This work aims at implementing an advanced monitoring and diagnostic tool for μ-CHP PEM fuel cell systems running on-board. Such a tool is able to determine the FC current status (condition monitoring) to support stack failures detection. Six faults are investigated: fuel starvation, air starvation, flooding, drying, CO contamination and sulphur poisoning. The developed methodology is based on the use of the Electrochemical Impedance Spectroscopy (EIS) measurements and Equivalent Circuit Model (ECM) approach to infer on them. Experimental data from HEALTH-CODE project are exploited for training and to finally validate the model-based algorithm. The fault detection and isolation algorithm, which works on the relevant features extracted from EIS data, is herein reported. Eventually, the offline validation results of the algorithm are presented.
ECM-based algorithm for on-board PEMFCs diagnosis
Adinolfi E. A.
;Polverino P.;Pianese C.
2020-01-01
Abstract
This work aims at implementing an advanced monitoring and diagnostic tool for μ-CHP PEM fuel cell systems running on-board. Such a tool is able to determine the FC current status (condition monitoring) to support stack failures detection. Six faults are investigated: fuel starvation, air starvation, flooding, drying, CO contamination and sulphur poisoning. The developed methodology is based on the use of the Electrochemical Impedance Spectroscopy (EIS) measurements and Equivalent Circuit Model (ECM) approach to infer on them. Experimental data from HEALTH-CODE project are exploited for training and to finally validate the model-based algorithm. The fault detection and isolation algorithm, which works on the relevant features extracted from EIS data, is herein reported. Eventually, the offline validation results of the algorithm are presented.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
