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Despite its significant impact on performance and reliability, heat dissipation in solar cells, particularly silicon solar cells, has received relatively little attention. This paper presents a comprehensive two-dimensional (2D) simulation of heat transfer in silicon solar cells. Using COMSOL Multiphysics, we investigate the temperature distribution in conventional silicon solar cells by integrating optical, electrical and thermal modules. Subsequently, we focus on enhancing the performance of solar cells by investigating the effect of three key electrical and thermal parameters on their efficiency. These parameters include donor concentration (ND), acceptor concentration (NA) and temperature (T0). Using the Taguchi method and ANOVA analysis we designed an L9(33) orthogonal array to minimize experimental variance and identify the optimal control parameters. An artificial neural networks (ANN) based model has been developed.

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Coupled Optical-Electrical Thermal Modeling in Silicon Solar Cells Through Artificial Neural Network and Taguchi Method

  • 会议论文

  • 发表日期：

    2026年

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  • Zouhour Rhaim,
  • Fraj Echouchene,
  • Sabra Habli,
  • Mohamed Hichem Gazzah,
  • Habib Ben Aissia

摘要

Despite its significant impact on performance and reliability, heat dissipation in solar cells, particularly silicon solar cells, has received relatively little attention. This paper presents a comprehensive two-dimensional (2D) simulation of heat transfer in silicon solar cells. Using COMSOL Multiphysics, we investigate the temperature distribution in conventional silicon solar cells by integrating optical, electrical and thermal modules. Subsequently, we focus on enhancing the performance of solar cells by investigating the effect of three key electrical and thermal parameters on their efficiency. These parameters include donor concentration (ND), acceptor concentration (NA) and temperature (T0). Using the Taguchi method and ANOVA analysis we designed an L9(33) orthogonal array to minimize experimental variance and identify the optimal control parameters. An artificial neural networks (ANN) based model has been developed.