Alongside technical performance features, low system costs are the highest priority in the field of low-power electric drives. This makes adoption of magnetic bearing technologies difficult in applications with higher production volumes. Since published literature on cost-saving measures in this field is scarce, we present in this paper some ideas and stimuli using three selected examples: 1) a highly integrated hybrid bearing that provides active stabilization in 3 degrees of freedom (DOF); 2) a brushless permanent magnet motor with additional axial bearing capability; and 3) a bearingless flux-switching motor. We describe their particular designs and operational features, and compare the additional mechanical and electrical costs of these and similar systems with those of standard motor technologies. This paper concludes with a stability analysis of example 2), since it features passive permanent magnetic bearings, which are inherently only weakly attenuated.

Manufacturing processes such as cutting, welding or assembling originate mechanical and thermal effects that locally introduce residual stresses. Consequently, the magnetic material shows a decrease of magnetic permeability and an increase of hysteresis losses. The adoption of catalogue data for electrical machines simulation thus follows low accuracy of the results. This paper deals with effective modeling the degradation of relative permeability and iron losses in soft magnetic materials due to manufacturing processes. The degradation profile is assessed by means of experimental activity conducted on a set of ring samples where the damaged material has been removed through electrical discharge machining. The proposed modeling approach is particularly relevant when it is implemented in numerical simulation tools.

This work is on deriving precise lumped parameter thermal networks for modeling the transient thermal characteristics of electric machines under variable load conditions. The goal is to facilitate an accurate estimation of the temperatures of critical machines' components and to allow for running the derived model in real time to adapt the motor control based on the load history and maximum permissible temperatures. Consequently, the machine's capabilities can be exhausted at best considering a highly-utilized drive. The model shall be as simple as possible without sacrificing the exactness of the predicted temperatures. Accordingly, a specific lumped parameter thermal network topology was selected and its characteristics are explained in detail. The measurement data based optimization of its critical parameters through an evolutionary optimization strategy, and the therefore utilized experimental setup will be described in detail here. Measurement cycles were recorded for modeling and verification purposes including both static and dynamic test cycles with changing load torque and speed requirements. Applying the proposed hybrid approach for determining the model's parameters through involving physics-based equations as well as numerical optimization followed a significant improvement of the preciseness of the predicted motor temperatures compared to solely determining the networks's coefficients based on expert knowledge. Thereby, the validation included both the original measurement data as well as extra measurement runs. The proposed and applied strategy provides an excellent basis for future thermal modeling of electric machines.

This article presents the nonlinear modeling of the torque of brushless PMSMs by using symbolic regression. It is still popular to characterize the operational behavior of electrical machines by employing linear models. However, nowadays most PMSMs are highly utilized and thus a linear motor model does not give an adequate accuracy for subsequently derived analyses, e.g., for the calculation of the maximum torque per ampere (MTPA) trajectory. This article focuses on modeling PMSMs by nonlinear white-box models derived by symbolic regression methods. An optimized algebraic equation for modeling the machine behavior is derived using genetic programming. By using a Fourier series representation of the motor torque a simple to handle model with high accuracy can be derived. A case study is provided for a given motor design and the motor model obtained is used for deriving the MTPA-trajectory for sinusoidal phase currents. The model is further applied for determining optimized phase current waveforms ensuring zero torque ripple.

This article focuses on the numerical implementation of a continuous model for local degradation in soft magnetic materials. Cutting and punching processes required for producing electric machines impact the magnetic properties of soft magnetic materials. A simplified but effective model for considering local degradation of soft magnetic materials is defined and experimentally validated. The proposed model is then implemented in the source code of numerical simulation tool to assign directly at the mesh level the material characteristics as a function of the distance from the cut edge. The procedure as well as the flow chart for the implementation is described in detail. The proposed method allows avoiding the drawbacks of multilayer approaches and consequently increased model complexity and a required finer mesh density. A comparison between simulation results and measurements of lamination rings is presented and discussed.

This paper deals with the analysis and optimization of radial, coaxial magnetic gears. Multi-objective optimizations of the modulator shape of two magnetic gear configurations, one with and one without back iron on the outer rotor, are presented and discussed. The focus of this publication is to show that a magnetic gear with the same total volume and output torque can provide a much higher efficiency when the outer rotor is built with no back iron. To guarantee comparable results, equal start geometries, objectives, and appropriate optimization parameters are considered. After the optimization process, the geometries of the two gear configurations show different optimal modulator shapes and, therefore, different flux distributions. The gear configuration without back iron achieves a comparable torque capability in comparison with the gear configuration with iron yoke. However, 2-D finite elements calculations estimate for the latter design a loss reduction of 70%.