Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System

Feng Han, Ying Tian, Qiang Zou and Xin Zhang
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Feng Han: Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Ying Tian: Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Qiang Zou: Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Xin Zhang: Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China

Energies, 2020, vol. 13, issue 10, 1-18

Abstract: In this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of this method is that it can filter data with Gaussian noise and diagnose faults under dynamic conditions, and the amplitude of characteristic parameters is reduced to ±10%. Under dynamic conditions with Gaussian noise, the faults of the PEM fuel cell system are simulated and the original dataset is established. The possibilistic fuzzy C-means (PFCM) algorithm is used to filter samples with membership and typicality less than 90% and to optimize the original dataset. The artificial bee colony (ABC) algorithm is used to optimize the penalty factor C and kernel function parameter g . Finally, the optimized support vector machine (SVM) model is used to diagnose the faults of the PEM fuel cell system. To illustrate the results of the fault diagnosis, a nonlinear PEM fuel cell simulator model which has been presented in the literature is used. In addition, the PFCM-ABC-SVM method is compared with other methods. The result shows that the method can diagnose faults in a PEM fuel cell system effectively and the accuracy of the testing set sample is up to 98.51%. When solving small-sized, nonlinear, high-dimensional problems, the PFCM-ABC-SVM method can improve the accuracy of fault diagnosis.

Keywords: fault diagnosis; PEM fuel cell system; PFCM-ABC-SVM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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