Optimization Of Solar Cell Performance Research Articles

Machine Learning Guided Strategies to Develop High Efficiency Indoor Perovskite Solar Cells

AbstractIndoor Perovskite Solar Cells (IPSCs) have recently gathered massive research attention, driven by their promising role in powering the continuously expanding Internet of Things (IoT) devices and simultaneous advancements in the Perovskite solar field. To further accelerate the development of IPSCs, a machine learning (ML) approach to assist the advancement of IPSCs is proposed in the current study. Here, a ML model to predict the most important performance parameters such as short circuit current (JSC), open circuit voltage (VOC), fill factor (FF), and power conversion efficiency (PCE) of IPSCs under various light sources and intensities is presented. This developed model can effectively predict the performances of Perovskite Solar Cells (PSCs) operated under indoor illumination close to the true/experimental values. The factors affecting the IPSC performance by Correlation matrix and SHAPley analysis are also analyzed. These findings demonstrate that the proposed ML model provides accurate predictions of VOC, JSC, FF, and PCE of IPSCs, ultimately contributing to the optimization of solar cell performance under indoor environments and the advancement of renewable energy technology.

Machine learning applications for photovoltaic system optimization in zero green energy buildings

In this paper, the energy supply of a zero-energy building with 220 square meters is considered using optimized nanocomposite solar panels with respect to maximum efficiency. An optimized hybrid machine learning method plays a key role in presenting solar panel modeling with over 0.99% accuracy. Predicting the properties of the nanomaterial solar cell in four different seasons is performed by efficient support vector machines (SVM), and k-nearest neighbors (KNN) machine learning algorithms. In addition, the KNN algorithm is optimized by the Particle Swarm Optimization (PSO) method to improve the capabilities of KNN and reveal the best performance criteria for the photovoltaic modeling characteristics. The parameters of the nanocomposite cells are optimized using the proposed novel Multidisciplinary Optimization Method (MDO) to increase the efficiency of the solar panel by up to 170%. Optimization of solar cell performance with nanocomposite material under energy consumption constraints is carried out to propose the best construction of cells with 3 layers. The presented approach as a solution and indicator for the next generation of commercial and residential buildings can increase the potential values of solar cells to at least 70%.

Relating band edge DOS occupancy statistics associated excited state electron entropy generation to free energy loss and intrinsic Voc deficit of solar cells.

Ever since the invention of solar cells, thermodynamics has been used to assess their performance limits and investigate advances in materials science and photovoltaic technology to reduce the gap between practical efficiencies and thermodynamic limits to photovoltaic energy conversion. By systematically addressing thermodynamic efficiency losses in current photovoltaics, ultrahigh efficiency photovoltaic can be expected. Currently, the non-radiative recombination of some ultrahigh efficient solar cells is almost completely suppressed, and the radiative recombination loss is the key to restricting the further improvement of device performance. This work relates the energy band edge electronic density of states (DOS) of semiconductor absorber and transport layer, excited/transfer state electronic entropy to thermodynamically inevitable energy loss during photoelectric conversion in solar cells. Through comprehensive theoretical analysis and device simulation, it is revealed why solar cells based on semiconductor material with a low DOS have higher Voc. On account of the basic limitations of thermodynamic laws on the energy conversion process, this work reveals a hidden variable that affects the photovoltaic performance and puts forward the band edge DOS engineering as a new dimension in performance optimization of solar cells apart from the traditional material and defect passivation engineering, etc. This work highlights the great importance of DOS engineering for further improving the performance of solar cell devices.

Spectroscopic Perception of Trap States on the Performance of Methylammonium and Formamidinium Lead Iodide Perovskite Solar Cells.

To enhance the efficiency and stability of the organic-inorganic hybrid perovskite (OIHP) solar cells, doping has been demonstrated as a straightforward method. Nevertheless, the perception of trap states regulated by doping and their effects on the performance of solar cells is not in-depth. Herein, typical OIHPs (CH3 NH3 PbI3 and Cs0.05 FA0.85 MA0.10 Pb(I0.97 Br0.03 )3 ) doped with RbI are employed to expound the doping mechanism in affecting the efficiency of devices. Systematic spectroscopic characterizations indicate that doping significantly influences the photocarrier dynamics via directly regulating the trap states. The results indicate that doping would reduce the trap density by passivating defects and induce extra trapping centers. This directly manipulates the transient transport of the photocarriers and finally influences the output of devices. The optimization of solar cell performance requires the tradeoff of competitive relation between the passivation and introduction of trapping centers. The results provide the spectroscopic perception on how doping concentration affects trap density, carrier dynamics, transport behavior, and ultimately the parameters of devices. It provides a straightforward guidance to the design and optimization of OIHP-based solar cells.

