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A novel parameter identification strategy based on COOT optimizer applied to a three-diode model of triple cation perovskite solar cells

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Abstract

The remarkable optoelectronic characteristics of hybrid metal halide perovskite semiconductors, such as high defect tolerance, extended carrier lifetime and diffusion length, and adjustable optical bandgap, have garnered much interest in the last decade. Therefore, this paper considers the experimental and mathematical modeling of triple-cation perovskite solar cells (PSCs) with two different device structures. It is challenging to construct a reliable mathematical model of triple-cation perovskite solar cells based on the three-diode model due to its complex nature. This is related to the perovskite materials' dynamics, nonlinearity, and sensitivity. This paper proposes a novel method incorporating a recent metaheuristic algorithm named COOT optimizer to estimate the optimal parameters of the three-diode equivalent circuit of triple-cation perovskite solar cells. The key idea is to use the swarm intelligence-based COOT to optimally achieve the PV panel's optimal parameters. The identification method benefits from the exploration and exploitation abilities of the COOT algorithm to obtain its parameters effortlessly and precisely. Two experiments are conducted in this work; the first is measured I–V datasets for a triple-cation perovskite (TC-per) solar cell at standard conditions. The second consists of the measured I–V datasets for a triple-cation modified perovskite (TCM-per) perovskite solar cell. During the optimization process, the nine unknown parameters of the three-diode model (TDM) are used as decision variables. The objective function to be minimized is the root-mean-square error (RMSE) between the measured and estimated data. An extensive comparative study is presented with other optimizers of the whale optimization algorithm (WOA), seagull optimization algorithm (SOA), sine cosine algorithm (SCA), ant lion optimization (ALO), and dragonfly algorithm (DA). Furthermore, statistical analysis of ANOVA is performed. The obtained results confirm the superiority of the proposed method in constructing a reliable model of the three-diode model of PSCs as it provides the least RMSE between the measured and estimated characteristics of 1.61E−05 in the first dataset. In contrast, the poorest algorithm (SCA) provides 1.03E−04. Similarly, in the second dataset of experiments, COOT achieves the least RMSE of 1.82E−05; meanwhile, the largest RMSE of 1.03E−04 using ALO. Based on the strong correlation between experimental and theoretical results using the COOT algorithm, we proposed a theoretical way (close to reality) to get the photovoltaic parameters of ideality factor and parasitic resistances in perovskite solar cell devices.

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Acknowledgements

This work was supported by the European Union's Horizon 2020 Marie Curie Innovative Training Network 764787 "MAESTRO" project. M. Elsenety is thankful to the Greek Ministry of Foreign Affairs and the Egyptian Government for his PhD Scholarship.

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Authors and Affiliations

  1. Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir, Saudi Arabia

    Hegazy Rezk

  2. Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt

    Hegazy Rezk

  3. Chemistry Department, Faculty of Science, Al-Azhar University, Nasr City, Cairo, 11884, Egypt

    Mohamed M. Elsenety

  4. Departement of Electrical Engineering, University of M’sila, M’sila, Algeria

    Seydali Ferahtia

  5. Institute of Nanoscience and Nanotechnology, National Centre for Scientific Research “Demokritos”, 15341, Agia Paraskevi Attikis, Athens, Greece

    Polycarpos Falaras & Alaa A. Zaky

  6. Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, 33511, Kafr El-Sheikh, Egypt

    Alaa A. Zaky

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Rezk, H., Elsenety, M.M., Ferahtia, S. et al. A novel parameter identification strategy based on COOT optimizer applied to a three-diode model of triple cation perovskite solar cells. Neural Comput & Applic 35, 10197–10219 (2023). https://doi.org/10.1007/s00521-023-08230-8

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