Approaches for Improving Lumped Parameter Thermal Networks for Outer Rotor SPM Machines

This work is about the transient modeling of the thermal characteristics of outer rotor SPM machines by considering a lumped parameter thermal network based approach. The machine considered here poses particular challenges for the modeling, e.g., due to the semi-closed stator surrounded by a rotor bell that provides a speed-dependent cooling of the stator coils. Starting from a simpler basic network configuration, model extensions and refinements are presented and discussed. The subsequent parameter identification is done by means of an initial design of experiments based sampling, and a subsequent single-objective and also a multi-objective optimization of error functions for the components' temperatures. Analyzing the therefrom derived Pareto fronts and the consequent tradeoff regarding achievable minimum modeling errors for different system's components gives insights into where and how the modeling can be further improved. All the investigations are based on experimental results obtained through operating a particularly developed test setup

Measurement-Based Identification of Lumped Parameter Thermal Networks for sub-Kw Outer Rotor PM Machines

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

State-of-the-art and future trends in soft magnetic materials characterization with focus on electric machine design-Part 2

© 2019 Walter de Gruyter GmbH, Berlin/Boston 2019. The first part of this two-part article is about a retrospective view of material characterization, starting with the work of J. Epstein around the year 1900 and respective basic explanations. Consequently, the work presented herein is about the current state-of-the-art, recent developments, and future trends in characterization of ferromagnetic materials. Modeling is in fact a type of characterization, in a phenomenological and mathematical sense, and therefore it is treated with due attention in this article. Quantifying the properties of soft magnetic materials retains significant scientific attention. Thanks to new optimization techniques and advances in numerical evaluation, modern electromagnetic devices feature high utilization. Classical models exhibit assumptions that do not allow modern machine or device characterization with high accuracy. In this manuscript, typically applied techniques and recent incremental improvements, as well as newly developed models are introduced and discussed. Moreover, the significant impact of manufacturing on the materials' characteristics and its quantification is illustrated. Within this article, a broad overview of the state-of-the-art as well as recent advances and future trends in soft magnetic material characterization is presented. Thus, it is a valuable reading for beginners and experts, from academia and industry alike

Measurement-Based Optimization of Thermal Networks for Temperature Monitoring of Outer Rotor PM Machines

This paper is about deriving suitable lumped parameter thermal networks for modeling the transient thermal characteristics of electric machines under variable load conditions. The network should allow for an accurate estimation of the temperatures of critical machines' components. In best case, the model can be run 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 further shall be as simple as possible while guaranteeing a decent accuracy of the predicted temperatures. A lumped parameter thermal network is selected and its characteristics are explained in detail. Besides the model selection and the optimization of its critical parameters through an evolutionary optimization strategy, an experimental setup will be described in detail. The model accuracy is evaluated for both static and dynamic test cycles with changing load torque and speed requirements. Finally, the significant improvement of the accuracy of the predicted motor temperatures is presented and the results are compared with measurements