Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells.

Area of Science:

• Electrochemical Engineering
• Materials Science
• Automotive Engineering

Background:

• Vehicular applications impose harsh conditions on proton exchange membrane fuel cells (PEMFCs), limiting their use in electric propulsion.
• Rapidly varying current demands can cause critical failures like flooding and catalytic degradation, impacting membrane electrode assembly and performance.
• Monitoring internal PEMFC states is crucial due to the significance and cost of catalyst layers.

Purpose of the Study:

• Develop a diagnostic-oriented multi-scale model for PEMFC catalytic degradation.
• Incorporate failure effects on cell dynamics and global stack performance.
• Implement a fault diagnosis scheme for real-time monitoring and reliable operation.

Main Methods:

• A multi-scale PEMFC catalytic degradation model was developed.
• A square root unscented Kalman filter (SRUKF)-based multiple-model fault diagnosis scheme was embedded.
• The SRUKF estimated internal PEMFC parameters, updating a Bayesian framework for model selection and fault indication.

Main Results:

• The developed model successfully incorporated catalytic degradation effects on cell dynamics and stack performance.
• The SRUKF-based diagnosis scheme provided online state estimates and Bayesian model selection for fault indication.
• Simulations using LA 92 and NEDC driving cycles validated the proposed diagnosis scheme's reliability.

Conclusions:

• The proposed diagnostic model and fault diagnosis scheme effectively monitor PEMFC catalytic degradation.
• This approach enhances the reliability and operational safety of PEMFCs in vehicular applications.
• The method provides crucial insights into internal fuel cell states for improved performance management.