Behavioral Switching Loss Modeling of Inverter Modules

TL;DR

This paper introduces a novel behavioral model for inverter switching power loss estimation, utilizing genetic programming and multi-objective optimization to accurately predict losses across various operating conditions.

Contribution

The paper develops a compact, reliable behavioral loss model for inverter modules using GP and MOO, considering multiple operational parameters.

Findings

Model accurately predicts switching losses over diverse conditions

Genetic programming effectively identifies loss behavior

Model reduces computational complexity for loss estimation

Abstract

This paper presents a new behavioral model for switching power loss evaluation in phase-shifted full-bridge inverter Power Modules (PoMs). The proposed model has been identified by means of a Genetic Programming (GP) algorithm combined with a Multi-Objective Optimization (MOO) technique. A large set of loss data, evaluated by means of analytical loss formulas, has been considered for the identification of a compact behavioral model. The GP-MOO approach considers the inverter switching frequency, input voltage, duty-cycle and load resistance as model input variables, and the MOSFET gate driver voltage and resistance as parameters influencing the coefficients values of the identified loss formula. The behavioral model loss predictions confirm their reliability for a wide range of operating conditions.