No abstract

No abstract

A simple method for analysing a fuel cell system using the voltage response to a step change in load resistance is presented in this work. With the modified Randles model, where two resistors and two capacitors are implemented for the Warburg impedance, the response curve is fitted with three exponential curves. Six independent equations corresponding to six parameters of the model can be obtained from the fitted values, and they are solved under two specific assumptions for the initial state. The parameters are the charge double layer capacitance, the charge transfer resistance, a resistor–capacitor pair related to gas diffusion and another pair related to water diffusion. A down curve of the voltage for the step down change in load and an up curve for the step up change are fitted and parameters for both cases are evaluated separately. Analysis is performed by comparing the parameters at different current levels. The impedance is also simulated using the estimated parameters in order to show which parameter is the rate‐determining factor of the fuel cell reaction with increasing current levels.

Different signal processing technique performances are compared to each other with regard to separating the mean and fluctuating velocity components of a simulated one-dimensional unsteady velocity signal comparable to signals observed in internal combustion engines. A simulation signal with known mean and fluctuating components was generated using experimental data and generic turbulence spectral information. The simulation signal was generated based on observations on the measured velocity data obtained using LDV in a motored Briggs-and-Stratton engine at about 600 RPM. Experimental data was used as a guide to shape the simulated signal mean velocity variation; fluctuating velocity variations with specified spectrum and standard deviation was used to mimic the turbulence. Cyclic variations were added both to the mean and the fluctuating velocity signals to simulate prescribed cyclic variations. The simulated signal was introduced as input to the following algorithms: ensemble averaging; high-pass filtering; Proper-Orthogonal Decomposition (POD); Wavelet Decomposition (WD) and Wavelet Decomposition/Principal Component Analysis (WD/PCA). The results were analyzed to determine the best method in correctly separating the mean and the fluctuating velocity information, indicating that the WD/PCA performs better compared to other techniques.

No abstract