Online Impedance Spectrum Measurement of Fuel Cells With Discrete-Interval Binary Sequence Signals

Electrochemical impedance spectroscopy (EIS) is widely used in fault diagnosis of fuel cells (FCs) but faces challenges in online applications due to high cost and lengthy measurement durations. This article introduces a rapid EIS measurement technique centered on adaptive, segmented discrete-interval binary sequence (DIBS) signals that are dynamically tailored to the FCs’ actual electrochemical noise (EN). The noise characteristics are determined through autoregressive (AR) model-based power spectral estimation (PSE), allowing the excitation signal to be optimized for a high signal-to-noise ratio (SNR) across the target frequency range while respecting system linearity constraints. An advanced data processing method is also applied to the raw impedance data to further improve its quality. Comparative simulation analysis of EIS results obtained using different DIBS schemes and the traditional maximum-length binary sequence (MLBS) excitation demonstrates the accuracy and robustness of the proposed method. In addition, a practical experimental platform is established based on the programmable electronic load to verify its real-world utility. Experimental validation confirms that a relatively accurate impedance spectrum from 0.5 to 1 kHz can be obtained in a single test of 16$\boldsymbol{\sim}$18 s, providing a key technological foundation for online monitoring of FCs under dynamic operating conditions.