Multi-objective Optimization of PM AC Machines using Computationally Efficient - FEA and Differential Evolution

Document Type

Conference Proceeding



Format of Original

6 p.

Publication Date



Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

2011 IEEE International Electric Machines & Drives Conference (IEMDC)

Source ISSN



A total of eleven independent stator and rotor variables are simultaneously employed for the optimization of a generic example IPM motor design. The multi-objective criterion maximizes efficiency, while minimizing torque ripple at the rated output condition. A Pareto-based differential evolution (DE) algorithm with 100 generations, each with a population of 100 individuals, is presented. Computationally efficient FEA (CE-FEA), which is based on a reduced number of magnetostatic solutions for a motor model in the abc reference frame, is employed. As a result, a total of 10,000 candidate motor designs, which are included in the comprehensive study, are evaluated in a record short time on a typical PC-based workstation. The paper includes an engineering trade-off discussion based on a typical-reference motor, two optimum designs in terms of average torque and torque ripple, and a best-compromise solution. For the case-study, an order of magnitude reduction of the rated-load torque ripple and open-circuit cogging torque has been achieved. This is while, at the same time, the specific torque output has been increased by as much thirty seven percent.


Published as part of the proceedings of the 2011 IEEE International Electric Machines & Drives Conference (IEMDC), 2011: 1528-1533. DOI.