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

作　　者：Xuhao Liu Zilin Yan Junwei Wu Jake Huang Yifeng Zheng Neal PSullivan Ryan O'Hayre Zheng Zhong Zehua Pan 

机构地区：[1]School of Science,Harbin Institute of Technology,Shenzhen 518055,Guangdong,China [2]School of Materials Engineering,Harbin Institute of Technology,Shenzhen 518055,Guangdong,China [3]Department of Metallurgical and Materials Engineering,Colorado School of Mines,1500 Illinois St.,Golden 80401,Colorado,USA [4]College of Materials Science and Engineering,Nanjing Tech University,No.30 Puzhu Road(S),Nanjing 211816,Jiangsu,China [5]Department of Mechanical Engineering,Colorado School of Mines,1500 Illinois St.,Golden 80401,Colorado,USA

出　　处：《Journal of Energy Chemistry》2023年第3期582-588,I0016,共8页能源化学（英文版）

基　　金：funding from the National Natural Science Foundation of China,China(12172104,52102226);the Shenzhen Science and Technology Innovation Commission,China(JCYJ20200109113439837);the Stable Supporting Fund of Shenzhen,China(GXWD2020123015542700320200728114835006)。

摘　　要：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.

关 键 词：Protonic ceramic fuel cell/electrolysis cell Electrochemical impedance spectroscopy Distribution of relaxation times Artificial neural network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM911.4[自动化与计算机技术—控制科学与工程]