LAUSR

Conventionalpermanent magnet (PM) machine temperature assessment approaches require physical, material, and loss distribution information. In practice, accurate structural information is rarely available, a lot of thermal parameters can only be calibrated based on extensive experiments, and the determination of various machine losses is also a demanding task. To solve these problems, an online temperature prediction method is proposed in this article. A general mathematical model that describes the relationship between machine temperature and loss variation is derived through analyzing a PM machine three-dimensional lumped parameter thermal network. With distributed sensors, this model can be determined purely from the measured data, which eliminates the need to acquire machine physical and material information. The analyses of loss distribution are also avoided by utilizing two measured temperatures. The evolution of all sensors can be concerned, so the prediction of the time and spatial distribution of temperature can be realized. The proposed method is validated by the experiments processed on a 10-kW PM machine under various working conditions. The influences of key parameters and the comparison with a neural network model are also demonstrated. The results suggest that the proposed method has the advantage of possessing closed-form expression and being computationally efficient.