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.

Conventional non‐rare‐earth permanent magnet (PM) electrical machine can hardly achieve comparable performance with the neodymium iron boron (NdFeB) machine due to the low coercivity and remanence. To solve this problem, a new kind of electrical machine using only Alnico magnet is proposed in this study. The novel rotor topology eliminates rotor iron core, and thus effectively limits armature reaction and improves torque density. A kind of rapid assessment method for the demagnetisation effect of q current in different slot–pole combinations through magnetomotive force and winding factor is presented and verified by 2D finite‐element analysis. The optimisation of critical dimensions is demonstrated, and the trade‐off between high PM utilisation and low demagnetisation effect is emphasised. After that, the good torque–current linearity and robust to demagnetisation characteristics of the proposed topology are proved through a comparison with two rare‐earth‐free machines. Finally, the performance comparison with a manufactured conventional NdFeB machine under the same volume constraints is provided. The result suggests that the proposed machine has a higher power factor, lower eddy‐current loss, and rare‐earth PM machine comparable torque density.

This study proposes a computationally efficient hybrid approach to calculating permanent magnet (PM) loss in surface‐mounted PM machine (SPMM) over the whole working points. By analysing the impact of current amplitude, current angle, rotor speed, and PM temperature on the corresponding subdomain PM loss results, a simplified model that considers harmonic current and explicitly expresses the relationship between PM loss and working point variables is derived. Unlike the corresponding subdomain method, the proposed model does not involve a Fourier expansion or summation over a large number of terms, which simplifies the calculation and makes the PM loss under any operating condition can be acquired instantly. Thus, the needs for PM loss table and massive experiments or calculations are eliminated. The accuracy of this hybrid approach is validated against the finite element method results of a six‐phase SPMM over a wide range of working conditions. However, the cross‐verification studies demonstrate that the proposed method has good efficiency and high precision, making it a promising tool for online PM loss prediction.

Accurate long-term temperature prediction is critical for the reliable operation of mass-produced electrical machines. However, due to the randomness inherent in the manufacturing process, machines with identical design parameters often exhibit distinct thermal properties. The aging of the insulation system can also lead to variation in thermal performance. Conventional lumped-parameter thermal network (LPTN) models with fixed parameters fail to account for these factors, thus leading to biased prediction results for long-term temperature forecasting of mass-produced machines. To enhance the robustness of LPTN models, this paper proposes a methodology for adaptive online parameter updating. Based on the mathematical formulation of LPTN, a fast Jacobian matrix calculation method for model prediction errors is developed, which avoids the time-consuming numerical computation process. To further alleviate the computational burden, key parameters with significant impacts on prediction errors are screened prior to each optimization iteration. These improvements collectively reduce computational resource requirements and enable real-time online implementation. Finally, experimental verification is conducted on a 10 kW permanent magnet machine. Comparative analyses against the numerical method and extended Kalman filter (EKF) demonstrate that the proposed method can be efficiently realized and is more effective in estimating the model parameters online.