Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

This paper presents a comparative study on different types of robust design optimization methods for electrical machines. Three robust design approaches, Taguchi parameter design, worst-case design and design for six-sigma, are compared for low-dimensional and high-dimensional design optimization scenarios, respectively. For the high-dimensional scenario, the computational burden is normally massive due to the robustness evaluation of a huge number of design candidates. To attempt this challenge, as the second aim of this paper, a space reduction optimization (SRO) strategy is proposed for these robust design approaches, yielding three new robust optimization methods. To illustrate and compare the performance of different robust design optimization methods, a permanent magnet motor with soft magnetic composite cores is investigated with the consideration of material diversities and manufacturing tolerances. 3-D finite element model and thermal network model are employed in the optimization process and the accuracy of both models has been verified by experimental results. Based on the theoretical analysis and optimization results, a detailed comparison is provided for all investigated and proposed robust design optimization methods in terms of different aspects. It shows that the proposed SRO strategy can greatly improve the design optimization effectiveness and efficiency of those three conventional robust design methods.

A new axial flux permanent magnet machine (AFPM) with soft magnetic composited (SMC) cores is proposed for electric vehicle (EV) application in this paper. As its windings are wound on the stator ring core, it can be regarded as a toroidally wound internal stator (TORUS) machine. With the adopted SMC material for stator core, this machine has the benefits of 3-D magnetic flux properties. The windings and SMC cores can be designed to form a very compact structure, and thus, the torque density can be improved greatly. To obtain the a good flux concentrating ability, two TORUS machines are designed and analyzed, one is with NdFeB magnet for high-performance EV application and the other is with the cheap ferrite magnet for low-cost application. The 3-D finite-element method is used to analyze the electromagnetic parameter and performance. For performance comparison, a commercial AFPM with yokeless and segmented armature P400 is used.

Compared with traditional induction machine and direct current machine, permanent magnet synchronous machine (PMSM) holds many merits like higher torque ability and efficiency when high magnetic co-energy sintered NdFeB magnet is used. However, for the operation with high frequency, the resulted eddy current loss by the permanent magnet (PM) is very high and this kind of loss can bring the PM with high-temperature rise, making the PM face the risk of irreversible demagnetization. To reduce the PM eddy current loss, complete magnet segmentation is an effective method. However, taking this kind of method will increase manufacturing cost and reduce the mechanical robustness of the PMSM. Thus, a partial magnet segmentation method was proposed in the past. In this paper, a new annular partial segmentation (APS) method is proposed for the reduction of the PM eddy current loss, including single-side APS and double-side APS configurations. Considering that the additional process on the PM will reduce the mechanical robustness of the PM and the electromagnetic performance of machine, both the electromagnetic performance and the mechanical strength of the PM have been analyzed, based on 3-D finite-element method. It can be found that if the proposed new annular partial segmentation (APS) method is adopted, the eddy current loss in the PM can be reduced greatly, while the mechanical robustness of the PM can be guaranteed comparing with the traditional partial magnet segmentation method.

In the past decades, various methods have been investigated for assessing performance variation and robust optimization for electromagnetic device design under uncertainties and/or tolerances. However, in actual production, the manufacturing tolerances are variable to a certain extent, which can be optimized for integrating the performance, manufacturing cost, and production quality. This paper proposes a tolerance design optimization approach by optimizing the design parameters and tolerances simultaneously based on design for six sigma technique. A permanent magnet claw pole motor with soft magnetic composite cores is optimized by using the proposed approach. For this high-dimensional optimization problem involving electromagnetic and thermal performance, Kriging model and 3-D thermal network model are employed under the multilevel framework for increasing the optimization efficiency. Finally, through the analysis, the proposed robust tolerance optimization method shows good performance with improved motor performance as well as the diversity controlling without cost increasing.

This paper investigates the efficient robust design and optimization of a high-temperature superconducting (HTS) linear synchronous motor by using the Taguchi parameter design approach. The manufacturing tolerances of the HTS magnets, primary iron core and the air gap are considered in the robust design to ensure that the optimal design is less sensitive to these uncertainties. To overcome the disadvantages of the conventional Taguchi parameter design approach, a sequential Taguchi robust optimization method is presented for improvement of the motor performance and manufacturing quality. The proposed method is efficient because it holds the advantages of both Taguchi method and sequential optimization strategy. It can significantly increase the average thrust and decrease the thrust ripple of the in-vestigated HTS linear synchronous motor.