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Linear fitting Rule of I–V characteristics of thin-film cells based on Bezier function

Author

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  • Shi, Nan
  • Lv, Yanling
  • Zhang, Yuchen
  • Zhu, Xianhui

Abstract

Thin-film cells are a promising type of solar cell. To evaluate their performance in PV power generation, it is important to have a simple and accurate model of the I–V output characteristics, which describes the relationship between output voltage and current. However, the mathematical model that describes the I–V characteristics of thin film cells is a difficult-to-solve nonlinear equation. Additionally, the limited data provided by manufacturers makes it challenging to calculate all the unknown parameters in the equation, further complicating modeling. This paper proposes a method for fitting the output characteristic curves of thin-film PV cells using limited manufacturer data. The method uses two 2nd-order Bezier functions to fit the curves to the left and right of the maximum power point of the I–V characteristics. This is done by constructing a straight line that passes through the maximum power point, parallel to the line segment connecting the open-circuit voltage and short-circuit current points, and selecting control points for Bezier functions on the line. This approach ensures that the fitting results pass through the short-circuit current point, maximum power point, and open-circuit voltage point, while also ensuring smooth connection of the two fitting curves at the maximum power point. The paper identifies two optimal control point locations for Bezier functions, based on 523 different manufacturers and models of thin-film cells. A linear relationship between the optimal control point locations and module filling factors is established. Finally, the proposed linear laws are verified using 35 new thin-film cells and compared with other modeling methods. The results demonstrate that the proposed method has an average relative error of less than 0.9% for the linear law of the Bezier function, indicating its accuracy and simplicity.

Suggested Citation

  • Shi, Nan & Lv, Yanling & Zhang, Yuchen & Zhu, Xianhui, 2023. "Linear fitting Rule of I–V characteristics of thin-film cells based on Bezier function," Energy, Elsevier, vol. 278(PB).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223013919
    DOI: 10.1016/j.energy.2023.127997
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    References listed on IDEAS

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    1. Pindado, Santiago & Cubas, Javier, 2017. "Simple mathematical approach to solar cell/panel behavior based on datasheet information," Renewable Energy, Elsevier, vol. 103(C), pages 729-738.
    2. Gulkowski, Slawomir & Muñoz Diez, José Vicente & Aguilera Tejero, Jorge & Nofuentes, Gustavo, 2019. "Computational modeling and experimental analysis of heterojunction with intrinsic thin-layer photovoltaic module under different environmental conditions," Energy, Elsevier, vol. 172(C), pages 380-390.
    3. Abbassi, Rabeh & Abbassi, Abdelkader & Jemli, Mohamed & Chebbi, Souad, 2018. "Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 453-474.
    4. Orioli, Aldo & Di Gangi, Alessandra, 2013. "A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data," Applied Energy, Elsevier, vol. 102(C), pages 1160-1177.
    5. Dizqah, Arash M. & Maheri, Alireza & Busawon, Krishna, 2014. "An accurate method for the PV model identification based on a genetic algorithm and the interior-point method," Renewable Energy, Elsevier, vol. 72(C), pages 212-222.
    6. Boutana, N. & Mellit, A. & Lughi, V. & Massi Pavan, A., 2017. "Assessment of implicit and explicit models for different photovoltaic modules technologies," Energy, Elsevier, vol. 122(C), pages 128-143.
    7. Santiago Pindado & Javier Cubas & Elena Roibás-Millán & Francisco Bugallo-Siegel & Félix Sorribes-Palmer, 2018. "Assessment of Explicit Models for Different Photovoltaic Technologies," Energies, MDPI, vol. 11(6), pages 1-22, May.
    8. Ebrahimi, S. Mohammadreza & Salahshour, Esmaeil & Malekzadeh, Milad & Francisco Gordillo,, 2019. "Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm," Energy, Elsevier, vol. 179(C), pages 358-372.
    Full references (including those not matched with items on IDEAS)

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