The modeling of photovoltaic (PV) systems is substantial for the estimation of energy production and efficiency analysis in the PV systems under the changing environmental conditions. A PV model mathematically expresses the electrical characteristic of the PV modules according to temperature and irradiance. The most popular electrical circuit models are the single-diode model (SDM) and the double-diode model (DDM). Considering accuracy and complexity, SDM was used in this paper. In the equivalent circuit model used to estimate the electrical behavior of the PV modules, the parameter estimating has become an optimization problem. In recent studies, it is seen that metaheuristic algorithms are often employed in solving this optimization problem. In this paper, a new six-parameter PV model is proposed to improve the accuracy of the five-parameter SDM, taking into account the temperature dependence of the series resistance. Particle swarm optimization (PSO) and a couple of metaheuristic algorithms have been executed to estimate six unknown parameters of the proposed model under standard test conditions (STC: 25 °C, 1000 W/m2, AM1.5) using current–voltage (I–V) data of PV module. In order to evaluate the performance of the proposed method under the changing environmental conditions, it was compared with the three methods commonly used in the literature. Accuracy of the proposed model has been indicated by the root mean square error (RMSE) within the range of current data and the model current values. Simulation results demonstrate that the proposed model can predict the I–V curve for the PV modules with high accuracy.