ABSTRACT The identification of key component parameters of a Boost converter across different operating conditions, while keeping the hardware and measurement paths unchanged, is investigated. The objective is to obtain accurate parameter values that support stable control and reliable waveform quality when the operating point varies. To achieve this, a mechanism‐guided and data‐assisted approach is developed. A discrete‐mapping model is constructed to retain the converter's main switching modes and energy relations, and short oscilloscope records from standard measurement channels are aligned within fixed windows to serve as the identification basis. Closed‐form relations are applied to reduce the search space for the capacitor branch, and information from current‐slope behavior and duty‐cycle consistency provides initial values for the inductor branch. A constrained fitting stage then evaluates candidate parameter sets by comparing predicted and measured waveforms and selecting the set with the best match. Validation is carried out in Simulink and on a hardware prototype under variations in inductance, capacitance, capacitor ESR, switching frequency, and load. Across all conditions, the identified parameters remain consistent, and the reconstructed waveforms agree closely with measurements. The results show that the method offers an efficient and practical solution for assessing converter behavior with limited data and low computational cost.
Li et al. (Tue,) studied this question.