The contribution of artificial intelligence to phase change materials in thermal energy storage: From prediction to optimization

Shuli Liu, Junrui Han, Yongliang Shen, Sheher Yar Khan, Wenjie Ji, Haibo Jin and Mahesh Kumar

Renewable Energy, 2025, vol. 238, issue C

Abstract: Artificial Intelligence (AI) is leading the charge in revolutionizing research methodologies within the field of latent heat storage (LHS) by using phase change materials (PCMs) and elevating their overall efficiency. This comprehensive review delves into AI applications within the domain of PCM for TES systems, mainly including prediction and optimization. The review article emphasizes the crucial role of AI in predicting physical properties of composite PCM and its performance in LHS systems. Also, the review article highlights the significance of AI in optimizing the structure and layout, as well as the operation and control strategies of latent heat storage systems using PCMs across various research fields. The study at hand discusses literature encompassing both experimental and theoretical articles that detail the integration of AI techniques within TES systems by using PCM, and compares the advantages and limitations of AI prediction models and optimization algorithms with existing typical technologies in the field of LHS. The summarization of the limitations in prior research has been presented, along with the proposal of potential avenues for performance enhancement of AI applied in LHS system. Additionally, the primary directions and challenges for future investigations have been emphasized, accompanied by suggested strategies.

Keywords: Thermal energy storage; Phase change materials; Artificial intelligence; Prediction; Optimization (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014812402041X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:238:y:2025:i:c:s096014812402041x

DOI: 10.1016/j.renene.2024.121973

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().