A Data Mining Process Model to Support the Development of Energy Systems of Electric Vehicles

The development processes for electric vehicles are becoming increasingly demanding, partly due to the high complexity of the energy systems. Furthermore, the developers have to handle various usage requirements of the systems and diverse user behavior. Here, inductive development approaches can help developers deal with high complexities by identifying interrelationships. This work introduces a Data Mining Process Model for Energy Systems development (DMPMES) that has been adapted for the energy system’s highly technical domain to support the developers in their work. The model represents an interdisciplinary process requiring expertise in data science, data engineering, and the development of energy systems. It systematically supports the quick usage of fleet data, the building of algorithms, and the investigation of them and their outputs. The first application studies are discussed, where fleet data was used to investigate the system behavior under actual operating conditions and thus discovered anomalies in the energy systems. Machine learning algorithms can find these anomalies, which are investigated afterward. In this way, the developers are supported in their work, learning complex interrelationships and previously unknown knowledge about the energy system through the application of the DMPMES. Consequently, the DMPMES is a helpful tool that supports the systematic use of inductive development approaches in energy systems.