Boosting photoelectric performance of thin film GaAs solar cell based on multi-objective optimization for solar energy utilization

For enhancing the photoelectric performance of the GaAs photovoltaic cell, the non-dominated sorted genetic algorithm-II (NSGA-II) is employed to solve the multi-objective optimization problem, which consists of the thicknesses of active layer (GaAs) and three anti-reflection (AR) films (TiO2 -HfO2 -SiO2). Through maximizing (minimizing) the light absorption of photons with energies higher (lower) than the bandgap in the active layer directly and reducing the cost at the same time, the Pareto optimal solution is obtained. An increment in the absorptance, short circuit current and photoelectric conversion efficiency by 50%, 43.4% and 44.9% is achieved by the optimal GaAs solar cell due to excellent AR properties obtained by NSGA-II optimization. We systematically investigate the effects of carrier lifetime, surface recombination velocity, carrier mobility as well as doping concentration on the photovoltaic parameters, and acquire the limitations of these parameters. The strong robustness of the optimal GaAs solar cell enables it to be adapted to wide-angle sunlight incident, and the probability to achieve an absorptance of 0.963 is 97% even if there is a 20% machining error.