## 4 Discussion  
The tests were performed in three different scenarios: constant, stepped constant, and dynamic. During the initial constant tests, the car was accelerated to 30 km/h, 40 km/h, and 50 km/h, respectively, and kept this speed for 900 s to collect data. A pretty bad cell condition would have been detected already in this case. Still, during these tests, the aim was to set up data collection and saving. As expected, slight variations were observed between battery cells. The step was held at 30 km/h, 40 km/h, and 50 km/h during constant load, with each case sampled for 300 s.  
Thus, there were some transient conditions already during the measurements. During the evaluation, it was found that 1.5 s data saving was insufficient and more efficient to use 1.2 s data saving. The last step was the dynamic load measurements, which lasted 900 s, but included several accelerations and braking (also regenerative). From a battery diagnostic point of view, these are the most helpful measurement and related analyses because the more significant load peaks caused several transients that can be investigated. Based on the results, it was discovered that serial data readout could return several errors to the system. The major problem was that voltage values sorted under the same time stamp showed a significant deviation from each other. This is due to the high probability of a load arriving during each cell's serial readout during a cycle in the dynamic case. Thus, the cell values read out before they give a higher voltage value than those sampled after the load. A solution to this problem could be the windowing (clustering) method, whereby the three adjacent values of the incoming are filtered through the entire measurement data set. The advantage of this method is that the number of hits is significantly reduced; the disadvantage is that the number of possible false/faulty cases is reduced due to the specificity of the method. The other method is to vary the data readout, whereby fewer cell voltage values are tested but sampled more than once. In this way, cells that are considered to be faulty are targeted. The disadvantage of this method is that new measurements have to be taken. Both methodologies can be used to solve the problem, but the most effective way to solve the problem will likely be to unify the two. Both methods were applied in the tests. The results from three constants, one stepped constant, and three dynamic measurements were applied as a baseline test. Averaging was performed on these tests; the multiple-cycle measurements were presented as a validation test. Table 2 summarizes the critical values defined during each measurement. Figure 15: Aggregated error factor assigned to the cell identifier in the cyclical measurement. Table 2 shows the results of the three different evaluations. The first column shows the cell ID, the second column shows the results without averaging, and the third column shows the AVR values (for the differences between the two solutions, see Figure 10) according to Figure 13. The fourth column shows the results of the validation measurements. It is important to note that only critical cases (ID) have been included in Table 2. Red highlighting indicates cells with failures in that aspect of the assessment, and yellow indicates those that require further monitoring. From the comparison, it can be seen that several failures in the AVR results were also moved to the adequate category. It could be due to data loss, but it could also be due to a possible incorrect classified case. Based on the validation test, both cases could be present, e.g., the cell with the IDs 7, 32, and 71 did not appear to be faulty after averaging, but based on the validation, it clearly seems to be faulty. The cells with IDs 5, 40, and 63 appear to be defective based on the first measurement, but the validation test and averaging suggested only monitoring. The solution could be applying a combination of the three methods, weighting and artificial intelligence, to select the cells with errors.