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.

Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 1 Abstract-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 work 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. Multi-physics 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 eighteen high-dimensional multi-physics 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.

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