Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine

Weiwei Huo, Weier Li, Chao Sun, Qiang Ren and Guoqing Gong
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Weiwei Huo: School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100092, China
Weier Li: School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100092, China
Chao Sun: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Qiang Ren: Guangzhou Automobile Group Co., Ltd., Automotive Engineering Research Institute, Guangzhou 510006, China
Guoqing Gong: School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100092, China

Energies, 2022, vol. 15, issue 6, 1-15

Abstract: The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization.

Keywords: fuel cell; fault diagnosis; extreme learning machine; support vector machine; genetic algorithm (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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