In this work, different analytical methods for calculating the mechanical stresses in the rotors of permanent magnet machines are presented. The focus is on interior permanent magnet machines. First, an overview of eight different methods from the literature is given. Specific differences are pointed out, and a brief summary of the analytical approach for each method is provided. For reference purposes, a finite element model is created and simulated for each rotor geometry studied. A total of seven rotors rom representative automotive powertrains are considered in their specific speed range. The analytical methods are used to determine the maximum mechanical stress concentration factors for the seven rotor geometries, in which we are determined to find maximum mechanical stress as a final step of the analytical process. For each geometry and each respective operating speed range, the deviations from the finite element reference are determined. In addition, the error in the selected geometry variations is evaluated. A recommendation for the method with the lowest error considering all cases studied is given specifically for the stress in the airgap bridge and the central bridge.

The power density of electric machines for future battery electric vehicles must be further increased to improve customer benefits. To this end, this paper compares two state-of-the art electrical traction machines and evaluates the potential for increasing the power density using a third, novel high-speed machine design. The analysis is performed using an electromagnetic finite element analysis, a thermal network with lumped parameters, and a coupled electromagnetic–thermal simulation. The simulations of the three machines evaluate the potential for increasing the power density and overload margins, as well as reducing material consumption. With regard to the active thermal control, the new design aims for reduced thermal capacities and increased loss density to optimize the thermal controllablity and overall performance. The thermal active control is analyzed in thermal transient simulations and electromagnetic simulations with different magnet temperatures. The results show that higher magnet temperatures benefit efficiency and reduce losses for low torque at high speeds. However, a colder magnet is needed for maximum torque at base speed. A maximum loss reduction of 24% is achieved with a 100 °C magnet-temperature difference at maximum speed and low torque.

Accurately estimating electrical machine temperatures, particularly the magnet temperature, remains a challenging task. Data-driven approaches can be a new method if they offer highly accurate temperature predictions with compact models suitable for real-time embedded systems. This paper presents an investigation on thermal neural networks, a data-driven modeling approach. The main goal of this study is to investigate the benefits and drawbacks of this approach. Firstly, the model architecture and the input dataset from simulations in the ANSYS MotorCAD design software are presented, followed by the training routine. The initial analysis evaluates the best number of nodes for the model, already indicating its strong performance, with a maximum absolute error of the permanent magnet temperature of 1.37 K on an unknown validation dataset. The result of this first analysis is the base model with four internal nodes. This study then explores methods to enhance the physical interpretability of this thermal neural network. Firstly, a sparsing technique reduces the model’s parameters by removing connections that lack physical meaning. Secondly, in an experiment the ambient node is eliminated and a virtual node with constant temperature is introduced to isolate the influence of the ambient node. This model with virtual node has a maximum absolute error of the permanent magnet temperature of 2.75 K and offers faster training with fewer parameters than the base model. The paper finally outlines key steps toward creating a more diverse training dataset, ensuring real-world applicability and usability for temperature-based control systems in permanent magnet synchronous machines.