International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Hybrid PV–TEG Systems for Waste-Heat Recovery: Material Performance, Thermal Strategies and ML-Driven Optimization

Author(s) Arshit, Geena Sharma, Aman Choudhary
Country India
Abstract The efficiency of photovoltaic (PV) systems is fundamentally limited by their inability to harvest the thermal component of solar irradiance, with typical PV cells converting only 20–25% of incident energy into electricity. This study introduces a Hybrid Thermoelectric–Solar Optimized Model (HTSOM) that couples a standard PV array with a thermoelectric generator (TEG) and adaptive thermal management to valorize waste heat and enhance total power output.
Using MATLAB and Simulink, we developed a mixed‐methods simulation framework encompassing real‐time solar irradiation modeling, finite‐difference thermal–electrical circuit analysis, and machine‐learning–based efficiency prediction. We conducted comparative ZT analysis of three leading thermoelectric materials—Bi₂Te₃, PbTe, and SiGe—across temperatures ranging from 300 to 700 K. Results confirmed that Bi₂Te₃ performs best under unconcentrated PV conditions (ZT≈1.02 at 340 K), while PbTe and SiGe are more suitable for high‐temperature applications.
The hybrid PV–TEG system simulated under solar irradiance levels of 200–1000 W/m² yielded efficiency improvements of 0.5–2.4% over PV‐only configurations, with optimized phase‐change material (PCM) cooling contributing an additional 0.8–1.2% gain. Three machine learning regression models achieved R² = 0.96 prediction accuracy, and particle-swarm optimization refined TEG geometry for maximum performance.
Keywords: Hybrid PV–TEG, Thermoelectric Materials, MATLAB Simulation, Figure of Merit (ZT), Machine Learning, Phase‐Change Material, Maximum Power Point Tracking, Solar Energy Harvesting
Field Engineering
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-17
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.57794
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