Multi-physics approach of bifacial PV systems using hybrid optimization of one- and two-diode models

• Developed a coupled optical–thermal–electrical framework for bifacial PV modules. • Introduced a hybrid two-stage method for accurate parameter extraction. • Performed detailed comparative analysis of one-diode and two-diode electrical models. • Demonstrated higher accuracy in capturing recombination and irradiance asymmetry. • Provided practical guidelines for selecting model complexity in diverse environments. . This study presents an integrated optical–thermal–electrical framework for analyzing bifacial photovoltaic (BPV) modules using the five-parameter one-diode and seven-parameter two-diode electrical models. Front and rear irradiance are determined through a view-factor optical formulation, and a detailed five-layer heat transfer model predicts cell temperature under varying environmental conditions. Electrical parameters are extracted using a hybrid analytical and optimization strategy that combines genetic algorithms with interior-point refinement to ensure stable identification under dynamic conditions. Validation results show that the two-diode model provides higher accuracy, reducing the power prediction error from 1.6 % in the one-diode model to 0.8 %. The one-diode model, however, offers comparable accuracy under near-standard conditions with reduced computational demand. Comparative hourly analyses show that the two-diode model yields more reliable power prediction under front–rear irradiance asymmetry typical of BPV systems. The largest difference occurs during summer noon, where the one-diode model overpredicts power by about 31.35 W, while in winter the discrepancy remains below 4 W. These results confirm that irradiance level, temperature, and rear-side contribution strongly influence the divergence between the models. Overall, these findings highlight the trade-off between model complexity and efficiency and provide useful guidance for selecting appropriate modeling strategies to enhance energy-yield prediction and optimize the design of BPV installations. .