## 5 Conclusions  
A data collection, processing, and evaluation strategy for an electric vehicle battery system was developed. The advantage of the developed algorithm is that the method can be adapted to any electric or hybrid vehicle battery. Based on the results, it is possible to assess whether any of the cells in the battery system are faulty or weaker. Furthermore, its place becomes localizable. Several tests were executed with constant, stepped constant and dynamic measurements to check and evaluate the method. On this basis, it was concluded that for battery diagnostic purposes, dynamic loads could be applied; the higher load peaks create many transients that can be tested. According to the results, it can be observed that serial data readout can introduce errors in the system.  
The major problem is that voltage values sorted under the same timestamp showed high deviation from each other. It is due to the high probability of a load arriving during the serial readout of each cell during a cycle in the dynamic case. Thus, in turn, the cell values read out first give a higher voltage value than those sampled after the load. The solution to this problem is to vary the data readout, whereby fewer cell voltage values are sampled but sampled more. It allows targeted testing of cells that are taken into consideration to be faulty. The disadvantage of this method is that more measurements have to be performed. The authors have applied both the raw measurement data and solutions in this article. The results of the different approaches were evaluated in aggregate.  
\begin{table}
\begin{tabular}{c c|c c} \hline
**Cell.**ID & **Measurement** & **Measurement.**AVR** & **Validation Test** \\ \hline
4 & 27.60 & 12.45 & 4.90 \\ \hline
5 & 28.90 & 3.54 & 6.00 \\ \hline
7 & 60.07 & 12.72 & 11.16 \\ \hline
11 & 60.37 & 10.00 & 10.15 \\ \hline
21 & 60.48 & 60.78 & 11.00 \\ \hline
23 & 60.78 & 6.72 & 9.20 \\ \hline
29 & 60.34 & 7.44 & 8.40 \\ \hline
32 & 60.84 & 11.27 & 11.16 \\ \hline
37 & 38.87 & 17.59 & 5.90 \\ \hline
40 & 31.70 & 18.54 & 4.70 \\ \hline
47 & 36.95 & 36.41 & 5.35 \\ \hline
63 & 24.68 & 19.88 & 5.60 \\ \hline
71 & 60.77 & 11.20 & 11.10 \\ \hline
75 & 27.30 & 27.31 & 6.50 \\ \hline
83 & 10.00 & 50.25 & 5.60 \\ \hline
87 & 60.20 & 10.05 & 9.45 \\ \hline \end{tabular}
\end{table}
Table 2: Critical cells by ID in the different tests (based on Figure 13).  
The results showed that all three solutions marked the same cells as weaker. However, several good cells were placed in the possible error category without averaging.  
In contrast, with the averaging (AVR) method, several cells that appeared to be faulty were put into the adequate category. For multi-cycle analysis, however, the short measurement range is the bottleneck. Therefore, using all three methods together is recommended for proper defective cell detection.  
Conceptualization, S.K.S. and A.A.; methodology, S.K.S. and G.S.; software, S.K.S., G.S. and A.A.; validation, S.K.S.; fromal analysis, S.K.S.; investigation, S.K.S., I.L. and S.F.; resources, I.L.; data curation, S.K.S., G.S. and A.A.; writing--original draft preparation, S.K.S. and S.F.; writing--review and editing, S.K.S. and S.F.; visualization, S.K.S., G.S., A.A., S.F.; supervision, I.L. and S.F.; project administration, S.K.S.; funding acquisition, S.K.S. and I.L. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the European Union within the framework of the National Laboratory for Autonomous Systems (RRF-2.3.1-21-2022-00002).  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.