Examination of Temperature-Dependent Iron Loss Models Using a Stator Core

Chen, Junquan; Wang, Dong; Jiang, Yapeng; Teng, Xuan; Cheng, Siwei; Hu, Jinghua

doi:10.1109/tmag.2018.2829632

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…However, space-resolved loss models (FEA) also suffer from parameter uncertainty and might be inaccurate. Moreover, all loss phenomena are temperature-dependent themselves which necessitates to calculate and store enormous loss data beforehand covering the entire speed-torque plane for different motor temperature distributions [69], [70]. Thus, it can be concluded that white-box LPTNs have their strengths not in real-time temperature monitoring during online motor operation, but as a complementary motor design tool that allows faster computational cycles compared to FEA and CFD.…”

Section: A White-box Lptnsmentioning

confidence: 99%

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Wallscheid

2021

IEEE Open J. Ind. Applicat.

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Temperature sensing in electric motors is an important task to ensure component protection against excessive heat while maximizing the power and torque capabilities. In order to optimize the dynamic performance limits of a motor during online operation, important motor temperatures must be known in real time. Since temperature measurements are associated with costs and integration efforts, model-based estimation methods became highly relevant. In recent years, many promising contributions have been made to this field leading to a vast literature basis. This paper organizes the literary landscape and gives a bird's-eye overview of the three most important estimation classes: Indirect methods, which track temperature-sensitive electrical motor parameters, and direct methods, namely lumped-parameter thermal networks as well as supervised machine learning. Practical limitations as well as special challenges of the respective methods are emphasized throughout the text. The aim of the paper is therefore to facilitate the entry for interested researchers and engineers in this field as well as to stimulate new research impulses. Hence, open problems as well as future research questions are addressed at the end of this review.

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Section: A White-box Lptnsmentioning

confidence: 99%

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Wallscheid

2021

IEEE Open J. Ind. Applicat.

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“…However, their increased complexity and computational demands should be considered alongside the specific needs of the analysis. The choice between constant-and variable-coefficient models should be guided by a deep understanding of the system, available data, and the desired level of precision, as both have valuable roles in scientific and engineering simulations [27][28][29][30][31]. Despite the fact that variable-coefficient models express the loss coefficient as a polynomial function of magnetic flux density and frequency, the numerical fitting process can be ill-conditioned, leading to inaccuracies when the ranges of frequency and magnetic flux density are extensive.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Iron Loss in Permanent Magnet Synchronous Motors Based on Particle Swarm Optimization and a Recurrent Neural Network

Xu,

Guo,

Lei

et al. 2023

Magnetism

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The popularity of permanent magnet synchronous motors (PMSMs) has increased in recent years due to their high efficiency, compact size, and low maintenance needs. Calculating iron loss in PMSMs is crucial for designing and optimizing PMSMs to achieve high efficiency and a long lifespan, as this can significantly affect motor performance. However, multiple factors influence the accuracy of iron loss calculations in PMSMs, including the intricate magnetic behavior of the motor under different operating conditions, as well as the influence of the motor’s dynamic behavior during the operation process. This paper proposes a method based on particle swarm optimization (PSO) and a recurrent neural network (RNN) to estimate the iron loss in PMSMs, independent of the empirical iron loss formula. This method establishes an iron loss calculation model considering high-order harmonics, rotating magnetization, and temperature factors. Accounting for the multifactor influence, the model studies the law of loss change under different magnetic flux densities, frequencies, and temperature conditions. To avoid the deviation problem caused by conventional polynomial fitting, a multilayer RNN and PSO are used to train and optimize the neural network. Iron loss in complex cases beyond the measurement range can be accurately estimated. The proposed method helps achieve a PMSM iron loss calculation model with broad applicability and high accuracy.

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“…The influence of temperature on the electromagnetic properties of 0.5 mm medium grade NGO silicon steel was studied, and the action mechanism model was proposed [ 13 ]. Temperature coefficient K and effective resistivity of silicon steel lamination was subsequently introduced to explore the change trend of iron loss with temperature, and came to a conclusion that the iron loss decreases with temperature rise [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Transient Magnetic Properties of Non-Grain Oriented Silicon Steel under Multi-Physics Field

Wang

Tian

et al. 2022

Materials

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High-speed, high-efficiency and high-power density are the main development trends of high-performance motors in the future. At present, the design accuracy of traditional electric machines is already high enough; however, for the future demand of high performance and utilization in special environments (such as aviation and aerospace fields), more thorough research of materials’ performance under multi-physics field (MPF) conditions is still needed. In this paper, a test system that combined temperature, stress and electromagnetic fields along with other fields, at the same time, is built. It can accurately simulate the actual complex working conditions of the motor and explore the dynamic characteristics of non-grain oriented (NGO) silicon steel. The rationality of this method is verified by checking the test result of the prototype, and the calculation accuracy of the motor model is improved.

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Examination of Temperature-Dependent Iron Loss Models Using a Stator Core

Cited by 7 publications

References 16 publications

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Estimation of Iron Loss in Permanent Magnet Synchronous Motors Based on Particle Swarm Optimization and a Recurrent Neural Network

Transient Magnetic Properties of Non-Grain Oriented Silicon Steel under Multi-Physics Field

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