Modern trends for electric traction drives lead to an increased DC link voltage and steeper voltage slopes from novel SiC inverters. This results in higher electrical stress for the insulation system of the stator motor windings. Current motor simulation models lack the precision to investigate the highly dynamic voltage propagation. Consequently, the insulation system is designed inappropriately. To solve this problem, a physically based equivalent circuit model (ECM) is presented for calculating the dynamic voltage distribution. The model is suited for an early design stage and requires neither measurements nor parameter fittings. The ECM structure is strictly linked to physical components in the hairpin stator. This enables the localization of high electrical stress spots in the winding. 2D finite element (FE) models are proposed to calculate the required values of the frequency dependent network elements for the bars in the machine. The bars in the endwinding region are treated with analytical formulas. The ECM is modeled and solved in the frequency domain. Hence, the computational effort is reduced to solving a linear algebraic problem instead of a system of partial differential equations. The model results are validated with measurements in both frequency and time domain. Applications with transient switching events show where locations of high voltage differences can appear inside the slot and the winding heads.

This article presents a prototype of a planar aligned transverse flux machine with spatially distributed phases. This topology enables a flatter assembly space compared to typical transverse flux machines and, additionally, features a reduction gear. This makes it a suitable solution for low-speed high-torque applications primarily found in medical or industrial robotic surroundings. The exemplary motor is described throughout its design process and evaluated in detail in simulations and measurements. Benefits and challenges of the concept are considered and presented.

Exoskeletons are powerful tools for aiding humans with pathological conditions, in dangerous environments or in manually exhausting tasks. Typically, they are designed for specific maximum scenarios without taking into account the diversity of tasks and the individuality of the user. To address this discrepancy, a framework was developed for personalizing an exoskeleton by scaling the components, especially the electrical machine, based on different simulated human muscle forces. The main idea was to scale a numerical arm model based on body mass and height to predict different movements representing both manual labor and daily activities. The predicted torques necessary to produce these movements were then used to generate a load/performance cycle for the power unit design. Considering these torques, main operation points of this load cycle were defined and a reference power unit was scaled and optimized. Therefore, a scalability model for an electrical machine is introduced. This individual adaptation and scaling of the power unit for different users leads to a better performance and a lighter design.