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Featured Application: The results of this work can be included in the design stage of modular permanent magnet synchronous machines for fault-tolerant applications. Addressing stator-rotor misalignment, usually called eccentricity, is critical in permanent magnet (PM) machines since significantly high radial forces can be developed on the bearings, which can trigger a major fault and compromise the structural integrity of the machine. In this regard, this paper aims to provide insight into the unaddressed identification and analysis of the impact of eccentric tolerances on relevant performance indices of permanent magnet synchronous machines (PMSMs) with modular stator core. Static and dynamic eccentricity are assessed for different slot/pole combinations through the finite element method (FEM), and the results are compared with those of PMSMs with a conventional stator core. The unbalanced magnetic forces (UMF), cogging torque, back-emf, and mean torque variations are described and related to the eccentricity magnitude and classification. The main findings indicate that severe radial forces and significant additional cogging torque harmonics are generated because of eccentricity. Additionally, it is found that the main differences between modular PMSMs and conventional PMSMs rely on the value of slots per pole per phase. [ABSTRACT FROM AUTHOR]

Surrogate model (SM)-based optimization approaches have gained significant attention in recent years due to their ability to find optimal solutions faster than finite element (FE)-based methods. However, there is limited previous literature available on the detailed process of constructing SM-based approaches for multi-parameter, multi-objective design optimization of electric machines. This paper aims to present a systematic design optimization process for an interior permanent magnet synchronous machine (IPMSM), including a thorough examination of the construction of the SM and the adjustment of its parameters, which are crucial for reducing computation time. The performances of SM candidates such as Kriging, artificial neural networks (ANNs), and support vector regression (SVR) are analyzed, and it is found that Kriging exhibits relatively better performance. The hyperparameters of each SM are fine-tuned using Bayesian optimization to avoid manual and empirical tuning. In addition, the convergence criteria for determining the number of FE computations needed to construct an SM are discussed in detail. Finally, the validity of the proposed design process is verified by comparing the Pareto fronts obtained from the SM-based and conventional FE-based methods. The results show that the proposed procedure can significantly reduce the total computation time by approximately 93% without sacrificing accuracy compared to the conventional FE-based method. [ABSTRACT FROM AUTHOR]

Conventionally, the small mean and variance of peak-to-peak cogging torque of permanent magnet (PM) machines considering manufacturing uncertainties can be achieved by a robust design or by reducing the uncertainties range. However, the consequent compromise of other design objectives or the increase in the manufacturing costs are frequently inevitable. In this paper, the combination sequence of the uncertainties is highlighted and implemented to achieve a stable performance even for a non-robust design without increasing the cost too much. A PM assembling approach is proposed to mitigate the influences of PM uncertainties on cogging torque by means of combining the uncertainties in a particular sequence, where the effects of uncertainties would counteract each other. Both the singular type of PM uncertainties and the combined ones are considered in the assembling approach. Furthermore, the proposed approach is verified by comparing the cogging torque performance with PM randomly assembled models, where several hundreds of models featuring different uncertainties are calculated through the finite element method. The proposed approach is discussed, particularly for the application of mass production and a small amount of prototypes. Two prototypes with different PM assembling sequences are fabricated and further verify the effectiveness of the proposed PM assembling approach. [ABSTRACT FROM AUTHOR]

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of manufacturing tolerances on the cogging torque of a 24-slot 28-pole tooth-coil-winding permanent magnet synchronous machine with a modular stator core (TCW-MPMSM). Dimensional tolerances and asymmetries associated with the modular topology are studied by means of finite element simulations in order to identify key parameters that increase the cogging torque above the expected values of a faultless machine. Among five selected dimensional parameters, it was found that angular displacement, radial displacement, and tooth–tip width deviations of the stator segments have the most significant impact on the cogging torque. Considering these three key parameters, a full-range tolerance analysis is carried out by means of a proposed superposition-based approach, evaluating all possible combinations of typical deviation values. It is concluded that the cogging torque increment, generated by tolerances, is relatively independent of the faultless tooth–tip width of the stator segments and the arc-to-pole ratio. Robustness of the TCW-MPMSM, regarding cogging torque generation, depends on both the tightness of the tolerances handled in the manufacturing process and the rated cogging torque: the lower the cogging torque of the ideal machine, the less robust is the machine and, therefore, manufacturing imperfections will be required to be tightened. [ABSTRACT FROM AUTHOR]