@Article{cmes.2026.084256,
AUTHOR = {Hamza Rafik, Oussama Khouili, Mohamed Louzazni, Petru Adrian Cotfas, Daniel Tudor Cotfas},
TITLE = {Optimization of Photovoltaic Systems via AI-Based Solar Tracking and MPPT: Trends, Challenges, and Bibliometric Insights},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/27869},
ISSN = {1526-1506},
ABSTRACT = {The rapid expansion of photovoltaic (PV) technologies has necessitated the enhancement of energy conversion efficiency by developing more and more sophisticated control and optimization techniques. In particular, novel MPPT methods combined with solar tracking systems and AI approaches emerge as a promising solution to surmount the barriers of the conventional PV systems. This research presents a critical assessment of the recent developments in the research area of PV systems with MPPT algorithms, solar tracking mechanisms, and AI-based techniques. Therefore, papers with publication years from 2021 to 2025 were selected using Web of Science Core Collection. Then, a series of bibliometric metrics were used in this paper, including publication output, citation impact, collaboration patterns, and keyword co-occurrence, to highlight the evolution of this research area. The significant growth of scientific productivity is depicted in the paper, together with the strong contribution of both emerging and developed economies, in addition to rising interest in intelligent control strategies, machine learning, and hybrid optimization methodologies. As a result, these findings provide important insights regarding recent research developments and future challenges and offer a useful reference for researchers and practitioners interested in high-efficiency PV systems.},
DOI = {10.32604/cmes.2026.084256}
}