The distribution of relaxation times (DRT) method is an efficient approach for analyzing electrochemical impedance spectroscopy (EIS) of fuel cells. However, facing electrochemical processes with close characteristic times/frequencies, low frequency resolution of DRT puts a damper on the analysis of results. To address this problem, a new DRT method integrating elastic net regularization and quantum particle swarm optimization (QPSO) algorithm is proposed in this study. Elastic net regularization combines the advantages of Lasso's feature selection and Tikhonov's coefficient shrinkage, thereby enhancing the frequency resolution of DRT. The use of the QPSO algorithm avoids the manual selection of parameters. A comprehensive evaluation of the developed method is conducted using both synthetic EIS and experimental EIS. The results show that the developed DRT method shows good performance and the reconstructed DRT functions exhibit high frequency resolution and accuracy. Overall, the proposed DRT method holds promise for advancing the characterization of fuel cells.