Multi-objective surrogate optimization of mixed-variable IPM motor design problem with ADMM-based approach

Designing electric motors boils down to solving multi-objective optimization problems where we look for design variables satisfying multiple requirements on, e.g., torque and electromagnetic losses. To design high-performance motors without restriction by existing design ideas, it is necessary to ta...

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Vydáno v:Optimization and engineering Ročník 26; číslo 3; s. 1699 - 1724
Hlavní autoři: Asanuma, Tatsuya, Kanno, Yoshihiro
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Nature B.V 01.09.2025
Témata:
Design optimization
Designers
Electric motors
Electromagnetic losses
Finite element method
Genetic algorithms
Heuristic methods
Linear programming
Methods
Multiple objective analysis
Optimization
Permanent magnets
Rounding
Variables
ISSN:1389-4420, 1573-2924
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Shrnutí:Designing electric motors boils down to solving multi-objective optimization problems where we look for design variables satisfying multiple requirements on, e.g., torque and electromagnetic losses. To design high-performance motors without restriction by existing design ideas, it is necessary to take the design space as wide as possible at the initial design stage. When dealing with the extensive design space, it is required to treat the basic structure parameters of motors and the detailed dimensions as design variables. For an interior permanent magnet (IPM) motor, which is one of the typical motor types, we need to handle discrete integer design variables such as the number of slots and the coil pitch in addition to the continuous ones. However, it is difficult to obtain the discrete design variable values without rounding off to the nearest allowed values in the framework of the existing continuous surrogate optimization such as sequential approximation optimization (SAO) with the response surface created from the electromagnetic finite element analysis results. We solve this mixed-variable optimization problem by the heuristic approach based on the alternating direction method of multipliers (ADMM) and show with example design problems that the better results are obtained more efficiently in terms of the number of expensive objective function evaluations compared to taking rounding-off measures.
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ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-024-09954-9