A new method for acquiring temporal and spatially resolved electrochemical impedance spectroscopy data on a segmented proton exchange membrane fuel cell is developed. The cell used in this study consists of a segmented anode with 20 galvanically insulated segments. By measuring all segments simultaneously, restricting the frequency range, imposing all frequencies within one measuring task, and decoupling the acquisition and post-processing steps, we can reduce the acquisition time to 1 second. The results yield the spatial and temporal evolution of the TLM’s parameters, e.g., membrane resistance Rmem. Our results show that the temporal evolution of local Rmem strongly differs from the temporal evolution of the global Rmem. Moreover, we can see strong differences in the temporal evolution of local Rmem. The results further indicate that re-humidification after a decrease of stoichiometry appears almost instantly, while after an increase of stoichiometry it takes several minutes until the cell is dried out and equilibrated again. Furthermore, we can show, that the ratio of the 1 kHz resistance R1kHz to Rmem locally changes over time, making it an unsuitable substitute for Rmem. We therefore suggest R1kHz not to be taken as an indicator or substitute for Rmem.

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An approach for local and transient in operando measurements of humidification along the channel of a segmented Proton exchange membrane fuel cell (PEMFC) is presented. Based on the impedances at fixed frequencies and temperature, equivalent relative humidity data is acquired and compared for steady state and transient experiments. Based on our results we can show that the classical 1kHz resistance in combination with a proper parametrization is a good choice for monitoring the ionomer humidification. A fast acquisition rate offers high resolution insights into the spatio-temporal dynamics of a PEMFC. It reveals that the current density and humidity distribution response to changes in stoichiometry and total current is highly inhomogeneous along the channel. The data is further used to establish a qualitative model that explains and predicts local dynamic phenomena occuring along the channel during local drainage or humidification.