Fault Classification Method for PEMFC Based on Equivalent Circuit and SVM

Abstract:
Through the state detection and data analysis of the hydrogen fuel cell system, the development trend of the battery fault is diagnosed and predicted, and the predictive maintenance scheme is formulated in advance and the maintenance is carried out in time, which is the key strategy to improve the safety, reliability and stability of the hydrogen fuel cell. In this paper, a hydrogen fuel cell fault detection system is established by using the soft computing technology of support vector machine. The research results show that the support vector machine model can effectively detect the fault type of hydrogen fuel cell. Through the analysis of the multi-physics coupling characteristics and fault isomorphism issues in hydrogen fuel cell systems, this paper proposes a fault classification method based on an equivalent circuit model and Support Vector Machine (SVM). First, key electrochemical parameters are extracted by constructing PEMFC equivalent circuit models, forming a 14-dimensional initial feature dataset. The dataset is then reduced to 7 dimensions using the correlation coefficient method, followed by z-score normalization to eliminate dimensional influences. On this basis, a multi-class fault classification model is established using the SVM algorithm (radial basis kernel function), with dynamic parameter optimization and a closed-loop training mechanism to enhance model generalization. Experimental results demonstrate that the proposed method achieves classification accuracy exceeding 97% for normal operation, flooding, membrane drying, and oxygen starvation conditions, with an F1-score of 0.98 for normal operation and an overall test set accuracy of over 98%. Compared to CNN and BP models, SVM maintains high precision while reducing diagnostic time to 0.33 seconds, demonstrating significant potential for engineering applications. This study provides an efficient and robust solution for intelligent fault diagnosis in hydrogen fuel cells.