The DNN-DRT model was firstly benchmarked against synthetic experiments . Using the single ZARC model, we sought to evaluate the magnitude of the truncation 𝜀 and integration 𝜀 errors. We also studied the influence of the number of collocation points, 𝑁, and of the experimental noise, 𝜺 , on the DRT recovery. Next, we considered more complex models with overlapping features (2 × ZARC), discontinuities (fractal, piecewise constant (PWC), Gerischer), and inductive features (hook). Finally, we tested the DNN-DRT model against real EIS data from one battery and two fuel cells.