Multimode sensing technique for carbon monoxide plume tracking and forecasting for reliable field deployed air breathing PEM fuel cell operation

Abstract

An air breathing proton exchange membrane fuel cell (PEMFC) combines H2 or reformed hydrocarbon fuel and O2 from air to produce electrical energy. The efficiency of nano-scale platinum catalysts at the fuel cell cathode is highly susceptible to carbon monoxide (CO) poisoning and results in irreversible damage to the electrode. Higher CO2 level in air does not induce catalytic poisoning, however it lowers the partial pressure of O2 at cathode. This results in a drop of fuel cell output power. Our work proposes a combined range sensing and proximate sensing based approach for tracking of smoke plume. Our approach helps to tackle the PEMFC degradation issue by forecasting nature of the plume in vicinity of the fuel cell. The simulation results quantify the dynamic changes in PEMFC electrode resistance with respect to higher levels of CO concentration in air. Drop in fuel cell output power with respect to higher mass fractions of CO2 and ambient humidity are also quantified in this work. The proposed threat prediction based approach helps to enhance the lifetime of a field deployed air PEMFC by reducing and/or inhibiting the air contaminant based fuel cell degradation mechanisms.