The use of optimization techniques has been extensively adopted in vehicle design and with the increasing complexity of systems, especially with the introduction of new technologies, it plays an even more significant role. Market competition, stringent mandatory emission regulations and the need for a future sustainable mobility have raised questions over conventional vehicles and are pushing toward new cleaner and eco-friendly solutions. Fulfilling this target without sacrificing the other vehicle’s requirements leads to extremely challenging tasks for vehicle designers. The use of virtual prototyping emerges as a possible breakthrough allowing to rapidly assess the effect of design changes and the impact of new technologies. The study presented in this work provides a suitable approach to compare different vehicle powertrain architectures through optimization techniques and deploying model-based simulation to rapidly assess vehicle performances. The vehicle model is defined at the components level through scalable models obtained from based on detailed simulation. An optimal energy management is applied to the power sources and transmission gear shifting. The optimization technique consider the main design variables of the various components including vehicle chassis and extensively exploits the design space. The multi-objective optimization considers vehicle’s consumption, emission, range, longitudinal and lateral dynamics, costs and further performances to comprehensively assess the vehicle. The results allow to compare four different powertrain architectures: combustion engine vehicle, hybrid electric vehicle with parallel and series configuration, and battery electric vehicle. The results allows furthermore to identify technological limitations and conflicts among the different objectives. A critical analysis over the main design variables allows to identify the more suitable values and in particular, for combustion engine, gearbox and electric traction drive detailed comparisons are provided.


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    Titel :

    Multi-objective vehicle optimization: Comparison of combustion engine, hybrid and electric powertrains


    Beteiligte:

    Erscheinungsdatum :

    2020-01-01



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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