Computationally-efficient finite-element-based thermal models of electric machines

Zhou, Kan; Pries, Jason; Hofmann, Heath; Kim, Youngki; Lee, Taekyung; Filipi, Zoran

doi:10.1109/vppc.2011.6043205

Cited by 7 publications

(10 citation statements)

References 5 publications

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“…In previous work [10], in order to build a reduced-order model, all the eigenvectors of the 2D FEA model are calculated. However, this is computationally infeasible for a 3D model, which may contain millions of eigenvectors.…”

Section: A Decomposition Of Dynamic and Static Eigenmodesmentioning

confidence: 99%

“…To select the significantly excited eigenmodes, the extent of excitation of the i-th eigenmode for a given input vector q is evaluated by the following equation [10]:…”

Section: B Dynamic Eigenmodes Selectionmentioning

confidence: 99%

“…Also, low order dynamic models determined by lumpedparameter model may be not sufficient to capture complex driving cycle commands and cooling conditions. A 2D finite-element-based model-order-reduction technique was presented in [10]. The computation time is dramatically reduced while at the same time maintaining satisfactory accuracy compared to the full-order model.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, a 3D model is needed to accurately capture the thermal dynamics in the machine. However, the model-order-reduction technique presented in [10] is computationally infeasible for the huge matrices generated by 3D FEA.…”

Section: Introductionmentioning

confidence: 99%

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Computationally-efficient 3D finite-element-based dynamic thermal models of electric machines

Zhou

Pries

Hofmann

2013

2013 International Electric Machines &Amp; Drives Conference

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Knowledge of the internal temperatures of an electric machine under real-time operating conditions would be extremely useful in order to determine its torque capabilities. This knowledge is also useful for full-scale electric or hybrid electric vehicle (EV/HEV) simulation and optimization. In this paper, we present a technique for developing computationally-efficient thermal models for electric machines that can be used for real-time thermal observers and EV/HEV powertrain-level simulation and optimization. The technique is based upon simulating eigenmodes of the thermal dynamics as determined by 3D finite element analysis. The order of the model is then dramatically reduced in two ways. First, the dynamic system is decomposed into two parts by using the orthogonality property of the eigenvectors. The extent of excitation of each eigenmode is calculated, and only eigenmodes that are significantly excited are included in the dynamic model; other eigenmodes are treated as static modes. Second, only the temperatures in few "hot spots" in various regions of the machine are chosen. By using the proposed model order reduction techniques, the computation time of the model is shown to be reduced by over five orders of magnitude, while maintaining sufficient accuracy. Experimental work also shows a good agreement between simulation results and measured data.

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Section: A Decomposition Of Dynamic and Static Eigenmodesmentioning

confidence: 99%

“…To select the significantly excited eigenmodes, the extent of excitation of the i-th eigenmode for a given input vector q is evaluated by the following equation [10]:…”

Section: B Dynamic Eigenmodes Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computationally-efficient 3D finite-element-based dynamic thermal models of electric machines

Zhou

Pries

Hofmann

2013

2013 International Electric Machines &Amp; Drives Conference

Self Cite

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“…Other methods for the efficient simulation of electrical motor models can be found in the following works: Bertin et al [13] and Gao et al [14] apply their reduction methods to thermal networks. As it is done with BIRKA in this work, thermal finite element models of a motor have been reduced by Zhou et al [15] by capturing the most significant eigenmodes and only considering the temperature 'hot spots'. The outline of the paper is as follows: In Section 2, BIRKA is introduced and its operation mode for large systems is explained.…”

Section: Introductionmentioning

confidence: 99%

Parametric model order reduction of thermal models using the bilinear interpolatory rational Krylov algorithm

Bruns

Benner

2014

Mathematical and Computer Modelling of Dynamical Systems

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The Bilinear Interpolatory Rational Krylov Algorithm (BIRKA; P. Benner and T. Breiten, Interpolation-based H 2 -model reduction of bilinear control systems, SIAM ) is a recently developed method for Model Order Reduction (MOR) of bilinear systems. Here, it is used and further developed for a certain class of parametric systems. As BIRKA does not preserve stability, two different approaches generating stable reduced models are presented. In addition, the convergence for a modified version of BIRKA for large systems is analysed and a method for detecting divergence possibly resulting from this modification is proposed. The behaviour of the algorithm is analysed using a finite element model for the thermal analysis of an electrical motor. The reduction of two different motor models, incorporating seven and thirteen different physical parameters, is performed.

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An overview of thermal modelling techniques for permanent magnet machines

Khalesidoost

Faiz

Mazaheri‐Tehrani

2022

IET Science Measure & Tech

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Permanent magnet (PM) machines have gained increasing attention from the industry because of their potential merits over conventional electrical machines. High‐temperature level in electrical machines is a major factor that can adversely impact the performance and health conditions of these machines. These effects need to be minimized to make them competitive with currently operational machines in the industry. It can be done by performing a thorough thermal analysis of the prototype during the design process and proper online thermal monitoring of the machine during the operation stage. Thermal modelling is a powerful tool for achieving such goals in both cases. This paper aims to review the state‐of‐the‐art of thermal modelling techniques and provide a detailed comparison of them in PM machines. The fundamental concepts of heat transfer and sources of heat in PM machines are first discussed. Then, different thermal modelling techniques, including thermal subdomain models (TSMs), lumped parameter thermal networks (LPTNs), finite element models (FEMs), computational fluid dynamics (CFD), reduced order models (ROMs), and hybrid thermal models (HTMs) are introduced and compared. Finally, the utilization of these models in the thermal design, control, and monitoring of PM machines is studied.

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Computationally-efficient finite-element-based thermal models of electric machines

Cited by 7 publications

References 5 publications

Computationally-efficient 3D finite-element-based dynamic thermal models of electric machines

Computationally-efficient 3D finite-element-based dynamic thermal models of electric machines

Parametric model order reduction of thermal models using the bilinear interpolatory rational Krylov algorithm

An overview of thermal modelling techniques for permanent magnet machines

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