首页|Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy:Robustness improvement and evaluation
Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy:Robustness improvement and evaluation
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Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability.Nevertheless,ensuring the robustness of diagnosis remains a critical and challenging task in real application.To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance,a robust diagnostic procedure based on electrochemical impedance spectroscopy(EIS)and a new method for evaluation of the diagnosis robust-ness was proposed and investigated in this work.To improve the diagnosis robustness:(1)the degrada-tion mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model(ECM)with better inter-pretability,simplicity and accuracy;(2)the feature extraction was implemented on the identified param-eters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3)a Siamese Network was adopted to get features with higher robustness in a new embedding.The diagnosis was conducted using 6 classic classification algorithms-support vec-tor machine(SVM),K-nearest neighbor(KNN),logistic regression(LR),decision tree(DT),random forest(RF),and Naive Bayes employing a dataset comprising a total of 1935 collected EIS.To evaluate the robustness of trained models:(1)different levels of errors were added to the features for performance evaluation;(2)a robustness coefficient(Roubust_C)was defined for a quantified and explicit evaluation of the diagnosis robustness.The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100％but also higher robustness for diagnosis models.Despite the initial performance being similar,the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method,which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient.This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.
PEM fuel cellData-driven diagnosisRobustness improvement and evaluationElectrochemical impedance spectroscopy
Dan Yu、Xingjun Li、Samuel Simon Araya、Simon Lennart Sahlin、Vincenzo Liso
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Department of Energy,Aalborg University,Aalborg 9220,Denmark
Walker Department of Mechanical Engineering,The University of Texas at Austin,Austin,TX 78712,USA
Materials Research and Technology Development,Luxembourg Institute of Science and Technology(LIST),4422 Belvaux,Luxembourg
Chinese Scholarship CouncilChinese Scholarship Councilenergy department of Aalborg University
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