Despite the use of state-of-the-art thermal modeling tools in the design stage, the measured thermal behavior of prototype electrical machines can differ significantly from the modeled ones. This paper shows how a thermal model, based on the finite-element method, of an electric machine can be improved using inverse modeling techniques. In a thorough study, a forward high fidelity finite-element thermal model of a 4-kW axial flux permanent magnet (PM) machine is introduced and improved using inverse modeling techniques via noncollocated thermal sensors. Parametric model order reduction of the high fidelity finite-element thermal model based on the moment matching method is performed to make the recovery of the actual thermal parameters characterizing the thermal behavior of the axial flux PM machine tractable. Furthermore, the same reduced order model is used to identify the different power loss components in the machine. Experimental results confirm that the presented two-stage approach is capable of identifying the thermal parameters and losses with high accuracy.

中文翻译：

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轴向磁通永磁电机热参数和损耗识别的逆热建模方法


尽管在设计阶段使用了最先进的热建模工具，但原型电机的测量热行为可能与建模的有很大不同。本文展示了如何使用逆向建模技术改进基于有限元方法的电机热模型。在一项彻底的研究中，引入了 4 kW 轴向磁通永磁 (PM) 电机的正向高保真度有限元热模型，并通过非并置热传感器使用逆向建模技术进行了改进。基于矩匹配法对高保真有限元热模型进行参数模型降阶，使得表征轴向磁通永磁电机热行为的实际热参数易于恢复。此外，相同的降阶模型用于识别机器中的不同功率损耗分量。实验结果证实，所提出的两阶段方法能够高精度地识别热参数和损耗。