Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

Numerical multi-physical optimization of operating condition and current collecting setup for large-area solid oxide fuel cells

Due to the depletion of traditional fossil fuels and the aggravation of related environmental problems,hydrogen energy is gaining more attention all over the world.Solid oxide fuel cell(SOFC)is a promising power generation technology operating on hydrogen with a high efficiency.To further boost the power output of a single cell and thus a single stack,increasing the cell area is an effective route.However,it was recently found that further increasing the effective area of an SOFC single cell with a flat-tubular structure and symmetric double-sided cathodes would result in a lower areal performance.In this work,a multi-physical model is built to study the effect of the effective area on the cell performance.The distribution of different physical fields is systematically analyzed.Optimization of the cell performance is also pursued by systematically tuning the cell operating condition and the current collection setup.An improvement of 42%is revealed by modifying the inlet gas flow rates and by enhancing the current collection.In the future,optimization of cell geometry will be performed to improve the homogeneity of different physical fields and thus to improve the stability of the cell.