Synthesis tool for conventional and hybrid powertrains
Traffic and Transport Systems, Intelligent and Automated Traffic
Final Report Abstract
In this research project, a synthesis tool for conventional and hybrid powertrain concepts was developed. Computer-aided syntheses make it possible to examine all theoretically conceivable combinations of the aggregate design parameters and their interconnection to identify promising concepts depending on the defined requirements as well as evaluation criteria. By generating all theoretically possible parameter combinations, new topologies, aggregates or components can also be identified by means of synthesis. Since the number of degrees of freedom of a drive system is very high, new approaches are required in order to realize an optimization according to the defined criteria. For this reason, an identifier, was developed which enables optimization of the overall system on the basis of expert knowledge. The decisions made by the identifier during the optimization of a drive synthesis are supported by a data-based, self-learning system. In this way, existing expert knowledge, both in the form of physical correlations and development experience, is transferred into algorithms and program structures and used to optimize complex systems.
Publications
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Elektrifizierte Antriebe für automatisierte Fahrzeuge, Dissertation, Band 78, Schriftenreihe des Instituts für Fahrzeugtechnik TU Braunschweig
Sturm, A.
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Optimale Hybridantriebe mit synthetisierten Verbrennungsmotoren und Getrieben, HEV 2022 - Tagung Hybrid and Electric Vehicles, Gifhorn
Wolgast, C.; Sturm, A.; Eilts, P. & Küçükay, F.
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AI-based self-learning synthesis for electrified drives“, 45. Internationales Wiener Motorensymposium 2024, Wien
Sturm, A.; Henze, R.; Küçükay, F. Wolgast, C. & Eilts, P.
