Parameter Identification of DC-DC Converters under Steady-State and Transient Conditions Based on White-Box Models

Abstract

This paper proposes a white-box approach for identifying the parameters of DC-DC buck and boost switch mode power converters. It is based on discretizing the differential equations that describe the dynamic behavior of the converters. From the discretized equations and experimental data, the parameters of the converters are identified, thus obtaining both the values of the passive components and the transfer function coefficients of the controller. To this end, steady state and transient experimental signals are analyzed, including the input and output voltages and the inductor and output currents. To determine the accuracy of the proposed method, once the parameters are identified, a simulation with the identified parameters of the converter is run and compared with experimental signals. Such results show the accuracy and feasibility of the approach proposed in this work, which can be extended to other converters and electrical and electronic devices.

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

“…To evaluate the accuracy of the proposed method, the results are compared to the ones obtained using the method detailed in [1], which discretizes the differential equations governing the dynamics of the converters, so that the open loop parameters (passive elements) are found. The closed loop (control loop parameters) parameters are estimated by using the duty cycle and the system identification tool of Matlab®.…”

“…Different strategies are found in the technical bibliography to solve this problem, including off-line and online methods, or methods based on the time-or frequency-domain. In [1] the parameters of a DC-DC converter are identified by solving the differential equations governing the dynamics of the converter, although this approach requires measuring the current in the inductor. In [22] the parameters of a VSC converter are estimated using artificial neural networks, thus requiring a large dataset of training and test signals under different load conditions.…”

“…Fig. 8 compares experimental input/output data under steady state conditions (left) and under a sudden load change (right) against the results obtained from the discrete simulation model with the identified parameters (NLS-TTRLS) and the results derived from the analytical equations of the converter [1]. Figs.…”

“…WITCHING mode power converters (SMPCs) offer interesting features, including high conversion efficiency and compactness [1], [2], which are highly appreciated when designing power conversion systems. They are mostly applied in different applications, including domestic appliances [3], portable electronics, computers and motor drives [4], or in power conversion systems applied to renewable generation [5], among others.…”

“…However, other alternatives are possible, such as grey-box and black-box models. Whereas black-box models are based on mathematical models that reproduce or emulate the behavior of the system without describing its behavior from physical equations, greybox models are not entirely described by physical equations [1].…”