Optimization of ZnTe:O solar cell using genetic algorithms

Lakehal, Brahim; Lakhdar, N.; Chebbara, Fouad; Boulesbaa, Mohammed; Dendouga, Abdelghani; Bendib, T.

doi:10.1109/ccee.2018.8634467

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Optimizing Solar Cell Design1

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2023

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“…Genetic algorithms work by initializing a population of random cell designs, then iteratively selecting, breeding and mutating designs to improve performance [15][16][17]. Operators like tournament selection, uniform crossover, and Gaussian mutation are commonly used.…”

Section: Optimizing Solar Cell Designmentioning

confidence: 99%

Predictive Modeling and CIGS Integration for Multi-Junction Solar Cell Efficiency Enhancement

Zakarya,

Moustafa Yassine,

Tewfik

et al. 2023

Preprint

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Multi-junction solar cells comprised of stacked III-V semiconductor junctions represent the highest-efficiency photovoltaic technology, with recent demonstrations exceeding 47% efficiency . Optimizing the design and thickness of each junction is critical for maximizing performance . This work utilizes Silvaco TCAD tools to systematically optimize a 5-junction cell based on AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge similar to recent record cells . The junction thicknesses are varied using a predictive profiler to sample the parameter space . For each combination, the spectral absorption and I-V characteristics are simulated to determine the efficiency . Statistical analysis identifies the optimal thickness set that maximizes performance to 26% under 1 sun illumination .

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Section: Optimizing Solar Cell Designmentioning

confidence: 99%