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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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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DOI: https://doi.org/10.1007/s40430-024-04723-2