The thermal modeling and design process of highly utilized electric machines is a complex task. The main heat paths within an electric drive are influenced by a variety of different thermal interfaces, that can mainly be described by thermal conduction and convection. The proper selection and estimation of these design parameters is challenging especially in the early design stage. This paper provides a systematic investigation evaluating inaccuracies and design influences of single thermal resistances, such as the convective heat transfer in the rotor-stator annulus, in the cooling jacket or interfaces between different machine components. Within the study, electromagnetic finite element models and thermal lumped parameter models of two traction drives are developed and validated using test bench measurements. The thermal design parameters are varied in a range resulting from intensive literature research and own laboratory experience. The influence of the design parameters in different operational points of the drives are studied in a sensitivity analysis.

Due to the increasing demand for electrification in the automotive sector, the interest in the manufacturing and processing of pure Copper (Cu; purity 99.99%) is also increasing. Laser-based technologies have proven to be challenging due to Cu’s high optical reflectivity. Processing pure Cu with Electron Beam Melting (EBM) is a promising manufacturing route, allowing for high design freedom. The highest priority is to achieve outstanding thermal and electric conductivity in manufactured Cu components. Chemical contamination or manufacturing defects, such as porosity, significantly reduce the thermal and electric conductivity. The literature on post-processing (thermal and abrasive) of additively manufactured Cu is scarce. Therefore, this study discusses the correlation between as built and heat treated microstructure, as well as surface roughness on the EBM electric conductivity. EBSD analysis is performed to analyze the effect of microstructure on electric conductivity. The effect of sandblasting and vibratory finishing on surface roughness and electric conductivity is investigated. Additionally, the samples are mechanically tested in terms of hardness.

The proper orthogonal decomposition (POD) is an efficient model order reduction method, which is frequently coupled with the discrete empirical interpolation method (DEIM) to solve nonlinear electromagnetic problems. A drawback of this method is that instabilities can occur related to the reduction operator of the nonlinear part. In this contribution, different DEIMs and the Gappy POD are employed and analyzed. Consecutively, the methods are employed to efficiently estimate the behavior of a permanent magnet synchronous machine in terms of global quantities, such as torque and iron losses.

The accurate computation of multi‐coupled electric drive systems is a complex process. In order to consider a variety of physical effects, their interactions as well as the interactions of different components, multiple fields of science have to be addressed. Numerical simulations help to compute these complex systems and to design machines in the best possible way for its specific application. The numerical computation scheme proposed in this study consists of six main sectors: the rough machine design, the preparation of the finite element method including the characterisation and modelling of materials, the electromagnetic simulation, the mechanical simulation, the control and power electronics and the thermal design and simulation. In each of the main sectors, different effects affecting the machine and the system behaviour can be considered and calculated. Depending on the application, they can be added or removed modularly in order to adapt the simulation effort and the simulation accuracy to the application. A detailed insight into the computation methods and approaches in each of these main sectors and their coupling is given in this study.

Optimization methods are increasingly used for the design process of electrical machines. The quality of the optimization result and the necessary simulation effort depend on the optimization methods, machine models and optimization parameters used. This paper presents a multi-stage optimization environment for the design optimization of induction machines. It uses the strategies of simulated annealing, evolution strategy and pattern search. Artificial neural networks are used to reduce the solution effort of the optimization. The selection of the electromagnetic machine model is made in each optimization stage using a methodical model selection approach. The selection of the optimization parameters is realized by a methodical parameter selection approach. The optimization environment is applied on the basis of an optimization for the design of an electric traction machine using the example of an induction machine and its suitability for the design of a machine is verified by a comparison with a reference machine.

In electric drivetrains, the traction machines are often coupled to a gear transmission. For the noise and vibration analysis of such systems, linearised system models in the frequency domain are commonly used. In this paper, a system approach in the time domain is introduced, which gives the advantage of analysing the transient behaviour of an electric drivetrain. The focus in this paper is on the dynamic gear model. Finally, the modelling approach is applied to an exemplary drivetrain, and the results are discussed.