Unraveling the Hole-Selective Nature of Si/MoOX Heterojunction

MoO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> has emerged as an effective hole-selective layer for Si based heterojunction solar cells on account of its high workfunction. The hole selectivity of MoO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> is fundamentally different from other carrier selective films as it does not have an electron blocking energy barrier in form of conduction band offset with Si. Accordingly, much is unknown about the hole-selective nature of Si/MoO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> junction with many unresolved puzzles. Here, through a combination of detailed modeling and experiments, we provide a coherent, calibrated, and comprehensive model to describe unique hole selective features of Si/MoO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> junctions. The model accurately predicts the dark characteristics (ideality factor and reverse saturation current) and the open-circuit voltage of Si/MoO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> based heterojunction solar cells reported in the literature. In addition, the same model anticipates the trends for solar cells with a-Si as interlayer passivation. As such this work establishes the limits of achievable VOC and its functional dependence on MoO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> material parameters and, hence, could be of broad interest to enable further performance optimization of solar cells with large workfunction carrier selective layers such as V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> and WO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> .

Performance Optimization of Solar Cells Based on Heterojunctions with Cu2O: Numerical Analysis

AbstractA numerical analysis for performance optimization of solar cells based on heterojunctions with copper oxide (Cu2O) is performed. The equations given by the single-diode model for solar cell...

Optimization of the Geometric Design of Silicon Solar Cells under Concentrated Sunlight

The optimization of silicon solar cells is presented. The desired performance characteristics of a solar cell include a high conversion efficiency and a large power output through optimal grid contact design with minimal power losses under concentrated sunlight. Both square and rectangular shapes of linear grid contact patterns are considered for silicon solar cells with constraints on design parameters. The optical and ohmic losses are noted in computing the conversion efficiency and power output, which influence the structure and size of the solar cell and the grid contact design. In the optimization of solar cell performance, the various power losses caused by grid contact that influence the solar cell size, geometry of the fingers, busbars, and spacing between the metal grid lines are also considered. The proposed methodology permits the design of the solar cell for optimal cell performance even when some of the parameters, such as the size of the cell or the geometry of the fingers, are prespecified.

SiO2/TiO2 Hollow Nanoparticles Decorated with Ag Nanoparticles: Enhanced Visible Light Absorption and Improved Light Scattering in Dye‐Sensitized Solar Cells

Hollow SiO2 /TiO2 nanoparticles decorated with Ag nanoparticles (NPs) of controlled size (Ag@HNPs) were fabricated in order to enhance visible-light absorption and improve light scattering in dye-sensitized solar cells (DSSCs). They exhibited localized surface plasmon resonance (LSPR) and the LSPR effects were significantly influenced by the size of the Ag NPs. The absorption peak of the LSPR band dramatically increased with increasing Ag NP size. The LSPR of the large Ag NPs mainly increased the light absorption at short wavelengths, whereas the scattering from the SiO2 /TiO2 HNPs improved the light absorption at long wavelengths. This enabled the working electrode to use the full solar spectrum. Furthermore, the SiO2 layer thickness was adjusted to maximize the LSPR from the Ag NPs and avoid corrosion of the Ag NPs by the electrolyte. Importantly, the power conversion efficiency (PCE) increased from 7.1 % with purely TiO2 -based DSSCs to 8.1 % with HNP-based DSSCs, which is an approximately 12 % enhancement and can be attributed to greater light scattering. Furthermore, the PCEs of Ag@HNP-based DSSCs were 11 % higher (8.1 vs. 9.0 %) than the bare-HNP-based DSSCs, which can be attributed to LSPR. Together, the PCE of Ag@HNP-based DSSCs improved by a total of 27 %, from 7.1 to 9.0 %, due to these two effects. This comparative research will offer guidance in the design of multifunctional nanomaterials and the optimization of solar-cell performance.

