This study provides a comprehensive review of the utilization of artificial intelligence (AI) technology in phase change material (PCM) energy storage. The review primarily focuses on its application in solar thermal utilization systems, electric vehicle/electronic device thermal management systems, and building energy efficiency systems.
1. Introduction Phase change energy storage materials (PCESM) refer to compounds capable of efficiently storing and releasing a substantial quantity of thermal energy during the phase transition process.
Phase change thermal storage systems offer distinct advantages compared to sensible heat storage methods. An area that is now being extensively studied is the improvement of heat transmission in thermal storage systems that involve phase shift . Phase shift energy storage technology enhances energy efficiency by using RESs.
In conclusion, the introduction and necessity of phase change materials in latent heat thermal energy storage systems are driven by their potential to enhance energy efficiency, reduce environmental impact, lower costs, improve comfort, and provide versatile and future-proof energy solutions (Akeiber et al., 2016).
Materials with phase changes effectively store energy. Solar energy is used for air-conditioning and cooking, among other things. Latent energy storage is dependent on the storage medium's phase transition. Acetate of metal or nonmetal, melting point 150–500°C, is used as a storage medium.
It emphasizes the investigation of new phase change materials (PCMs) that possess specific features, such as high latent heat, thermal conductivity, and cycling stability. The study investigates advanced methods such as nano structuring, hybridization, and encapsulation to improve the efficiency and dependability of PCESMs.
Thermal energy storage (TES) plays a vital role in advancing energy efficiency and sustainability, with phase change materials (PCMs) receiving significant attention due to their high latent heat storage
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Recent advancements in PCESMs have opened up opportunities for their extensive use in many industries, providing inventive solutions for effective energy storage,
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This research explored sustainable phase change materials (PCMs) for latent heat thermal energy storage systems, leveraging data-driven machine learning models.
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Photothermal phase change energy storage materials (PTCPCESMs), as a special type of PCM, can store energy and respond to changes in illumination, enhancing the efficiency of energy systems and
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This comprehensive review delves into AI applications within the domain of PCM for TES systems, mainly including prediction and optimization. The review article emphasizes
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Phase change materials (PCMs) represent a pivotal class of substances that store and release thermal energy through reversible transitions between solid and liquid states.
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To alleviate resource shortage and environmental pollution, solar energy can be converted into thermal energy stored in phase change materials and in turn generate electrical energy.
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In a recent issue of Angewandte Chemie, Chen et al. proposed a new concept of spatiotemporal phase change materials with high supercooling to realize long-duration storage
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Functional phase change materials (PCMs) capable of reversibly storing and releasing tremendous thermal energy during the isothermal phase change process have recently received tremendous
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