This paper presents a design optimization framework that jointly optimizes battery size with the geometric dimensions of the electric motor for a family of battery electric vehicles, with global optimality guarantees. As opposed to conventional models, we devise a quasi-static model of the motor internal losses as a function of both its geometry and operating points, using a convex surrogate modeling approach. Specifically, we implement a low-level motor scaling, capturing the impact on performance and losses of changing the motor geometry in axial and radial directions. Hence, we leverage the framework to solve a concurrent optimization problem and identify the optimal module sizing for a family of electric vehicles. Finally, we test our framework on a benchmark problem where we jointly design motor and battery for three different types of vehicles (a city car, a compact car, and a cross over), whereby the prediction efficiency is in line with the high-fidelity modeling software.


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    Titel :

    A Geometric Electric Motor Model for Optimal Vehicle Family Design


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Mastinu, Giampiero (Herausgeber:in) / Braghin, Francesco (Herausgeber:in) / Cheli, Federico (Herausgeber:in) / Corno, Matteo (Herausgeber:in) / Savaresi, Sergio M. (Herausgeber:in) / Clemente, Maurizio (Autor:in) / Borsboom, Olaf (Autor:in) / Salazar, Mauro (Autor:in) / Hofman, Theo (Autor:in)

    Kongress:

    Advanced Vehicle Control Symposium ; 2024 ; Milan, Italy September 01, 2024 - September 05, 2024



    Erscheinungsdatum :

    04.10.2024


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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