**5. Conclusions**

In this work, the PFCM-ABC-SVM method is proposed and verified by the PEM fuel cell simulator model. The Gaussian noise with variance of 0.1, 0.2, 0.5, and 1.0 are added to the PEM fuel cell simulator model, respectively, for fault diagnosis. The PFCM algorithm is used to filter samples with membership and typicality less than 90% and optimize the original dataset. The ABC algorithm is used to optimize the penalty factor C and kernel function parameter g, and the optimized SVM model is used to diagnose the faults of the PEM fuel cell system. The results show that under the dynamic conditions with the variance of the Gaussian noise decreasing from 1 to 0.1, the accuracy of the training set sample increases from 97.46% to 98.81%, and the accuracy of the testing set sample increases from 97.31% to 98.51%. The PFCM-ABC-SVM method is effective to diagnose the faults in the PEM fuel cell system, and it is better than other commonly used methods. The PFCM-ABC-SVM method has an advantage in solving the small-sized, nonlinear, and high-dimensional problems and furthermore, provides references for on-line fault diagnosis of a fuel cell system.

**Author Contributions:** Methodology, F.H.; writing—original draft preparation, F.H.; software, F.H. and Y.T.; data curation, Y.T.; writing—review and editing, Y.T. and Q.Z.; validation, Q.Z.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2018YFB0105500.

**Acknowledgments:** The work was sponsored by the National Key Research and Development Program of China—Fuel Cell Bus Electric-Electric Deep Hybrid Power System Platform and Vehicle Development (no. 2018YFB0105500).

**Conflicts of Interest:** The authors declare no conflicts of interest.
