Multi-Level Modeling Methodology for Optimal Design of Electric Machines Based on Multi-Disciplinary Design Optimization

Zehua Dai, Li Wang, Lexuan Meng, Shanshui Yang and Ling Mao
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Zehua Dai: Collage of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
Li Wang: Collage of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
Lexuan Meng: AC Systems, Power Grid division, ABB, 8000 Zurich, Sweden
Shanshui Yang: Collage of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
Ling Mao: Collage of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China

Energies, 2019, vol. 12, issue 21, 1-26

Abstract: The transportation sector is undergoing electrification to gain advantages such as lighter weight, improved reliability, and enhanced efficiency. As contributors to the safety of embedded critical functions in electrified systems, better sizing of electric machines in vehicles is required to reduce the cost, volume, and weight. Although the designs of machines are widely investigated, existing studies are mostly complicated and application-specific. To satisfy the multi-level design requirements of power systems, this study aims to develop an efficient modeling method of electric machines with a background of aircraft applications. A variable-speed variable-frequency (VSVF) electrically excited synchronous generator is selected as a case study to illustrate the modular multi-physics modeling process, in which weight and power loss are the major optimization goals. In addition, multi-disciplinary design optimization (MDO) methods are introduced to facilitate the optimal variable selection and simplified model establishment, which can be used for the system-level overall design. Several cases with industrial data are analyzed to demonstrate the effectiveness and superior performance of the modeling method. The results show that the proposed practices provide designers with accurate, fast, and systematic means to develop models for the efficient design of aircraft power systems.

Keywords: electrified aircraft; electric machine; MDO; intelligent design; system-level design; statistical learning; surrogate model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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