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This review presents methanol as a potential renewable alternative to fossil fuels in the fight against climate change. It explores the renewable ways of obtaining methanol and its use in efficient energy systems for a net zero-emission carbon cycle, with a special focus on fuel cells. It investigates the different parts of the carbon cycle from a methanol and fuel cell perspective. In recent years, the potential for a methanol economy has been shown and there has been significant technological advancement of its renewable production and utilization. Even though its full adoption will require further development, it can be produced from renewable electricity and biomass or CO2 capture and can be used in several industrial sectors, which make it an excellent liquid electrofuel for the transition to a sustainable economy. By converting CO2 into liquid fuels, the harmful effects of CO2 emissions from existing industries that still rely on fossil fuels are reduced. The methanol can then be used both in the energy sector and the chemical industry, and become an all-around substitute for petroleum. The scope of this review is to put together the different aspects of methanol as an energy carrier of the future, with particular focus on its renewable production and its use in high-temperature polymer electrolyte fuel cells (HT-PEMFCs) via methanol steam reforming.

In this paper, the main faults in a commercial proton exchange membrane fuel cell (PEMFC) stack for micro-combined heat and power ( μ -CHP) application are investigated, with the scope of experimentally identifying fault indicators for diagnosis purposes. The tested faults were reactant starvation (both fuel and oxidant), flooding, drying, CO poisoning, and H2S poisoning. Galvanostatic electrochemical impedance spectroscopy (EIS) measurements were recorded between 2 kHz and 0.1 Hz on a commercial stack of 46 cells of a 100- cm 2 active area each. The results, obtained through distribution of relaxation time (DRT) analysis of the EIS data, show that characteristic peaks of the DRT and their changes with the different fault intensity levels can be used to extract the features of the tested faults. It was shown that flooding and drying present features on the opposite ends of the frequency spectrum due the effect of drying on the membrane conductivity and the blocking effect of flooding that constricts the reactants’ flow. Moreover, it was seen that while the effect of CO poisoning is limited to high frequency processes, above 100 Hz, the effects of H2S extend to below 10 Hz. Finally, the performance degradation due to all the tested faults, including H2S poisoning, is recoverable to a great extent, implying that condition correction after fault detection can contribute to prolonged lifetime of the fuel cell.

This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode-and cathode stoichiometry is 100 % success rate. Overall global accuracy on the test data is 94.6 %.

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