Diagnosis of electrocatalyst degradation in polymer electrolyte fuel cells under automotive conditions

This paper presents a fuzzy inference system approach for diagnosis of electrocatalyst degradation in polymer electrolyte fuel cells (PEFC’s) under automotive conditions. The fuzzy inference system enables diagnosis of electrocatalyst degradation based on fuel cell operating conditions. The method incorporates classification of selected input parameters on a scale of membership to fuzzy sets or categories and provides connection to any consequential degradation through a database of diagnostic rules. Experimental procedures involved drive cycle durability testing including the world harmonized light-duty vehicle test procedure (WLTP) and start/stop cycling. The observed results support the validation of the proposed membership functions within the fuzzy inference system and the database of diagnostic rules. This approach can provide a fast and effective diagnosis of electrocatalyst degradation in PEFC’s and enable proactive decision support for planning operation and maintenance strategies for improved fuel cell reliability, availability and durability.