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In this paper, we present our work on high-efficiency multi-junction polymer and hybrid solar cells. The transfer matrix method is used for optical modeling of an organic solar cell, which was inspired by the McGehee Group in Stanford University. The software simulation calculates the optimal thicknesses of the active layers to provide the best short circuit current (JSC) value. First, we show three designs of multi-junction polymer solar cells, which can absorb sunlight beyond the 1000 nm wavelengths. Then we present a novel high-efficiency hybrid (organic and inorganic) solar cell, which can absorb the sunlight with a wavelength beyond 2500 nm. Approximately 12% efficiency was obtained for the multi-junction polymer solar cell and 20% efficiency was obtained from every two-, three- and four-junction hybrid solar cell under 1 sun AM1.5 illumination.

PbS quantum dots (QDs) are a promising nanostructured material for solar cells. However, limited works have been done to explore the active layer thickness, layer deposition techniques, stability improvement, and cost reduction for PbS QD solar cells. We address those issues of device fabrication herein and suggest their possible solutions. In our work, to get the maximum current density from a PbS QD solar cell, we estimated the optimized active layer thickness using Matlab simulation. After that, we fabricated a high-performance and low-cost QD photovoltaic (PV) device with the simulated optimized active layer thickness. We implemented this low-cost device using a 10 mg/mL PbS concentration. Here, spin coating and drop-cast layer deposition methods were used and compared. We found that the device prepared by the spin coating method was more efficient than that by the drop cast method. The spin-coated PbS QD solar cell provided 6.5% power conversion efficiency (PCE) for the AM1.5 light spectrum. Besides this, we observed that Cr (chromium) interfaced with the Ag (Cr–Ag) electrode can provide a highly air-stable electrode.

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In this paper, we present our work on Maximum Power Point Tracking (MPPT) using neural network. The MATLAB/Simulink is used to establish a model of photovoltaic array. The Simulink model is tested with different temperature and irradiation and resultant I-V and P-V characteristics proved the validation of Simulink model of PV array. We collected a set of data from the Simulink model of PV array after simulated under a range of irradiation and temperature. The data collected from the system is used to train the neural network. When we tested the neural network with different irradiance and temperature, we see that the neural network can accurately predict the maximum power point of a photovoltaic array. In this paper, the backpropagation training algorithm is used to train the neural network. Comparisons of MPPT with P & O algorithm and without MPPT tracker are also shown in this paper. It is demonstrated that the neural network based MPPT tracking require less time and provide more accurate results than the P&O algorithm based MPPT.