A Numerical-Analytical Hybrid Approach for the Identification of SDM Solar Cell Unknown Parameters


  • R. Abbassi College of Engineering, University of Hail, Saudi Arabia | University of Tunis, ENSIT, LaTICE Laboratory, Tunisia
  • A. Boudjemline College of Engineering, University of Hail, Saudi Arabia
  • A. Abbassi Department of Electrical Engineering, University of Tunis, ENSIT, LISIER Laboratory, Tunisia
  • A. Torchani College of Engineering, University of Hail, Saudi Arabia | University of Tunis, ENSIT, LISIER Laboratory, Tunisia
  • H. Gasmi College of Engineering, University of Hail, Saudi Arabia | University of Tunis El-Manar, ENIT, Tunisia
  • T. Guesmi College of Engineering, University of Hail, Saudi Arabia | University of Sfax, ENIS, Tunisia


Appropriate modeling and accurate parameter identification of solar cells are crucial in the optimization of photovoltaic (PV) systems. The single-diode model (SDM), consisting of an ideal current source, an ideal diode, a shunt resistor and a series resistor, is widely used to simulate the behavior of PV cells/panels. In this article, a hybrid approach for identification of solar cell SDM parameters is presented. This approach uses the inverse of the slope of the I-V curve under short-circuit and open-circuit conditions and combines numerical and analytical solutions. Indeed, knowing that numerical methods require appropriate initial values, the main idea of the proposed approach is to provide these solutions by analytical methods. The comparison of obtained results with experimental ones, based on manufacturer’s datasheet, proves that the algorithm thus obtained requires less information from the manufacturer and improves significantly the parameter identification accuracy.


solar energy, PV cell parameters, I-V and P-V characteristics, single-diode model


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How to Cite

R. Abbassi, A. Boudjemline, A. Abbassi, A. Torchani, H. Gasmi, and T. Guesmi, “A Numerical-Analytical Hybrid Approach for the Identification of SDM Solar Cell Unknown Parameters”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 3, pp. 2907–2913, Jun. 2018.


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