Abstract
This paper presents a novel step-optimization strategy for the multi-objective tradeoffs in surface-mounted permanent magnet synchronous machines (SPMSMs), i.e. genetic-Taguchi global optimization (GTGO). Specifically, the electromagnetic performances are accurately predicted by sub-domain (SD) model, and the objective functions of cost, efficiency and total harmonic distortion (THD) of back electromotive force (emf) are solved by genetic algorithm (GA) based on constraint conditions. Next, the Taguchi method optimizes local variables which are difficult to model by SD model, such as magnetic pole shift, magnet eccentricity and segmentation. The last, the effectiveness of proposed GTGO has been verified by finite-element analysis (FEA).
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