scholarly journals A Multi-Stage Fault Diagnosis Method for Proton Exchange Membrane Fuel Cell Based on Support Vector Machine with Binary Tree

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6526
Author(s):  
Jiaping Xie ◽  
Chao Wang ◽  
Wei Zhu ◽  
Hao Yuan
Keyword(s):  
Fault Diagnosis ◽  
Binary Tree ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cells ◽  
Proton Exchange ◽  
Support Vector ◽  
Multi Stage ◽  
Fault Features ◽  
Diagnosis Method ◽  
Exchange Membrane

The reliability and durability of the proton exchange membrane (PEM) fuel cells are vital factors restricting their applications. Therefore, establishing an online fault diagnosis system is of great significance. In this paper, a multi-stage fault diagnosis method for the PEM fuel cell is proposed. First, the tests of electrochemical impedance spectroscopy under various fault conditions are conducted. Specifically, prone recoverable faults, such as flooding, membrane drying, and air starvation, are included, and different fault degrees from minor, moderate to severe, are covered. Based on this, an equivalent circuit model (ECM) is selected to fit impedance spectroscopy by the hybrid genetic particle swarm optimization algorithm, and then fault features are determined by the analysis of each model parameter under different fault conditions. Furthermore, a multi-stage fault diagnosis model is constructed with the support vector machine with the binary tree, in which fault features obtained from the ECM are used as the characteristic inputs to realize the fault classification (including fault type and fault degree) online. The results show that the accuracy of the basic fault test and subdivided fault test can reach 100% and 98.3%, respectively, which indicates that the proposed diagnosis method can effectively identify flooding, drying, and air starvation of PEM fuel cells.

Synergien zwischen Batterie- und Brennstoffzellen/Synergies between battery and fuel cells - Evaluation based on structural and machine parameters in production processes

2020 ◽  
Vol 110 (10) ◽  
pp. 735-741
Author(s):  
Jens Schäfer ◽  
Hannes Wilhelm Weinmann ◽  
Dominik Mayer ◽  
Tobias Storz ◽  
Janna Hofmann ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cells ◽  
Proton Exchange ◽  
Manufacturing Industries ◽  
Sich Eine ◽  
Future Technology ◽  
The Past ◽  
Production Processes ◽  
Exchange Membrane

Nach Ankündigung diverser batterieelektrischer Modelle wird auch die PEM (Proton Exchange Membrane)-Brennstoffzelle als mögliche Zukunftstechnologie im Last- und Linienverkehr diskutiert. Ob und wann sich eine Technologie durchsetzt, hängt von der verwendeten Produktionstechnik ab, denn diese bestimmt Stückzahlen und resultierende Kosten. Die Vergangenheit zeigt, dass sich produzierende Industrien oft entlang vorhandener Kompetenzen in etablierten Bereichen entwickelt haben. In diesem Beitrag sollen daher Synergiepotenziale zwischen der Batterie- und Brennstoffzellenfertigung diskutiert werden.   Following the announcement of various battery electric models, PEM fuel cells are also discussed as a future technology in truck and line traffic. Whether and when a technology will be generally accepted depends largely on the production technology used, as this determines the number of units and the resulting costs. The past has shown that manufacturing industries have often developed along existing competencies in established areas. This article will therefore discuss the potential synergies between battery and fuel cell production.

scholarly journals Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3144
Author(s):  
K. V. S. Bharath ◽  
Frede Blaabjerg ◽  
Ahteshamul Haque ◽  
Mohammed Ali Khan
Keyword(s):  
Fuel Cells ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Data Driven ◽  
Fault Classification ◽  
Fault Identification ◽  
Support Vector ◽  
Model Based ◽  
Data Limitations ◽  
Exchange Membrane

This paper develops a model-based data driven algorithm for fault classification in proton exchange membrane fuel cells (PEMFCs). The proposed approach overcomes the drawbacks of voltage and current density assumptions in conventional model-based fault identification methods and data limitations in existing data driven approaches. This is achieved by developing a 3D model of fuel cells (FC) based on semi empirical model, analytical representation of electrochemical model, thermal model, and impedance model. The developed model is simulated for membrane drying and flooding faults in PEMFC and their effects are identified for the action of varying temperature, pressure, and relative humidity. The ohmic, concentration, activation and cell voltage losses for the simulated faults are observed and processed with wavelet transforms for feature extraction. Furthermore, the support vector machine learning algorithm is adapted to develop the proposed fault classification approach. The performance of the developed classifier is tested for an unknown data and calibrated through classification accuracy. The results showed 95.5% training efficiency and 98.6% testing efficiency.

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