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Methods for temperature estimation and monitoring of permanent magnet: a technology review and future trends

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

The permanent magnet is subject to high-temperature demagnetization under high-torque or high-speed operating conditions. Accurate estimation of the temperature of the permanent magnets allows for better improvement of the cooling system, such as flow distribution and structural optimization. It is also the object of research in tracking and monitoring technology, because timely sensing of the temperature of the permanent magnet is the key to generate thermal protection mechanisms for the cooling system and active thermal protection system. In recent years, techniques for estimating and monitoring the temperature of permanent magnet have evolved, and a large body of valuable literature has been generated in the process. Therefore, this paper discusses this literature by systematically categorizing it into estimation and monitoring techniques in order to promote more researchers to understand the advantages of various methods and rationally choose the research topics they are most interested in. In addition, trends and challenges in various research directions were discussed.

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Abbreviations

AI:

Artificial intelligence

AO:

Adaptive observer

APA:

Affine Projection Algorithms

AMSC:

Amorphous metal stator core

ANN:

Artificial neural network

BA:

Bayesian algorithm

BEMF:

Back electromotive force

CFD:

Computational fluid dynamics

CHT:

Conjugate heat transfer

CNN:

Convolutional neural network

DC:

Direct current

DDM:

Data-driven models

DFIM:

Doubly fed induction motors

DFNN:

Difference-estimating FNN

DTFC:

Direct torque and flux control

EKF:

Extended Kalman filter

EMI:

Electromagnetic interference

EM-PF:

Expectation maximization particle filter

ET:

Extremely randomized trees

EV:

Electric vehicle

FCS-MPCC:

Finite control set model predictive current control

FEA:

Finite element analysis

FNN:

Feedforward neural network

GA:

Genetic algorithm

HF:

High frequency

HFS:

High-frequency signal

HT:

Heat transfer

KF:

Kalman filter

KNN:

K-Nearest neighbor

LPTN:

Lumped parameter thermal network

LPV:

Linear parameter varying

LTI:

Linear time invariant

MILS:

Multi-innovation least squares

MIT:

Massachusetts Institute of Technology

MRAS:

Model-reference adaptive system

MLP:

Multi-layer perceptron

NAC:

Nonlinear adaptive control

NARX:

Nonlinear auto-regressive model with exogenous inputs

OLS:

Ordinary least square

PMSM:

Permanent magnet synchronous motor

PSO:

Particle swarm algorithm

PWM:

Pulse-width modulation

RBF:

Radial basis function

RF:

Random forest

RLS:

Recursive least squares

SMC:

Structural control method

SMILE:

State-space Model Interpolation of Local Estimates

SMO:

Sliding mode observer

SVR:

Support vector machine

TA:

Thermal analysis

TTL:

Transistor–transistor logic

USART:

Universal synchronous/asynchronous receiver/transmitter

2D:

Two-dimensional

3D:

Three-dimensional

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Funding

This work was supported by the Natural Science Foundation of Chongqing (Grant No.:cstc2021jcyj-msxmX0440); the youth project of science and technology research program of Chongqing Education Commission of China (No.:KJQN202301167); the Chongqing Graduate Education Teaching Reform Research Project (No.:YJG233120); and the Special Major Project of Technological Innovation and Application Development of Chongqing (No.:CSTB2022TIAD-STX0002).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacture Technology for Automobile Parts (Chongqing University of Technology), Ministry of Education, Chongqing, 400054, China

    Liange He, Yuhang Feng, Yan Zhang & Bingqi Tong

  2. Ningbo Shenglong Group Co., Ltd., Ningbo, 315104, China

    Liange He

  3. Chongqing Tsingshan Industrial Co., Ltd., Chongqing, 402761, China

    Yan Zhang

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  1. Liange He
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  2. Yuhang Feng
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Correspondence to Yan Zhang.

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He, L., Feng, Y., Zhang, Y. et al. Methods for temperature estimation and monitoring of permanent magnet: a technology review and future trends. J Braz. Soc. Mech. Sci. Eng. 46, 174 (2024). https://doi.org/10.1007/s40430-024-04723-2

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