Abstract An efficient and highly detailed Noise-Vibration-Harshness (NVH) modeling and analysis workflow for electric induction drives in E-mobility and hybrid applications due to magnetic force excitation is presented. It is well suited for predicting the acoustic and structural dynamic characteristics and the cross-domain causal physical paths for electric induction drives with high reliability in an early phase of advanced engineering. A combined nonlinear analytic and Finite-Element modeling structure for the electrodynamic and the structural mechanic domain is developed to embed the entire NVH-simulation in one simulation environment. Separating physical quantities as magnetic airgap forces and operational structural deflections into design inherent generic vector fields and solely operation point dependent amplitudes within a modal space representation yields an efficient simulation workflow. The working point dependent E-drive behavior can be described by order reduced models. Hence the NVH-simulation is finally performed based on modal force response superposition. This allows an effective connection of the NVH-models to various E-drive and control topologies such as induction drives for simulating the NVH-behavior of arbitrary operation cycles. The entire modeling and simulation procedure will be shown by means of a squirrel-cage induction E-motor in EV and hybrid applications. All significant electrodynamic and mechanic noise contributions, influences and sources will be analyzed. Critical working points and speeds are identified and operational structural deflections at resonances are highlighted. The high reliability of the presented NVH-modeling approach will be verified by using numerical and measured acoustic run-up test spectrograms for the induction motor.


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    Title :

    Efficient noise-vibration-harshness-modeling for squirrel-cage induction drives in EV applications


    Contributors:
    Kotter, Philipp (author) / Bischof, W. M. (author) / Köpf, C. (author) / Wegener, K. (author)


    Publication date :

    2017-01-01


    Size :

    24 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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