Optimizing Multi-Layer Perovskite Solar Cell Dynamic Models with Hysteresis Consideration Using Artificial Rabbits Optimization

Ahmed Saeed Abdelrazek Bayoumi, Ragab A. El-Sehiemy (), Mahmoud Badawy, Mostafa Elhosseini, Mansourah Aljohani and Amlak Abaza
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Ahmed Saeed Abdelrazek Bayoumi: Physics and Engineering Mathematics Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Ragab A. El-Sehiemy: Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Mahmoud Badawy: Department of Computer Science and Informatics, Applied College, Taibah University, Al-Madinah Al-Munawarah 41461, Saudi Arabia
Mostafa Elhosseini: Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Mansourah Aljohani: College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia
Amlak Abaza: Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Mathematics, 2023, vol. 11, issue 24, 1-16

Abstract: Perovskite solar cells (PSCs) exhibit hysteresis in their J-V characteristics, complicating the identification of appropriate electrical models and the determination of the maximum power point. Given the rising prominence of PSCs due to their potential for superior performance, there is a pressing need to address this challenge. Existing solutions in the literature have not fully addressed the hysteresis issue, especially in the context of dynamic modeling. To bridge this gap, this study introduces Artificial Rabbits Optimization (ARO) as an innovative method for optimizing the parameters of an enhanced PSC dynamic model. The proposed model is constructed based on experimental J-V data sets of PSCs, ensuring that it accounts for the hysteresis characteristics observed in both forward and backward scans. The study conducted a rigorous statistical analysis to validate the Modified Two-Diode Model performance with that of the Energy Balance (MTDM_E) optimized using the innovative ARO algorithm. The performance metric utilized for validation was the Root mean square error (RMSE), a widely recognized degree of the differences between values predicted by a model and the values observed. The statistical analysis encompassed 30 independent runs to ensure the robustness and reliability of the results. The summary statistics for the MTDM_E model under the ARO algorithm demonstrated a minimum RMSE of 4.84E−04, a maximum of 6.44E−04, and a mean RMSE of 5.14E−04. The median RMSE was reported as 5.07E−04, with a standard deviation of 3.17E−05, indicating a consistent and tight clustering of results around the mean, which suggests a high level of precision in the model’s performance. Validated using root mean square error (RMSE) across 30 runs, the ARO algorithm showcased superior precision in parameter determination for the MTDM_E model, with a mean RMSE of 5.14E−04, outperforming other algorithms like GWO, PSO, SCA, and SSA. This affirms ARO’s robustness in optimizing solar cell models.

Keywords: artificial rabbits optimizer; parameter extraction; dynamic models; hysteresis features; multi-layer perovskite solar cells (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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