Automated design of integrated inductive components for DC-DC converters

Stratta, Andrea; Gottardo, Davide; Nardo, Mauro Di; Lillo, Liliana De; Empringham, Lee; Espina, Jordi; Johnson, Mark

doi:10.1109/dmc51747.2021.9529936

Cited by 2 publications

(1 citation statement)

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“…The computational effort required by these iterative techniques represents a significant burden when the design optimisation of the inductor is carried out as a multi-objective optimisation problem (given the two conflicting objectives of volume and losses). The multi-objective design of inductors has already been treated in literature [16][17][18][19][20], and it is usually solved by means of population-based metaheuristic approaches. Despite their effectiveness, these approaches typically require the evaluation of many designs before converging.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Constraint Handling in Multi-Objective Inductor Design

et al. 2023

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This paper analyses the multi-objective design of an inductor for a DC-DC buck converter. The core volume and total losses are the two competing objectives, which should be minimised while satisfying the design constraints on the required differential inductance profile and the maximum overheating. The multi-objective optimisation problem is solved by means of a population-based metaheuristic algorithm based on Artificial Immune Systems (AIS). Despite its effectiveness in finding the Pareto front, the algorithm requires the evaluation of many candidate solutions before converging. In the case of the inductor design problem, the evaluation of a configuration is time-consuming. In fact, a non-linear iterative technique (fixed point) is needed to obtain the differential inductance profile of the configuration, as it may operate in conditions of partial saturation. However, many configurations evaluated during an optimisation do not comply with the design constraint, resulting in expensive and unnecessary calculations. Therefore, this paper proposes the adoption of a data-driven surrogate model in a pre-selection phase of the optimisation. The adopted model should classify newly generated configurations as compliant or not with the design constraint. Configurations classified as unfeasible are disregarded, thus avoiding the computational burden of their complete evaluation. Interesting results have been obtained, both in terms of avoided configuration evaluations and the quality of the Pareto front found by the optimisation procedure.

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Section: Introductionmentioning

confidence: 99%