As suggested in subsection 2.4, the DRT method using a Debye kernel is most useful for characterizing ZARC element contributions, therefore the focus here lies on the middle and upper frequency range of the EIS spectrum. In 6(c), 6(d), fit and residuals of a representative EIS spectrum are shown. The DRT-informed EIS fit and direct numerical EIS fit have a similar quality in the Nyquist plot, with comparable amplitudes of the residuals in the frequency domain despite considerably different DRT spectra and ECM parameters. Additional information from a DRT does not have a strong influence on the root mean square error between experimental and fitted data, consistent with results presented by Boukamp . However, DRT-based information helps to build a more physically reliable battery simulation model. Notice that while DRT residuals can be reduced to essentially heteroscedastic white noise, ECM fitting shows systematic residuals that are several times higher . Therefore, DRT is potentially suitable as a preprocessing step that contains the same information as the raw data, while an ECM fit is an effective, reduced -or compressed -representation.