## 4 Conclusions  
In this study, we demonstrated the effective data-driven identification of SoH by using the novel dynamic analysis method NFRA. First, an accelerated ageing test of an LIB at 25 \({}^{\circ}\)C was executed and a dynamic analysis with NFRA performed in defined cycle steps at constant environmental and measurement conditions. The nonlinear dynamics of the cell changed regarding the ageing protocol \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cell** & **Type** & **SoH\({}_{i}\)** & **SoH\({}_{SVM}\)** & **Accuracy/\%** \\ \hline B & identical & 28 & 29 & 3 \\ C & identical & 63 & 66 & 4 \\ D & non-identical & 100 & 96 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation of the degradation model using identical and non-identical cells at different SoHs. Figure 10: Degradation model for SoH estimation calculated by support vector regression (SVR) based on a highly ageing-sensitive data feature and in the inlet, the degradation feature values extracted from the data (blue) as well as the calculated values by the SVR model (red). Figure 9: Ageing-sensitive NFR data extracted by correlation analysis. with proceeding cycle numbers. The NFR increased monotonically with cycling, but the qualitative progress of the NFR spectra remained unaffected. The most likely ageing effects at the applied cycling current and temperature are electrolyte degradation and the growth of the SEI, respectively. Such ageing effects include capacity loss and increased cell impedance, which lead to higher NFR values, as observed in this ageing study. For identifying the most sensitive frequency range for ageing tests, a correlation analysis was performed. Here, the mid-frequency range from 0.2 Hz to 150 Hz shows the strongest correlation with LIB degradation. After extraction of extrapolated NFR at the \(y\)-axis intercept and the slope as informative features for LIB ageing, the extrapolated NFR values at the \(y\)-axis intercept were used to parametrise the SVR model for SoH degradation, as it shows a higher correlation to NFR ageing data. By using additional cells and data sets, the degradation model was validated and tested. It was shown that the degradation model can predict the SoH values with high accuracy. Taken together, these results demonstrate the usefulness of NFRA as an effective and fast SoH identification method as well as a versatile tool in the ageing diagnosis of LIBs in general. Furthermore, as the correlation coefficients indicate a perfect correlation of NFR to ageing for all frequencies between 0.2 Hz and 150 Hz, we suggest a reduction of the measurement range from 1 Hz to 100 Hz for the analysed cells, which leads to a dramatic reduction in measurement time of up to 92% compared to standard measurement protocols. Future work will take the evaluation of NFRA data sets of commercial cells and the impact of changes in the measurement scenarios, such as temperature variations, as well as ageing conditions on the SoH identification algorithm into account. Furthermore, we presently work on the effect of temperature distribution within cells on NFRA; this is a first step to account the more complex behaviour of battery packs, which show temperature and property distributions. [PERSON] and [PERSON] wrote the manuscript; [PERSON] conceived, designed and performed the experiments; [PERSON] analysed the data with SVR; [PERSON] contributed to the analysis of results; U.K. contributed to the analysis of results, provided feedback to the content and participated in writing the manuscript. The IGF project 8415 N/1 of the research association for Drive Technology (FVA e.V.) was funded by the German Federation of Industrial Research Associations (AiF) within the program Industrial Collective Research and Development (IGF) of the Federal Ministry of Economics and Technology (BMWi). Furthermore, we appreciate the support of the Battery LabFactory Braunschweig, notably the Institute for Particle Technology, for manufacturing the electrodes. The authors declare no conflict of interest.