The analysis and interpretation of electrochemical impedance spectroscopy (EIS) data are often complex and challenging, despite its popularity within the field of electrochemistry. Hence, recently, we have introduced novel data-driven approach for extracting the distribution of relaxation times (DRT) from impedance data in order to improve further analysis [1, 2]. This approach is based on the Loewner framework, which is a versatile and straightforward data-driven modeling method that computes reduced-order models in a state-space representation directly from frequency response data. Thus, it allows estimation of DRT without need of regularization procedures or iterative optimization algorithms.The effectiveness of this approach is successfully demonstrated by analysing the experimental EIS data of a polymer electrolyte membrane fuel cell (PEMFC). The results show that this method allows clear identification of the individual polarization processes based on their characteristic time constants (see Figure). Furthermore, the peaks observed in the DRT plot are correctly associated with different processes by examining spectra under various operating conditions. This is further validated by employing a physics-based model [3]. In this way, unambiguous fault identification and monitoring of the degradation state of the cell is possible and it can be the foundation for an on-board diagnostic tool.