Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cell with Interfacial Electrical Sensors Using...

Area of Science:

• Materials Science
• Electrochemistry
• Energy Storage and Conversion

Background:

• Protonic ceramic electrochemical cells (PCECs) are promising for energy applications but face challenges with component compatibility and interfacial contact.
• Degradation mechanisms in PCECs are not fully understood, hindering performance prediction and material selection.

Purpose of the Study:

• To develop a novel approach combining in situ electrochemical characterization and machine learning for diagnosing PCEC degradation.
• To quantify the contribution of individual cell components to overall degradation.
• To predict the remaining useful life (RUL) of PCECs.

Main Methods:

• Integration of an interfacial electrical sensor within PCECs for in situ monitoring.
• Application of machine learning algorithms to analyze electrochemical data and identify degradation patterns.
• Experimental validation of the developed diagnostic model.

Main Results:

• The oxygen electrode overpotential was found to be 48% less than the oxygen electrode/electrolyte interfacial contact overpotential after 1171 hours.
• Machine learning simulations predicted a remaining useful life (RUL) of up to 2132 hours.
• Increased oxygen electrode overpotential was identified as the root cause of degradation, accounting for 82.9% of total cell degradation.

Conclusions:

• The synergistic approach of in situ sensing and machine learning provides effective failure diagnosis for PCECs.
• This method offers valuable insights for improving performance prediction and material selection, enhancing PCEC durability and efficiency.
• The model's consistency with degradation modes validates its practical applicability.