Permanent magnet synchronous machines (PMSM) are widely used in industrial and automotive systems. The thermal behavior of motor parts impacts the performance of electrical drive systems, and accurate temperature information is vital for system protection and controllability. Replacing temperature sensors with thermal estimation reduces the system cost and improves the system's robustness and reliability. However, due to the complexity of high-fidelity thermal models, real-time estimation algorithms are computationally burdensome. To mitigate this computational burden, this study utilizes the calibratable and scalable 11-node reduced-order lumped-parameter thermal network (LPTN) to estimate critical PMSM temperatures in real-time. The Romer Douglas-Peucker (RDP) method is used in this study to develop surrogate loss models for interior permanent magnet machines (IPMSM). The proposed model can be applied to both transient and steady-state operational conditions. However, the temperature is obtained from averaged power loss calculations over a pre-defined raster rate. MATLAB/Simulink is used to understand the behavior of proposed reduced-order LPTN, which are compared with Motor CAD FEA results and Dyne test results to understand the accuracy of proposed models.


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    Title :

    A Model-Based Lumped Parameter Thermal Network for Online Temperature Estimation of IPMSM in Automotive Applications


    Contributors:


    Publication date :

    2023-12-12


    Size :

    810931 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English