Parameter extraction of solar cells
Bulk crystalline silicon is widely used in the photovoltaics market, but it does not have the best material parameters. Its bandgap is too low to be the best solar cell and also silicon is an indirect material which has low absorption co-efficient. Although this low absorption coefficient can be ove...
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Format: | Final Year Project |
Language: | English |
Published: |
2015
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Online Access: | http://hdl.handle.net/10356/62033 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Bulk crystalline silicon is widely used in the photovoltaics market, but it does not have the best material parameters. Its bandgap is too low to be the best solar cell and also silicon is an indirect material which has low absorption co-efficient. Although this low absorption coefficient can be overcome by light trapping, researchers have started looking into the context of organic solar cell. The back-lit silicon solar cell is simulated using Medici device simulator and MATLAB® to obtain the key parameters relating to its operation. The ideal thickness of the substrate has to be less than or equal to 70μm. In this back-lit solar cell design, its front surface comprises of both n-type collector and p-type substrate contact. To further analyse, the collector junction depth is extent from 1μm to 4μm and it is found that over or under extension of junction depth will result in a drastic drop in the cell power. In regards to the doping of collector and substrate, the concentration of 1016/cm3 and 1017/cm3 for the substrate and collector is able to achieve an optimum cell power of 22.9mW/cm2. Experimental data of organic cell is simulated using MATLAB® and OriginLab® to achieve the characterize parameters. MATLAB® is used to extract data such as open-circuit voltage, short-circuit current density, fill factor, maximum power point and efficiency. The remaining parameters series and shunt resistance, illuminated current and saturation current is then obtained through curve fitting. The resulted parameter from the two simulations done, show that the data set 3-4 has the optimum cell efficiency and data set 3-2 and 3-3 yield the least efficiency. |
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