Colloidal PbS Quantum Dot Solar Cells with High Fill Factor

We fabricate PbS colloidal quantum dot (QD)-based solar cells using a fullerene derivative as the electron-transporting layer (ETL). A thiol treatment and oxidation process are used to modify the morphology and electronic structure of the QD films, resulting in devices that exhibit a fill factor (FF) as high as 62%. We also show that, for QDs with a band gap of less than 1 eV, an open-circuit voltage (VOC) of 0.47 V can be achieved. The power conversion efficiency reaches 1.3% under 1 sun AM1.5 test conditions and 2.4% under monochromatic infrared (lambda=1310 nm) illumination. A consistent mechanism for device operation is developed through a circuit model and experimental measurements, shedding light on new approaches for optimization of solar cell performance by modifying the interface between the QDs and the neighboring charge transport layers.

In situ stress measurement for MOVPE growth of high efficiency lattice-mismatched solar cells

We have recently reported high efficiencies in a monolithic III-V triple-junction solar cell design that is grown inverted with a metamorphic 1.0 eV bottom In{sub .27}Ga{sub .73}As junction. The biaxial stress and strain grown into this highly lattice-mismatched junction can be controlled by varying the design of a step-graded Ga{sub x}In{sub 1-x}P buffer layer, in which most, but not all, of the 1.9% misfit strain is relieved. A multi-beam optical stress sensor (MOSS) is a convenient tool for in situ measurement of stress during metal-organic vapor phase epitaxy (MOVPE) for the optimization of solar cell performance. The analysis of stress from curvature data is complicated by significant temperature effects due to relatively small thermal gradients in our atmospheric-pressure MOVPE reactor. These temperature effects are discussed and approximations made to allow practical analysis of the data. The results show excellent performance of inverted In{sub .27}Ga{sub .73}. As solar cells grown with slight compressive stress, but degradation under tensile stress. The best devices had a V{sub oc} of 0.54 V and a dislocation density in the low 10{sup 6} cm{sup -2}. The in situ stress data is also compared with ex situ strain data derived from X-ray diffraction measurements.

Comparison and optimization of the performance of Si and GaAs solar cells

Designing solar cells involves optimizing the device makeup, such as the semiconductor layer thickness and the impurity doping concentration, as well as the device parameters, such as the minority-carrier lifetime, the minority-carrier diffusion coefficient, and the surface recombination velocity. Optimization of solar cell performance is normally carried out by varying the device makeup or a particular device parameters, and assuming the rest of the parameterrs are constant. These device parameters, nonetheless, are influenced by the device makeup and should not be considered as independent variables. We calculate the performance of Si and GaAs cells using device-makeup-dependent parameters determined from experimental observations or, if uncertainty arises, from physical judgement based on available information. Both n/p and p/n Si and GaAs optimized cell structures are suggested. For the specific device parameters used, our calculations show conversion efficiencies of 18.75% and 26.8% for the optimized, without texturized surface, Si and GaAs cells, respectively, under one AM1.5 global normal sun condition. For concentrator cell applications, the optimized Si and GaAs cells can yield 22.5% and 30.2% efficiencies, respectively. Comparison of model predications and experimental data shows good agreement.

Optimization of solar cell performance

A new technique for optimizing silicon solar cell performances has been established. The parameters to be optimized are replaced by independent variables without constraints. The variables include diffusion and recombination parameters, as well as non ideal effects such as band gap narrowing. The method allows a simultaneous optimization of parameters leading to maximization of solar cell efficiency and open-circuit voltage. It suggests an allowable range of the efficient parameters, and a new cell design criterion in terms of a thin base with a low recombination velocity.

Optical properties and quantum efficiency of a-Si1−xCx:H/a-Si:H solar cells

The optical properties of all components of heterojunction solar cells based on hydrogenated amorphous silicon (a-Si:H) have been measured individually. By combining these data with knowledge of the cell geometry, an envelope quantum efficiency curve is generated, being simply the fraction of incident photons absorbed in the i layer of the p-i-n cell. Resultant curves for typical cell dimensions are seen to agree remarkably well with observed values for cells with long minority-carrier collection lengths. The model is then used as a diagnostic tool, and finally is demonstrated to have predictive value in the quest for further optimization of solar-cell performance.