In Elektro-, Hybrid- und Brennstoffzellenfahrzeugen kann das Thermomanagement zu einer deutlichen Verringerung der Fahrzeugreichweite führen, was optimale Thermomanagementstrategien erforderlich macht. In diesem Aufsatz wird eine Methode vorgestellt, die es ermöglicht, eine energieoptimale Strategie zur Erzeugung der benötigten thermischen Leistung zu bestimmen, wobei alle Komponenten (Pumpen, Ventile, Lüfter usw.) und Einflüsse, wie die Umgebungstemperatur, Fahrzeuggeschwindigkeit, Motor- und Batterietemperatur sowie die Kühlkreislauftemperaturen berücksichtigt werden. Die Methode ist allgemein formuliert, um den Entwicklungsprozess für beliebige elektrisch angetriebene Fahrzeuge zu beschleunigen und dabei eine maximale Energieeinsparung zu erreichen (z. B. durch Abwärmenutzung). Basierend auf Simulationen von einem Versuchsfahrzeug mit einem komplexen Kühl- und Heizsystem wird das Potenzial der Methode aufgezeigt.

    In electric, hybrid electric and fuel cell vehicles, thermal management may have a significant impact on vehicle range. Therefore, optimal thermal management strategies are required. In this paper a method for determining an energy-optimal control strategy for thermal power generation in electric driven vehicles is presented considering all controlled devices (pumps, valves, fans, and the like) as well as influences like ambient temperature, vehicle speed, motor and battery and cooling cycle temperatures. The method is designed to be generic to increase the thermal management development process speed and to achieve the maximal energy reduction for any electric driven vehicle (e.g., by waste heat utilization). In this paper, a method to determine the energy-optimal control strategy for vehicle thermal management has been described. By transforming the single-stage decision with a large amount of possible combinations into a multi-stage decision, the computation effort is reduced. The simulation results provide the different regions of possible control strategies at all conditions (ambient temperature, traction system temperature, ...) to get the minimal energy consumption for given thermal power requests of the battery, the cab and the traction system. This automatically implies optimization approaches as waste heat utilization and the optimal choice between passive fan cooling and active refrigerant cycle cooling. Based on a prototype setup of an electric vehicle, the reliability of the method has been shown.


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