Abstract: To solve the problem of fault classification of proton exchange membrane fuel cells stack,one new fault diagnosis method based on fuzzy C means clustering and probabilistic neural network is proposed in this paper. Firstly,this paper is based on the modified Fouquet equivalent circuit model of the fuel cells stack and combines the experimental data of the stack EIS. The corresponding relationship between the four working states of the stack,namely normal,flooding,membrane drying and oxygen starvation,and the circuit model parameters is obtained. The appropriate fault characteristic quantity is extracted as the feature input of the clustering algorithm. Then,the paper uses the fuzzy C means clustering algorithm to cluster the fault sample data to form a standard clustering center. The probabilistic neural network algorithm is used to achieve multi-fault classification for the fault samples on this basis,which can effectively eliminate the singular data and improve the accuracy of the fault classification. Finally,the paper analyzes 200 sets of experimental data,and compares it with the support vector machine and the K-nearest neighbor method. The analysis results show that the method proposed in the paper can quickly identify the four working states of the stack,and the classification accuracy rate reaches98.33%,which verifies the effectiveness of the proposed algorithm.