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Switched reluctance machines (SRMs) provide a potential candidate and a feasible solution with increased interest for industrial applications due to their simple and rigid structure without permanent magnets, low manufacturing cost, excellent power-speed characteristics, and high reliability. However, the nonlinear inductance/flux linkage characteristics caused by the double-salient structure of SRM have created the challenges like high torque ripple and vibration. To solve this problem, a significant number of research works focus on the design and optimization of SRMs. Accordingly, this paper presents an in-depth literature review on the status and potential trends of design optimization techniques for SRMs, including design theory, electromagnetic and thermal modeling methods, novel topologies, optimization classifications, and techniques for optimization efficiency and effects. Existing approaches regarding the above aspects of SRMs are extensively discussed and comprehensively summarized. In addition, some essential trends in design optimization development are presented and highlighted as future perspectives. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the performance and reliability enhancements of SRMs for future applications.

This article 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.

This article 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.

In this paper, a new system-level sequential Taguchi method (SLSTM) is proposed to achieve the optimal solution with high robustness for switched reluctance motor (SRM) drive systems. An SRM drive system consisting of a segmented-rotor SRM and the angle position controller is investigated as a case study. In the implementation, the optimization function contains torque, loss, and torque ripple. The control factors of the system are selected according to the sensitivity analysis results. Manufacturing tolerances are considered to guarantee that the optimal solution features low sensitiveness to uncertainties. The process of defining the design levels of all the control factors and noise factor is illustrated and the orthogonal array is established. The optimization of the SLSTM is carried out sequentially until the certain convergence condition is satisfied. Finally, the component-level sequential Taguchi method (CLSTM) is carried out for comparison. It appears that the proposed SLSTM is efficient in searching for the robust optimal solution for the SRM drive system. Besides, it can achieve better output performance, such as higher average torque and lower torque ripple, and a higher level of robustness compared with the initial design and CLSTM.

In this article, a novel system-level robust design optimization method is presented to improve the performance of switched reluctance motor (SRM) drive systems under multiple operating conditions. Based on typical driving cycles of electric vehicles (EVs), five typical driving modes of the SRM are determined. The optimization objectives in each driving mode are established. The significant parameters of the motor and controller of each driving mode are selected as the optimization variables by using the sensitivity analysis. In order to simplify the optimization process, correlation analysis is performed to determine the coherence of the objective functions of all driving modes. Then, a sequential Taguchi method is applied to find an optimal design which is less sensitive to the noise factors. To verify the effectiveness of the proposed method, an SRM drive system applied in EVs with a 12/10 SRM and angle position control method is investigated. It is found that the proposed method can significantly reduce the torque ripple and improve the comprehensive performance. Finally, a 12/10 SRM is prototyped and tested to validate the simulation results.

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.

© 1986-2012 IEEE. According to the special section entitled 'Robust design and analysis of electric machines and drives', to be published in IEEE Transactions on Energy Conversion, the authors present an introduction to tolerance analysis, robust optimization, and measures to improve the reliability of electric machines, power electronics and drives, and their robust control in general. A comprehensive review of modeling uncertainties and evaluating robustness and reliability based measures is presented. In addition, techniques facilitating solving dedicated optimization scenarios are introduced. The most recent research activities will be illustrated. The article thus enables to easily catch up with the state-of-the-art in these fields and to take notice of ongoing and future work.