As already noticed by Borgia et al., UP regularization is fairly robust with respect to the exact value of the regularization parameter or, in this case, the weights, which play a similar role on a local scale . In this particular case, this is amplified by the broad DRT modes without any particularly narrow features, especially in the high frequency region where the noise level is lower and a higher resolution could be achieved. However, while visually the differences between the three DRTs may appear marginal, the residuals are more discriminating. With unweighted DRT (Figure ), systematic residuals above the random noise level are clearly apparent at frequencies higher than 1 kHz. Such residuals are indicative of lost information that is contained in the data. Particularly when looking for small changes during cycling of a battery, such a loss of information in the frequency range that is sampled most sensitively by EIS may be significant. Furthermore, an automatized, data-driven analysis of DRT may be able to discern features that are suppressed by unweighted data processing. Finally, batteries or battery components with narrow high frequency modes are expected to show a higher resolution with weighted DRT analysis.