During the charging and discharging process, cells interact with each other, which means that individual cells are not entirely independent of each other, which can affect diagnostics. Locating the faulty cell or cells can be conducted with or without disassembling the battery pack. Disassembling tests usually give more accurate results for the battery SOH [28; 29; 30; 22], but they come with a loss of guarantee. In the case of non-disassembling tests, the vehicle's BMS or ECU (Electronic Control Unit) needs to communicate, which is not always easy. A further difficulty of battery diagnostics is that, in most cases, the classic (OBD--On-board diagnostics) readout only allows voltage monitoring at a system level; the cell-by-cell analysis is not possible and is usually disabled. Factory diagnostic tools may already be suitable for cell-level monitoring, but these tools are expensive and limited in availability. The aim is to create a test method that can be carried out relatively quickly, in an easy-to-implement way, without expensive equipment or a test station. A further difficulty is a need for a large amount of measurement data, in which each element has to be tested individually, which is both time and resource-consuming. Accurate prediction also requires diagnostics of systems in different states. Fault detection is currently a popular area of research, with several methods available in the literature [31; 32; 33; 34; 35; 36]. Determining an accurate range in good condition is difficult for vehicle manufacturers, as many factors can influence it. These include temperature, driving style, terrain, heat exchange systems, and other consumers. There are various standards [37; 38; 39] and tests NEDC (New European Driving Cycle), WLTP (World Harmonized Light Vehicle Test Procedure), and EPA (U.S. Environmental Protection Agency) to determine the range of vehicles. Still, these values are mostly applicable to new vehicles [40]. In addition, the various dealerships review the condition of the cells annually; however, troubleshooting is limited to a quick test, and the whole system or module is replaced in case of a fault. Thus, many cells still in good condition end up as waste. At present, fault finding only very rarely covers cell-level fault finding. However, there are several types of analysis in the search for the causes of failure, such as the 23-month study presented by [PERSON] et al. The test on a test bench, on a test track, and under storage conditions found that the latter was one of the most critical factors [41]. In addition, statistical [42], big data analysis methods [43], and voltage fault detection methods [31] are applied to detect faulty cases. Current research also aims to develop a general data extraction and processing strategy that is as universal as possible. The data retrieval is unique for each vehicle since each cell has a unique CAN ID, but the retrieval process is already generalizable. In this article, the authors will focus on a data retrieval strategy where no factory readout unit is available;all data are accessed individually. The retrieval process is conducted through the CAN network in a request and response structure. In this paper publication, the authors have also analyzed the speed and errors of the responses received. The authors also further investigate the cellular voltage deviations and possible errors of the e-Golf [44]. Several conventional algorithms are used in battery condition assessment and estimation, summarized in the following publication [45]. The approach is based on a computational hybrid algorithm detecting and locating the faulty cell. In the method the authors implemented, a fault diagnosis based on voltage deviation was performed a posteriori with the data already collected. The measurement methods and the tests carried out are presented in the second section, along with the data extraction and storage methodology and its limitations. In the third section, the evaluation of the measurement results and the search for errors are presented in detail.