Nonlinear Least Squares Optimization for Parametric Identification of DC–DC Converters

Abstract

Switching mode power converters are being extensively applied in different power conversion systems. Parameter identification comprises a set of techniques focused on extracting the relevant parameters of the converters in order to generate accurate discrete simulation models or to design enhanced condition diagnosis schemes. This article applies a noninvasive optimization approach based on the nonlinear least squares algorithm to determine the model parameters of different commercially available dc-dc power converters (buck, boost, and buck-boost) from experimental data, including the parameters related to passive, parasitic, and control loop elements. The proposed approach is based on a noninvasive on-line acquisition of the input/output voltages and currents under both steady state and transient conditions. The proposed method can also be applied to many other applications requiring precise and efficient parameter identification, including rectifiers, filters, or power supplies among others.