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Optimisation of thermal energy storage systems incorporated with phase change materials for sustainable energy supply: A systematic review

期刊

ENERGY REPORTS
卷 10, 期 -, 页码 2496-2512

出版社

ELSEVIER
DOI: 10.1016/j.egyr.2023.09.044

关键词

Thermal energy storage; Thermal battery; Optimisation; Phase change material; Data-driven machine learning

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Thermal energy storage systems, also known as thermal batteries integrated with phase change materials, have gained attention as a promising solution for sustainable energy supply. This study presents a systematic review of various thermal batteries and identifies factors affecting their performance, providing insights for future research and development directions.
Thermal energy storage systems, also known as thermal batteries integrated with phase change materials, have gained significant attention in recent years as a promising solution for sustainable energy supply. Thermal batteries can significantly promote a sustainable energy supply by boosting the efficiency and reliability of renewable energy systems, enhancing energy access in isolated regions, lowering greenhouse gas emissions, and enhancing energy security. However, there are still challenges to optimising these systems to maximise their efficiency and effectiveness. This study presents a systematic literature review of various thermal batteries for industrial, commercial, and domestic applications. The preferred reporting items for systematic reviews and meta-analyses guidelines were adopted for this review. The primary objective was to identify factors affecting thermal battery performance. Data collection was focused on research papers published from 2013-2023 extracted from the Scopus, Web of Science, and Google Scholar databases. The study findings highlight the importance of considering material thermophysical properties, design configurations, and operating conditions when optimising thermal batteries. Also, this study highlights the current state of knowledge in the field and suggests future research and development directions. In particular, artificial intelligence and machine learning are suggested to promote faster and more precise optimisation of thermal batteries. The findings of this study are useful to academia and industries promoting the adoption of sustainable energy solutions for a greener and more resilient future.

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