The focus of this study is on presenting the concept of the Mini Company initiative launched in 2023 in the ZalaZONE automotive science park and proposing its development through international good examples. The aim of the project, based on the learning factory model, is to enable dual university students to experience the real market environment in a realistic, educational environment. The search for international good examples can help to further develop the concept, especially regarding the future role of automotive stakeholders. The presented results and good examples can be used in new research projects, develop teaching methods, and strengthen cooperation between industry and academia.

As electric motoring becomes more and more widespread, it is important to develop appropriate diagnostic measurements, especially for the increased electrical system. The most important part of the electrical system is the high voltage lithium-ion battery. Monitoring battery condition is essential to avoid failures and extend battery life, as these batteries degrade over time depending on the number of cycles, operating temperature, and charging habits. The project presented the contactless diagnostics of a Volkswagen e-Golf lithium-ion battery and analyze its capacity degradation through data acquisition via the Controller Area Network (CAN). The developed method allows to analyze the battery status and performance without disruption, which contributes to a more sustainable and economical vehicle usage. The measurement procedures include the analysis of the values of the state of health (SOH) and state of charge (SOC) indicators. The results will also provide insights into the optimization of the use of diagnostic tools and future battery maintenance options. To validate the method, two measurement scenarios were conducted: one on a chassis dynamometer and another under real-world driving conditions. The findings confirmed that contactless data acquisition can effectively detect cell imbalances and early degradation signs. The approach outlined in this study supports the implementation of efficient, scalable diagnostic solutions in both research and industrial settings.