This paper reviews many design issues and analysis techniques for the brushless permanent-magnet machine. It reviews the basic requirements for the use of both ac and dc machines and issues concerning the selection of pole number, winding layout, rotor topology, drive strategy, field weakening, and cooling. These are key issues in the design of a motor. Leading-edge design techniques are illustrated. This paper is aimed as a tutor for motor designers who may be unfamiliar with this particular type of machine.

This paper proposes a generalized equivalent magnetic circuit model for the design of permanent-magnet (PM) electric machines. Conventional approaches have been applied to PM machine design but may be insufficiently accurate or generalized without taking pole-slot counts into consideration. This would result in reduction of dimensioning accuracy at the initial design stage. Also, magnetic saturation is often ignored or compensated by correction factors in simplified models since it is difficult to determine the flux in individual stator teeth. In this paper, the flux produced by stator winding currents and PMs can be calculated accurately and rapidly using the developed model, taking saturation into account. A new modeling technique for PM poles is proposed so that the magnetic circuit is applicable to any pole-slot combinations. This aids machine dimensioning without the need for computationally expensive finite-element analysis (FEA). A 540-kW PM machine is first designed using the proposed method and then verified with FEA. Another 350-W machine is subsequently designed, manufactured, and validated by both FEA and experiments. The comparisons demonstrate the effectiveness of the proposed model.

This work presents an approach to detect the inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs) using machine learning. PMSMs possess many attractive advantages such as high efficiency and high power density. However, with their widespread applications, preventive fault diagnosis to secure safe operations and reduce maintenance costs becomes increasingly critical. To achieve this objective, in this article, machine learning is employed for fault diagnosis, wherewith minor ITSC faults can be detected in a preventive manner. Therefore, the faulty motors can be replaced early to ensure system healthy operations. Moreover, machine learning is also utilized here to classify fault levels (e.g., minor, moderate, serious faults) for reference to appropriate maintenance actions. The proposed method adopts support vector machines (SVMs) and convolutional neural networks (CNNs) for training the diagnosis model with experimental data collected from tests in the laboratory. In the SVM, appropriate features required for training are selected through analysis with the PMSM mathematical model considering ITSC faults. This allows for more efficient training with much fewer data. In contrast, CNNs are purely a data-driven approach that requires much more data for training. The accuracy of both methods is validated with another set of measured data. It is found that both methods can achieve an accuracy of 99%; however, the SVM requires much less amount of data to achieve the same accuracy. This demonstrates the advantages of the model-aided machine learning method, which would be more suitable for applications where faulty motor data are not easily acquired.

This paper analyzes and compares the performances of permanent-magnet-assisted synchronous reluctance machines (PMa-SynRMs) and interior permanent magnet synchronous machine (IPMSM) on their constant power speed range (CPSR) capability for electric vehicle (EV) applications. The recent trend of less rare-earth magnet makes the IPMSM unfavorable despite its excellent CPSR. As an alternative, the PMa-SynRM possesses a higher saliency with lower PM quantity. The two different rotor designs would affect the CPSR and these need to be investigated and compared. As a key performance index for EV traction motors, the CPSR calculated by analytical methods often suffers low accuracy due to omission of saturation. Finite-element analysis (FEA) can be used to obtain the CPSR but significant computation time is required. This would be disadvantageous for accurate performance prediction in design stage. To resolve these problems, six 10 kW, 30 Nm/L designs are studied and compared, covering IPMSMs and PMa-SynRMs. An approach associating analytical methods with FEA is developed for accurate and rapid calculation of CPSR for these machines. A discussion on PMa-SynRM rotor designs and suggestion for their PM quantity are provided considering performance, cost, and demagnetization. Two prototypes were tested, one being IPMSM and the other PMa-SynRM to validate the analysis.

This paper evaluates the electromagnetic and thermal performance of several traction motors for electric vehicles (EVs). Two different driving cycles are employed for the evaluation of the motors, one for urban and the other for highway driving. The electromagnetic performance to be assessed includes maximum motor torque output for vehicle acceleration and the flux weakening capability for wide operating range under current and voltage limits. Thermal analysis is performed to evaluate the health status of the magnets and windings for the prescribed driving cycles. Two types of traction motors are investigated: two interior permanent magnet motors and one permanent magnet-assisted synchronous reluctance motor. The analysis results demonstrate the benefits and disadvantages of these motors for EV traction and provide suggestions for traction motor design. Finally, experiments are conducted to validate the analysis.

Achieving a model which accurately diagnoses faults in electric machines is a vital step in data-driven fault detection approaches. To this aim, this article proposes a long short-term memory regulated deep residual network for data-driven fault diagnosis purposes in electric machines. The advantages of the proposed network are that it is more general in terms of fault type and measurement, results in a more accurate model for fault classification, and has faster convergence compared with other networks, such as conventional deep residual networks. In order to prove these advantages of the proposed network, it is evaluated by two different types of datasets. One is the inter-turn short circuit fault in a permanent magnet synchronous motor with the data of measured three-phase current. The second one is the Case Western Reverse University bearing fault dataset with the vibration measurements. The performance of the network is also compared with other networks. Results reveal that the model can accurately detect both types of faults by two different measurements with a test accuracy of 100%. Furthermore, it converges faster than other networks in the training procedure.