Advanced Design and Optimization Techniques for Electrical Machines
- Publication Type:
- Thesis
- Issue Date:
- 2020
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To investigate the design optimization techniques of electrical machines, a literature survey is conducted about the problem modeling, and techniques utilized for the effective optimization conduction with various case studies. As a comprehensive design optimization example, an in-wheel motor development for distributed direct vehicle driving is investigated in detail. The works on the application analysis, new material application (grain-oriented silicon steel), topology development, manufacturing process, experiment verification, and parametric deterministic optimization are presented.
Based on the state-of-art design optimization methods and case studies, challenges and proposals are also presented in the survey. In the design stage, the bring-up of new topology depends on the expertise of the designers. This means the expert system still plays an important role in the application-oriented design optimization process. Moreover, parametric optimization is carried out based on the specific design which means the freedom of optimization is limited. To overcome the restriction of parametric optimization for high freedom optimization topology optimization method is investigated. At the current stage, the topology optimization of the soft magnetic components of electrical machines considering various electromagnetic performances is studied. The optimization results of the design examples verified the effectiveness of the proposed method in achieving the optimal shape of the design.
Another problem is about the uncertainties in manufacturing such as tolerances which bring in reliability problems for the conventional deterministic optimal solution. Under this circumstance, the robustness optimization is important for searching optimal solution with both high objective performance and reliability. For the robust optimization of electrical machines, the additional uncertainty quantification and robustness assessment further aggravating the complexity and computation cost of the problem. Considering the problem with different types of uncertainties, high effective optimizers are proposed based on effective uncertainty quantification methods with a general framework. The numerical study results proved the effectiveness of the proposed method.
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