In the context of the United Nations Paris Climate Agreement of 2016, the majority of the global leading automotive manufacturers are committed to electrifying their fleets. A particular challenge in achieving this transformation is the efficient and economical development of new types of battery systems to meet the high customer requirements for electric range and fast-charging capability as well as legally required safety standards. These requirements must be guaranteed over the entire vehicle lifetime. However, the battery ages over time due to electrochemical degradation effects during operation. As a consequence, the battery state needs to be continuously monitored and analyzed. Thereby, new ways of analysis are required, as the current characterization of the battery state during maintenance is associated with high financial efforts, time-consuming measurement procedures, and is limited to a low number of available test capacities. An innovative and scalable alternative is offered by deploying battery models. In this context, this thesis addresses the research question to what extent battery-electric modeling is applicable to determine the battery state, using only in-vehicle operating data. To this end, this approach is divided into two research areas: First, the modeling of current electric battery behavior based on in-vehicle data, and second, the methodology for analyzing the battery state. While conventional battery models are mainly based on physical system representations, this thesis focuses on novel data-driven methods that are able to independently learn relevant correlations from vehicle operational data and to use this information to continuously update the battery model. In a preliminary analysis, artificial neural networks with a sliding window approach proved to be a suitable candidate to learn the electric battery behavior during operation. In terms of the methodology, the analysis of the battery state is considered separately at cell-level and system-level due to the high complexity of the ...