4.6 Article Proceedings Paper

Numerically Based Reduced-Order Thermal Modeling of Traction Motors

期刊

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 57, 期 4, 页码 4118-4129

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2021.3077553

关键词

Computational modeling; Traction motors; Heat transfer; Atmospheric modeling; Solids; Fans; Windings; Computational fluid dynamics (CFD); conjugate heat transfer (CHT); digital twin; e-mobility; electric machine; finite volume method; frozen rotor; railway application; reduced order modeling (ROM); thermal resistances; traction motor

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This article introduces a numerical approach using reduced-order modeling to analyze the thermal behavior of electric traction motors, and the results show good agreement between measured and estimated values, indicating high accuracy of the reduced-order model in predicting motor thermal performance.
This article presents an approach based on numerical reduced-order modeling to analyze the thermal behavior of electric traction motors. In this article, a single conjugate heat transfer analysis provides the possibility to accurately predict thermal performances by incorporating both computational fluid dynamic and heat transfer modules. Then, the developed model is used as the basis for deriving a fast reduced-order model of the traction motor enabling prediction of motor thermal behavior in duty cycles with a high number of operating points. All the results achieved are verified using flow and temperature measurements carried out on a traction motor designed and built for a traction application. A good agreement between the measured and estimated values of flows and temperatures is achieved while keeping the computation time within a reasonable range for both the full-order and reduced-order conjugate heat transfer models. The optimized full-order model can be run in minutes and the reduced-order model computation time is less than one second per operating point. The transient simulation based on the reduced-order model is conducted and both the learning phase and validation results are well illustrated. It is shown than the deviation of the reduced-order model in estimating the motor thermal performance is less than one celsius degree from the full-order model.

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