## 4 Discussion  
The resulting values for the specific heat capacity and the cross-plane thermal conductivity indicate a higher correlation of the thermal model parameters to the ambient temperature than to the SOC. Also, the specific heat capacity shows a more consistent behaviour [26; 30; 31; 32]. The averaged \(c_{p}\) concerning the SOC differs by 2.2% for all tests, showing slightly higher results for higher SOCs. The different averaged values by the temperature show a deviation of 6.5%, peaking at 35 \({}^{\circ}\)C as shown in Figure 12a. Furthermore, the information in Figure 12b shows the rising influence of higher \(T_{amb}\) on the test results. This leads back to the TIS testing schedule and the resulting temperature response due to less heat generated by a decreased ohmic resistance at higher temperatures. Examining the cross-plane thermal conductivity showed a more significant reliance on external conditions. While averaging the results per SOC, a minor deviation of 2.3% emerges, compared to the crucial impact of \(T_{amb}\). Here, the difference of 21.1% between the averaged values at 10 \({}^{\circ}\)C and 45 \({}^{\circ}\)C shows a linear dependency [33; 34] decreasing by \(-\)0.37%/K. [PERSON] et al. [18] also detected a similar behaviour using different measurement methods. A possible reason is the thermal contact resistance, as discussed in [5; 18]. In contrast, [3] did not observe such changes using lithium-iron-phosphate cells, leading to the assumption that the changes are highly dependent on cell chemistry. Thus, further investigations, not in the scope of this work, need to be conducted, especially considering these matters. Some considerations towards the applicability of the TIS have to be made, especially regarding the negative real part in the Nyquist plot. This behaviour represents at least a second heat capacity in the setup, further illustrated in the phase response of Figure 7b, showing a phase angle of less than \(-90^{\circ}\). An additional phase shift is caused by the impact of temperature measurements using physically contacted sensors [22], like the PT100 in this work. Also, the surroundings have to be considered in this context since every material has a heat capacity, which influences the resulting spectra. In this paper, the used model relies on a single RC element using only the heat capacity of the battery, which gives the primary response to the heat generated. In the electrical domain, models are usually described by the Foster model, utilising components connected in series. Here, every element can be more precisely distinguished. The thermal behaviour needs to be modelled differently to maintain the physical representation by the Cauer model, as shown in Figure 11b. The difference lies in the arrangement of the components, which are nested here. This concludes with the given thermal response and the caused spectra showing the aforementioned negative real part, representing a phase shift of more than \(-90^{\circ}\). To summarise, the negative part cannot be represented using the fitting function. However, this is neglected here since the influence (supposedly from the sensor) only changes the result insignificantly, as recorded in the literature [20; 21; 23]. Since a quasi-stationary state is reached at the beginning of the test schedule by increasing the length of the first measurement point, the cell's behaviour does not depend on the test schedule beforehand. That is important for further usage of TIS as an addition to standard battery check-up tests, using only slight adaptions to the test setup and increasing the testing time by approximately one day. The test is easily reproducible, showing only minor derivation over the conducted tests, for instance, while comparing the frequency bands in Figure 8. The test results mainly differ when changing the test setup or using another overall test setup, which causes changes in the thermal resistance, \(R_{poly}\), which covers the heat transfer to the surroundings.