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%0 Thesis %A Müller, Jonas %T Prädiktives Thermomanagement für hochflexible Zero-Impact Hybridfahrzeuge %I Rheinisch-Westfälische Technische Hochschule Aachen %V Dissertation %C Aachen %M RWTH-2026-01229 %P 1 Online-Ressource : Illustrationen %D 2025 %Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2026 %Z Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2025 %X The development challenges in the automotive industry are continuously increasing due to new technologies, regulatory requirements, societal trends, and changing customer mobility demands. This results in a high diversity of vehicle variants and a growing complexity of powertrains. In order to further reduce development times while maintaining high product quality and economic efficiency, the application of model-based methods for holistic powertrain design represents a promising approach. In this context, it is essential to identify the interactions of relevant target metrics under real driving conditions, with particular consideration given to numerous thermal effects within the powertrain and the driving environment. First, the developed design methodology is presented using a top-down approach. Based on this methodology, powertrain-relevant requirements for an exemplary target customer are translated into a specification sheet including dedicated test cases. Subsequently, the relevance of thermal management for different powertrain design target metrics is theoretically derived, and the state of the art of current thermal management systems and their control strategies are reviewed. To evaluate the resulting requirements, a full vehicle model with detailed thermal submodels is developed. Based on standardized test cases, a baseline design of a C-segment plug-in hybrid vehicle is carried out. The influence of different customer profiles on the design outcome is investigated using varying weighting factors. The target customer design is analyzed in four real-world driving scenarios with varying parameters such as ambient temperature and battery state of charge, focusing on the thermal impact on the target metrics. Finally, the potential of thermal management technologies and predictive driving functions is assessed in selected driving scenarios, taking into account the identified constraints of the baseline design. %F PUB:(DE-HGF)11 %9 Dissertation / PhD Thesis %R 10.18154/RWTH-2026-01229 %U https://publications.rwth-aachen.de/record/1027121