should be in the range of 1000e1200 W/m2K to achieve desired MH's performance. Furthermore, alternative heat sources such as fuel cell heat recovery or capturing MH heat during charging and releasing it back during discharging have also been thoroughly reviewed here. Finally, this review paper highlights the gaps and suggests directions accordingly for future research on thermal management for MH systems. • Renewable energy has emerged as the cornerstone of the global "modern energy system," effectively reducing reliance on fossil fuels. As a critical component of green renewable energy, hydrogen is globally acknowledged as the "ultimate energy carrier" for humanity, owing to its high energy density, superior energy conversion efficiency, environmentally benign and pollution-free attributes, and abundant natural availability. This review systematically examines the advancements in high-throughput screening (HTS) and machine learning (ML) within the realm of solid-state hydrogen storage material research. • This review emphasizes the unique advantages of high-throughput screening (HTS) in rapid material selection, highlighting its role in accelerating the discovery process of promising candidate materials. It then discusses the transition to the application of machine learning (ML) technologies, exploring their utility in predicting the performance of solid-state hydrogen storage materials, deciphering structure-property relationships, and optimizing material design frameworks. Furthermore, this paper reviews the recent applications of HTS-ML approaches in the screening and prediction of solid-state hydrogen storage materials. • This review provides a detailed overview of the applications of artificial intelligence (AI) in the development of high-performance solid-state hydrogen storage materials, highlighting the interdisciplinary nature of this extremely challenging research field. It not only describes the current status and potential of high-throughput screening (HTS) and machine learning (ML) technologies but also offers critical references and guidance for future research on solid-state hydrogen storage technologies. These advancements are set to make significant contributions to the sustainable development of clean energy technologies, positioning hydrogen as a key factor in the transition to a low-carbon global economy.