A Novel Sequential-Stage Optimization Strategy for an Interior Permanent Magnet Synchronous Generator Design

For an optimal design of an interior permanent magnet synchronous generator (IPMSG) for range the extended electric vehicle (REEV), many design variables and objective functions should be considered. Conventional optimization methods like the Taguchi method and multiobjective optimization algorithm...

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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 65; H. 2; S. 1781 - 1790
Hauptverfasser: Lim, Dong-Kuk, Jung, Sang-Yong, Yi, Kyung-Pyo, Jung, Hyun-Kyo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and analysis
Computer programming
Design optimization
Generators
Genetic algorithms
Interior permanent magnet synchronous generator (IPMSG)
Linear programming
Mathematical analysis
multiobjective optimization (MOO)
Multiple objective analysis
Optimization
Permanent magnets
robust optimization
Robustness (mathematics)
Signal to noise ratio
Strategy
Taguchi methods
Torque
ISSN:0278-0046, 1557-9948
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Abstract For an optimal design of an interior permanent magnet synchronous generator (IPMSG) for range the extended electric vehicle (REEV), many design variables and objective functions should be considered. Conventional optimization methods like the Taguchi method and multiobjective optimization algorithm have completeness or calculation time problems when solving the many design variables and objectives problems. To address these problems, a sequential-stage optimization strategy (SSOS) is proposed. In the first stage of the SSOS, the initial design result considering the various objective functions is derived by using the Taguchi method. In addition, the sensitive design variables are sorted through the calculation of synthetic signal to noise. In the second stage, the optimal solution for the sensitive design variables is derived using the surrogate assisted genetic algorithm (SAGA). The SAGA not only obtains an accurate and well-distributed Pareto front set but considerably reduces the number of function calls as well. In the last stage, the uncertainty consideration based on the worst-case scenario is applied to derive the robust optimal solution. By applying the proposed optimization strategy to the optimal design of IPMSG for REEV, an optimal solution is derived with fewer function calls, and the feasibility of the proposed optimization is verified by experimental results of manufactured generator.
AbstractList For an optimal design of an interior permanent magnet synchronous generator (IPMSG) for range the extended electric vehicle (REEV), many design variables and objective functions should be considered. Conventional optimization methods like the Taguchi method and multiobjective optimization algorithm have completeness or calculation time problems when solving the many design variables and objectives problems. To address these problems, a sequential-stage optimization strategy (SSOS) is proposed. In the first stage of the SSOS, the initial design result considering the various objective functions is derived by using the Taguchi method. In addition, the sensitive design variables are sorted through the calculation of synthetic signal to noise. In the second stage, the optimal solution for the sensitive design variables is derived using the surrogate assisted genetic algorithm (SAGA). The SAGA not only obtains an accurate and well-distributed Pareto front set but considerably reduces the number of function calls as well. In the last stage, the uncertainty consideration based on the worst-case scenario is applied to derive the robust optimal solution. By applying the proposed optimization strategy to the optimal design of IPMSG for REEV, an optimal solution is derived with fewer function calls, and the feasibility of the proposed optimization is verified by experimental results of manufactured generator.
Author Hyun-Kyo Jung
Kyung-Pyo Yi
Dong-Kuk Lim
Sang-Yong Jung
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SubjectTerms Algorithm design and analysis
Computer programming
Design optimization
Generators
Genetic algorithms
Interior permanent magnet synchronous generator (IPMSG)
Linear programming
Mathematical analysis
multiobjective optimization (MOO)
Multiple objective analysis
Optimization
Permanent magnets
robust optimization
Robustness (mathematics)
Signal to noise ratio
Strategy
Taguchi methods
Torque
Title A Novel Sequential-Stage Optimization Strategy for an Interior Permanent Magnet Synchronous Generator Design
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