A review of recent developments in electrical machine design optimization methods with a permanent magnet synchronous motor benchmark study

The paper systematically covers the significant developments of the last decade, including surrogate modelling of electrical machines, direct and stochastic search algorithms for both single- and multi- objective design optimization problems. The specific challenges and the dedicated algorithms for electric machine design are discussed, followed by benchmark studies comparing Response Surface (RS) and Differential Evolutionary (DE) algorithms on a permanent magnet synchronous motor (PMSM) design with 5 independent variables and a strong non-linear multi-objective Pareto front and on a function with 11 independent variables. The results show that RS and DE are comparable when the optimization employs only a small number of design candidates and DE performs better when more candidates are included.

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