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

Abstract

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.

A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

“…The assumptions typically taken into consideration for the model-based approach are very limited for the data available. Taking this into account, a different approach is presented in [169]. By developing a 3D semi-empirical FC model, the authors were able to consider several electrochemical and thermal parameters, as well as the impedance.…”

“…Symptoms Consequences Diagnostics Recovering mechanism [104] operation at nominal power [112] low temperature, and poor air distribution [52] water droplets retained at the GDL [76] anode flooding (unoptimized exhaustion system) [112] excess of water at the anode [113] increased pressure drops [54,118] increased membrane resistance [156] high level of overall impedance [159] double layer effect affected [101] temperature decreasing rapidly (oscillating dewpoint) + increased cathode pressure [76] voltage degradation [169] internal humidity levels higher than 100% [169] high reactants pressure [174] reactants hygrometry higher than 1.1 [125] low air stoichiometry [100] decrease in electric power [118] increased imaginary and real part in EIS results-Nyquist plot-(cathode flooded) [118] decrease temperature (bigger EIS semi-circle diameter) [52,53] neutron imaging [101] online machine learning: ENN (cathode pressure residuals) [112] infrared spectroscopy [113] pressure, mass flow rate and humidity monitorization [114] anode to cathode pressure drop [115][116][117] EIS [118] empirical equivalent model parameter estimation [156] harmonic impedance measurement [159] online threshold around the nominal polarization curve (current interrupt method) [160] online signal based (EMD) [161] online machine learning: BN [163][164][165] online machine learning algorithm…”

A System-Level Modeling of PEMFC Considering Degradation Aspect towards a Diagnosis Process

“…The FC can produce a large quantity of liquid water, degrading the performance. On the contrary, if the water becomes scarce in the membrane, then the drying phenomenon occurs, also decreasing the performance [36].…”

A Review of Recent Developments and Advanced Applications of High-Temperature Polymer Electrolyte Membranes for PEM Fuel Cells

“…An increase in the operating temperature can positively influence cell performance by decreasing the ohmic loss of the membrane. In the MEAs made by PBI, there is an indirect relation between temperature and the ohmic loss, which is due to decreasing the ohmic resistance of the membrane because of the improved current of ions through electrolytes [13,205]. Gaining an optimal operational condition with the MEAs is one of the fundamental necessities for developing the HT-PEMs.…”