Laser Micro-Texturing and AI-Driven Optimization for Thermal Management of Photovoltaic Systems

Purpose. Photovoltaic (PV) and other renewable systems suffer efficiency and reliability losses from overheating. This review emphasizes the need for scalable, integrated thermal management solutions. Design / Method / Approach. The paper evaluates recent advances in laser-based surface micro-texturing as a promising strategy for thermal regulation. Controlled micro/nano-scale structures enhance heat dissipation, expand surface area, and tune wettability. The study also explores the role of artificial intelligence (AI) in predicting, designing, and optimizing laser-induced textures for simultaneous improvements in thermal, optical, and mechanical durability. Findings. Laser-processed surfaces provide multifunctional benefits such as enhanced convective cooling, anti-reflection, and self-cleaning, but most demonstrations remain confined to laboratory scale. AI methods including neural networks, evolutionary algorithms, and reinforcement learning show strong predictive capability and multi-objective optimization potential, offering pathways for industrial adoption. Theoretical Implications. The review establishes links between surface morphology, thermo-fluid dynamics, and optical behavior, and shows how AI-enabled digital twins can extend these relationships into predictive, generalized models. It also highlights opportunities for modelling coupled thermo-optical effects and advancing data-driven surface engineering. Practical Implications. Integrating laser texturing with AI-driven optimization could embed thermal regulation directly into device structures, reducing reliance on external cooling systems and improving field durability. Originality / Value. Unlike prior reviews, this work unites laser surface engineering and AI optimization into a roadmap for renewable energy devices, highlighting digital twins and techno-economic assessment as enablers for scale-up. Research Limitations / Future Research. Challenges include scalability, durability under harsh environments, limited AI training datasets, and insufficient lifecycle analyses, requiring cross-disciplinary collaboration. Article Type. Review Paper.