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In this work, we investigated the kinetics and mass-transport limitations of the oxygen reduction reaction in the solid polymer fuel cell. The information obtained from electrochemical experiments and electrode characterization was analyzed with an agglomerate model presented in Part I of this paper. The electrochemical behavior of the cathode was studied by polarizing vs. a hydrogen reference electrode at a low sweep rate. For each potential, the iR-drop was measured with the current-interrupt technique. The cathode structure was investigated by porosimetry and electron microscopy techniques. The effects on the cathode polarization curves of the active layer thickness, oxygen partial pressure, and humidity of the oxygen gas were investigated. On the basis of the model results, conclusions could be drawn regarding the nature of mass-transport limitations because of the characteristic shape of the experimental polarization curves. The simulated curves were fitted to the experimental ones to give the kinetic and masstransport parameters. Finally, we discuss the validity of the model with regard to the values obtained for the transport and structural parameters.Solid polymer fuel cells ͑SPFCs͒ are a promising technology for an efficient and clean production of energy. Their efficiency is affected mostly by the cathode, due to the poor kinetics of the oxygen reduction reaction ͑ORR͒. In order to catalyze this reaction, noble metals such as platinum must be used in order to optimize its utilization. Therefore it is important to investigate which transport phenomena are limiting the reaction rate of the state-of-the-art cathodes. The cathode functioning has been experimentally investigated in numerous works, either in situ in SPFCs 1-4 or as films deposited on rotating disk electrodes ͑RDE͒. 5,6 However, these previous studies do not permit a conclusion about the exact phenomena limiting the reaction rate of the cathode, and this is for the following reasons. In fuel cells, the study of the specific behavior of the cathode requires the use of a reference electrode as well as the correction for the iRdrop occurring between the cathode and the reference electrode. Unfortunately, such an experimental procedure has rarely been employed. In the RDE system, the cathode is immersed in a liquid electrolyte, resulting in an environment different from that of the SPFC. Additionally, when using RDEs, the polarization curves have to be corrected for oxygen diffusion through the liquid electrolyte. This latter correction is not trivial, since the total current density of a porous cathode can present a first-or half-order dependence in oxygen concentration, depending on the mass-transport limitations occurring in the active layer. 7 In this paper, we present the results of an experimental study on SPFC cathodes prepared according to the thin-film procedure. The active layers were deposited on Nafion membranes with the spray technique. The electrochemical behavior of the cathode layers was studied by polarization at a low sweep rate ...

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The paper studies grid connected photovoltaic(PV)-hydrogen/battery systems. The storage component capacities and the rule-based operation strategy parameters are simultaneously optimized by the Genetic Algorithm. Three operation strategies for the hydrogen storage, namely conventional operation strategy, peak shaving strategy and hybrid operation strategy, are compared under two scenarios based on the pessimistic and optimistic costs. The results indicate that the hybrid operation strategy, which combines the conventional operation strategy and the peak shaving strategy, is advantageous in achieving higher Net Present Value (NPV) and Self Sufficiency Ratio (SSR). Hydrogen storage is further compared with battery storage. Under the pessimistic cost scenario, hydrogen storage results in poorer performance in both SSR and NPV. While under the optimistic cost scenario, hydrogen storage achieves higher NPV. Moreover, when taking into account the grid power fluctuation, hydrogen storage achieves better performance in all three optimization objectives, which are NPV, SSR and GI (Grid Indicator).