DC–DC Converters Dynamic Modeling With State Observer-Based Parameter Estimation

摘要

Online knowledge of dc–dc converters behavior is always of great interest. Based on a reliable model of the converters, some great improvement can be achieved. First, the control of the converters can be designed more precisely, especially while thinking in nonlinear theories model-based controls, and it can help to improve the energy management and the efficiency. Also, the knowledge of the converters gives some really useful indications about their state of health and, then, represents a good diagnosis tool and fault detection possibility. This paper proposes a modeling of the converters and a new state observer dedicated to an online estimation of the model parameters. The proposed average models include parameter modeling the losses and their estimation. They are validated on two different converters: the classical dc–dc boost converter and the current-fed dual-bridge dc–dc converter (CFDB—also called isolated boost). It is shown that the model of this last converter is strongly nonlinear, which impacts on the estimation. Simulations and experimental validation are given both on the boost and the isolated boost, and comparisons with the Luenberger state observer and extended Kalman filter are given to underline the interest of the proposed parameter estimation in terms of convergence for nonlinear systems and convergence rapidity.