Aug 13, 2026 · stat.APJ/K move · Enter open · S save
Žan Gorenc, Žiga Gradišar, Felix Mütter, Vanja Subotić+1
Jožef Stefan International Postgraduate School, Jamova cesta 39, Ljubljana, 1000, Slovenia · Institute of Thermal Engineering, Graz University of Technology, Inffeldgasse 25/B, Graz, 8010, Austria · Jožef Stefan Institute, Jamova cesta 39, Ljubljana, 1000, Slovenia · Faculty of Information Studies in Novo mesto, Ljubljanska cesta 30, Novo mesto, 8000, Slovenia
Estimating the distribution of relaxation times (DRT) fromelectrochemical impedance spectroscopy (EIS) is an ill-posed inverse problem that is highly sensitive to regularisation choices. We propose a physics-informed convolutional autoencoder that estimates DRT directly from EIS data without spectrum-specific tuning. A discretised relation between impedance and the DRT is embedded in the training process, constraining the network to produce impedance-consistent distributions. The model resolves overlapping relaxation processes in synthetic two-ZARC spectra and accurately reconstructs measurements from three independent solid oxide fuel and electrolysis cell datasets, with range-normalised errors below 1.1%. Decoder-probe analysis shows that the learned latent representation is organised according to relaxation timescale. Distances in this latent space capture operating changes, hydrogen-shortage events, and long-term degradation. The same lightweight architecture is applied across all datasets without modification, providing consistent DRT estimation and an interpretable basis for condition monitoring.