Optimizing Multi-Layer Perovskite Solar Cell Dynamic Models with Hysteresis Consideration Using Artificial Rabbits Optimization Article Swipe
Ahmed Saeed Abdelrazek Bayoumi
,
Ragab A. El‐Sehiemy
,
Mahmoud Badawy
,
Mostafa A. Elhosseini
,
Mansourah Aljohani
,
Amlak Abaza
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11244912
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11244912
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math11244912
- https://www.mdpi.com/2227-7390/11/24/4912/pdf?version=1702198073
- OA Status
- gold
- Cited By
- 5
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389542193
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389542193Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math11244912Digital Object Identifier
- Title
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Optimizing Multi-Layer Perovskite Solar Cell Dynamic Models with Hysteresis Consideration Using Artificial Rabbits OptimizationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-12-09Full publication date if available
- Authors
-
Ahmed Saeed Abdelrazek Bayoumi, Ragab A. El‐Sehiemy, Mahmoud Badawy, Mostafa A. Elhosseini, Mansourah Aljohani, Amlak AbazaList of authors in order
- Landing page
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https://doi.org/10.3390/math11244912Publisher landing page
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https://www.mdpi.com/2227-7390/11/24/4912/pdf?version=1702198073Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/11/24/4912/pdf?version=1702198073Direct OA link when available
- Concepts
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Mean squared error, Hysteresis, Robustness (evolution), Standard deviation, Computer science, Context (archaeology), Photovoltaic system, Root mean square, Statistics, Algorithm, Mathematics, Engineering, Physics, Paleontology, Chemistry, Electrical engineering, Quantum mechanics, Biology, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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