A method for the optimal design of complex systems is developed by effectively combining multi-objective optimization and analytical target cascading techniques. The complex systems with high dimensionality are partitioned into manageable subsystems that can be optimized using dedicated algorithms. The multiple objective functions in each subsystem are treated simultaneously, and the interactions between subsystems are managed using linking variables and shared variables. The analytical target cascading algorithm ensures the convergence of the optimal solution that meets the system level targets while complying with the subsystem level constraints. A design optimization of electric vehicles with in-wheel motors is formulated as a two-level hierarchical scheme where the top level has a model representing the electric vehicle and the bottom level contains models of battery and suspension. The vehicle model includes an electric motor model and a power electronics model. Pareto-optimal solutions are derived holistically. The effectiveness of the proposed method for optimizing the complex systems is compared against the conventional all-in-one optimization approach.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Multidisciplinary Design of Electric Vehicles Based on Hierarchical Multi-Objective Optimization



    Erscheinungsdatum :

    2019-01-01



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Multi-Objective Pareto Concurrent Subspace Optimization for Multidisciplinary Design

    Huang, C.-H. / Bloebaum, C. L. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2004


    Multi-Objective Pareto Concurrent Subspace Optimization for Multidisciplinary Design

    Chen-Hung Huang / Jessica Galuski / Christina Bloebaum | AIAA | 2007