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 new method for multi-objective robust design optimization of electrical machines and provides a detailed comparison with so far introduced techniques. First, two robust design approaches, worst-case design and design for six-sigma, are compared with the conventional deterministic approach for multi-objective optimization. Through a case study on a permanent magnet motor, it is found that the reliabilities of motors produced based on robust designs are 100% under the investigated constraints, while the reliabilities of deterministic designs can be lower than 30%. A major disadvantage of robust optimization is the huge computation cost, especially for high-dimensional problems. To attempt this problem, a new multi-objective sequential optimization method (MSOM) with an orthogonal design technique and hypervolume indicator (as a measure of convergence) is proposed for both deterministic and robust design optimization of electrical machines. Through another case study, it is found that the new MSOM can improve motor performance and greatly reduce the computational cost. For the robust optimization, the number of required finite element simulations can be reduced by more than 40%, compared with that required by the conventional approach. The proposed method can be applied to many-objective (robust) design optimization of electrical machines.

From the perspective of industrial production, the design and optimization of electrical machines are application oriented, including maximizing production quality and minimizing production cost in terms of different manufacturing conditions. To achieve these goals, this study presents an efficient application-oriented robust design optimization method for permanent-magnet (PM) motors. The method consists of two main contributions. The first one is the development of an overall optimization strategy, including qualitative and quantitative analyses to provide possible options for an application. Multiphysics analysis, uncertainty analysis, production cost, and optimization models need to be investigated. The second one proposes a multilevel optimization method for the high-dimensional robust design problem of each option. To illustrate the advantages of the proposed method, PM motors with soft magnetic composite cores are investigated for domestic applications. The design optimization is conducted in terms of three motor options and three batch production volumes for both conventional deterministic and robust approaches, and it consists of 18 high-dimensional multiphysics optimization problems in total. Main optimization results are presented and discussed. Experimental and simulation results are presented to validate the effectiveness of the proposed models and methods.

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.

The knowledge-based parametric design process of the rotor topology of the synchronous reluctance motor (SynRM) requires a tedious parameter definition, selection, and searching process until an optimal design can be obtained. This research proposes a topology optimization approach based on solid isotropic material with penalization method for the SynRM rotor topology design. The aim is to pursue a more intelligent and automatic design process. Specifically, the proposed method aims to generate the SynRM rotor design of high output torque profile. Meanwhile, the mechanical constraints including structural compliance and stress are also considered for promising the structural reliability of the rotor. Considering the high dimensional constraints caused by the multiphysics performances, the augmented Lagrangian method based optimization framework is developed to solve the minimization problem in a compact scheme. To prove the effectiveness of the developed method, simulations, and analysis on the topology optimization of SynRMs are performed in this article. Prototyping and experiment are also conducted for verifying the performance of the optimized structure.

This article presents the topology optimization of ferromagnetic components in electrical machines by using the density method. Explicit expressions of machine performance based on the principle of electromechanical energy conversion are derived and incorporated in the finite element model. Because the gradient-based algorithm is employed for efficient optimization, the performance sensitivities with respect to the design variables are derived subsequently. An optimization framework is then proposed to optimize effectively the electrical machine parameters and performance, such as the flux linkage, back electromotive force, and torque profile including the torque density and torque ripples. Two design examples are reported to confirm the feasibility and effectiveness of the presented topology optimization approach. The accuracy of optimal solutions is verified by using ANSYS MAXWELL, a commercial field analysis software.