This article proposes loss and thermally optimized copper and aluminum structures for automotive electrical machines. A first batch of multiphysics optimization is performed parametrically for six different electrical conductor topologies. Then, a multiphysics—thermal and electromagnetic—hybrid parametric and topology optimization coupled with multiobjective differential evolution (MODE) and with an additional step of local search (LS) is proposed. The introduced topology optimization (TO) algorithm is explained in detail and applied to optimize electrical conductor geometries of pure Cu and AlSi10Mg. After that, the manufacturing by additive manufacturing (AM) of the most promising model is presented. The produced topology is benchmarked against a well-known fully rectangular structure. The proposed topology-optimized geometry has been found to improve conventionally manufactured electrical conductors at intermediate frequencies, around 600–800 Hz, and improves considerably at high frequencies, achieving a reduction in losses of 58% at 2000 Hz compared to a conventional rectangular copper structure.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multiphysics Topology Optimization of Aluminum and Copper Conductors for Automotive Electrical Machines


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    2547391 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Topology Optimization in Multiphysics Problems

    Sigmund, O. / AIAA / United States; Air Force et al. | British Library Conference Proceedings | 1998



    Topology Optimization Integrated Deep Learning for Multiphysics Problems

    Kazemi, Hesaneh / Seepersad, Carolyn / Kim, H. Alicia | TIBKAT | 2022


    Topology Optimization Integrated Deep Learning for Multiphysics Problems

    Kazemi, Hesaneh / Seepersad, Carolyn / Kim, H. Alicia | AIAA | 2022