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

Wöckinger, Daniel; Bramerdorfer, Gerd; Vaschetto, Silvio; Cavagnino, Andrea; Tenconi, Alberto; Amrhein, Wolfgang; Jeskey, Frank

doi:10.1109/ecce47101.2021.9594930

Cited by 5 publications

(1 citation statement)

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“…Future analyses shall be about investigating and comparing different cost functionals, and focusing on the data selection to evenly represent all relevant domains. Besides, investigating multi-objective problems regarding the different machine components' temperatures and their respective errors can provide further insight to limitations and potential improvements for the structure of the proposed network and the speed-and temperature dependency of the heat transfer [36].…”

Section: Optimization Results and Discussionmentioning

confidence: 99%

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

Wöckinger

Bramerdorfer

Drexler

et al. 2023

IEEE Trans. on Ind. Applicat.

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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.

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Section: Optimization Results and Discussionmentioning

confidence: 99%