Water Management Fault Diagnosis for Open-Cathode PEMFC Systems Based on Electrochemical Impedance Spectroscopy

To extend the lifetime of proton exchange membrane fuel cells (PEMFCs), the two most prevalent operational faults— membrane drying and flooding—must be diagnosed and analyzed. This study presents a comprehensive fault diagnosis framework that integrates machine learning with an equivalent circuit model (ECM) for open-cathode PEMFC systems. First, based on the fuel cell fast impedance test platform built in the laboratory, water management fault experiments are conducted, and the corresponding fault impedance data are obtained. Then, the second-order R-CPE ECM parameters are identified adaptively using the rapid and efficient data processing method to obtain the 7-dimensional original sample set. Afterward, principal component analysis (PCA) is applied to downsize the high-dimensional state feature dataset, and four principal component parameters are selected as fault diagnosis features. In addition, FCM (Fuzzy C-Means) clustering and SMOTE (Synthetic minority over-sampling technique) algorithms are combined for feature enhancement. Finally, the support vector machine (SVM) optimized by the grid search technique is implemented for fault classification. Five-fold cross-validation shows that the proposed PCA-FCM-SMOTE+GS-SVM-linear framework can effectively identify normal, flooding, and drying states with 97.63% accuracy, outperforming other algorithms. Validation under varying temperatures further confirms its robustness, achieving 94.74% on unseen conditions.