Experimental design for dynamic impedance analysis of alkaline water electrolysis under wind and solar fluctuation conditions

Conventional laboratory instruction in alkaline water electrolysis is mainly based on steady-state measurements, such as polarization curves and gas production assessments, which cannot adequately explain the dynamic response of electrolysis under fluctuating renewable power. Under wind–photovoltaic coupling conditions, hydrogen production is strongly affected by gas–liquid two-phase transport, bubble accumulation on electrode surfaces, and mass-transfer limitations in porous structures. These phenomena are difficult to interpret using traditional confirmatory experiments, so students have limited opportunities to analyze noisy electrochemical signals or to connect data to physical mechanisms. To address the gap between laboratory teaching and practical engineering scenarios, this study develops a research-oriented comprehensive experiment for dynamic impedance analysis of alkaline water electrolysis under wind and solar fluctuation conditions.

The experiment was designed around the idea of using impedance spectroscopy as an “electrochemical microscope.” A modular alkaline water electrolysis platform was constructed with a near-zero-gap cell, nickel-based electrodes, electrolyte circulation, temperature regulation, and gas–liquid separation units. To controllably simulate fluctuating renewable input, a stepwise AC–DC superimposed excitation strategy was adopted. Different DC bias currents were applied sequentially, and a small sinusoidal perturbation was introduced at each operating stage to obtain full-frequency electrochemical impedance spectra. Students were guided to interpret the response in different frequency regions and distinguish ohmic loss, charge-transfer behavior, interfacial capacitance, and diffusion-related impedance. Because dynamic gas evolution caused random noise in the raw signals, Python-based processing was applied for signal cleaning, including high-frequency artifact correction, abnormal-point elimination, and low-frequency smoothing. After preprocessing, equivalent-circuit modeling was performed. A conventional model and a dynamic correction model containing a Warburg diffusion element were compared to reveal the effects of bubble shielding and pore blocking on hydrogen production efficiency.

Teaching practice and model analysis showed that the proposed experiment effectively transformed abstract dynamic hydrogen production behavior into an observable and analyzable process. Signal cleaning made the electrochemical spectra more regular and improved the reliability of subsequent fitting. Compared with the conventional circuit, the corrected model, including the Warburg element, described the impedance characteristics under high-current dynamic conditions more accurately, especially in the low-frequency region associated with mass transfer. At lower current densities, both models characterized the interfacial electrochemical process reasonably well. However, as the current increased, the conventional model gradually failed to reproduce the diffusion tail, whereas the corrected model maintained high fitting quality. Parameter evolution indicated that the solution resistance changed slightly with increasing current, while the charge-transfer resistance decreased gradually. In contrast, bubble-related resistance and diffusion-related impedance increased considerably in the high-current region, indicating that the limiting step shifted from interfacial kinetics to transport restriction caused by intensified bubble accumulation and pore blockage.

The proposed experiment extends alkaline water electrolysis instruction from steady-state verification to dynamic diagnosis and mechanism-oriented analysis. Integrating dynamic excitation, signal cleaning, and physically interpretable modeling into one framework enables students to identify transport bottlenecks in renewable-powered hydrogen production and understand the coupling between electrochemical reactions and two-phase flow. The experiment also promotes interdisciplinary training by combining chemical engineering, electrochemical testing, automatic control, and Python-based data analysis. It provides an effective teaching approach for cultivating students’ data-driven thinking, model-based reasoning, and innovation capability in hydrogen energy against the backdrop of emerging engineering education.