Abstract: In order to evaluate and diagnose the aging degree of photovoltaic modules effectively, a quantitative method of judging the aging degree of photovoltaic modules based on the aging index of photovoltaic modules with three parameters was proposed. According to the output I-V characteristics of photovoltaic modules, a modified quantum particle swarm optimization (QPSO) algorithm was used to identify three aging parameters of photovoltaic modules, i.e., photogenerated current, series resistance, and parallel resistance. Then, the identification parameters were mapped to the standard test condition parameters. Finally, probabilistic neural network (PNN) was used to calculate the aging index of components and obtain the quantitative value of aging degree. Simulation and experiment results show that this method can effectively diagnose the aging degree of photovoltaic modules under different environmental conditions, and can provide reference for the fault pre-warning and lifetime forecast of photovoltaic modules.