The electrification of the powertrain results in complex functions, such as the engine restart in hybrid vehicles. At the same time, large amounts of data are generated in the development process that are only used to a limited extent. An experience-based and less systematic procedure for the manual evaluation of individual measurements or an elaborate statistical evaluation of data often produce little knowledge in the development process of transmission functions. However, in the course of digitalization data must be used efficiently and intelligently.
The authors present a method that uses artificial intelligence to analyze large amounts of data automatically and efficiently to identify similarities and differences. The DBSCAN cluster algorithm is an important part of this method.
The method analyzes vehicle data of a hybrid vehicle engine restart. About 300 engine restarts are detected from vehicle measurements, twelve key performance indicators per start are evaluated and commonalities are identified with the help of the cluster algorithm. The evaluation results can be used to define important operating points for an engine restart and evaluate the engine restart strategy. This method supports the development engineer to get a better understanding of the system and gain new knowledge about the transmission functions from data.
Intelligent Data Analytics with Artificial Intelligence for Hybrid Engine Restart
Proceedings
2022-03-15
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Deutsch
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