Multi-Objective EMI Optimisation using a Metamodel-based SiC/GaN Converter and NSGA II

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Abstract
In Multi-Objective optimization (MOO) many conflicting key performance indicators (KPIs) require a large number of simulations to be carried out to obtain the Pareto fronts. The surrogate model-based approach allows faster model evaluation, easing the burden of MOO. This document presents the MOO of a Kriging-based metamodel of a half bridge circuit combined with an electromagnetic interference (EMI) filter using non-dominated sorting genetic algorithm II (NSGA-II) to comply with EMC limits. Out of a total of 14 design parameters which concern both the functional and the EMI design of the system, we study five dimensions in detail and obtain Pareto-optimal designs with a good compromise between fast switching and EMC compliance.

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