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Fuel cell hybrid systems have emerged rapidly in efforts to reduce emissions. The success of these systems mainly depends on implementation of suitable control architectures. This paper presents a control system design for a novel fuel cell - IC Engine hybrid power system. Control oriented models of the system components are developed and integrated. Based on the simulation results of the system model, the control variables are identified. The main objective for the control design is to manage fuel, air and exhaust flows in a way to deliver the required load on the system within local constraints. The controller developed for regulating flows in the system is based on model predictive control theory. The performance of the overall control system is assessed through simulations on a nonlinear dynamic model.

Hydrogen crossover and membrane hydration are significant issues for polymer electrolyte fuel cells (PEFC). Hydrogen crossover amounts to a quantity of unspent fuel, thereby reducing the fuel efficiency of the cell, but more significantly it also gives rise to the formation of hydrogen peroxide in the cathode catalyst layer which acts to irreversibly degenerate the polymer electrolyte. Membrane hydration not only strongly governs the performance of the cell, most noticeable through its effect on the ionic conductivity of the membrane, it also influences the onset and propagation of internal degradation and failure mechanisms that curtail the reliability and safety of PEFCs. This paper focuses on how hydrogen crossover and membrane hydration are affected by; (a) characteristic cell geometries, and (b) operating conditions relevant to automotive fuel cells.

This paper presents a control system design strategy for a novel fuel cell - internal combustion engine hybrid power system. Dynamic control oriented models of the system components are developed. The transient behavior of the system components is investigated in order to determine control parameters and set-points. The analysis presented here is the first step towards development of a controller for this complex system. The results indicate various possibilities for control design and development. A control strategy is discussed to achieve system performance optimization.

A qualitative FMEA study of Polymer Electrolyte Fuel Cell (PEFC) technology is established and presented in the current work through a literature survey of mechanisms that govern performance degradation and failure. The literature findings are translated into Fault Tree (FT) diagrams that depict how basic events can develop into performance degradation or failure in the context of the following top events; (1) activation losses; (2) mass transportation losses; (3) Ohmic losses; (4) efficiency losses and (5) catastrophic cell failure. Twenty-two identified faults and forty-seven frequent causes are translated into fifty-two basic events and a system of FTs with twenty-one reoccurring dominant mechanisms. The four most dominant mechanisms discussed that currently curtail sustained fuel cell performance relate to membrane durability, liquid water formation, flow-field design, and manufacturing practices.

PEMFC (proton exchange membrane or polymer electrolyte membrane fuel cell) is a potential candidate as a future power source for automobile applications. Water and thermal management is important to PEMFC operation. Numerical models, which describe the transport and electrochemical phenomena occurring in PEMFCs, are important to the water and thermal management of fuel cells. 3D (three-dimensional) multi-scale CFD (computational fluid dynamics) models take into account the real geometry structure and thus are capable of predicting real operation/performance. In this study, a 3D multi-phase CFD model is employed to simulate a large-scale PEMFC (109.93 cm2) under various operating conditions. More specifically, the effects of operating pressure (1.0-4.0 atm) on fuel cell performance and internal water and thermal characteristics are studied in detail under two inlet humidities, 100% and 40%.

Proton exchange membrane fuel cell (PEMFC) provides a promising future low carbon automotive powertrain solution. The catalyst layer (CL) is its core component which directly influences the output performance. PEMFC performance can be greatly improved by the effective optimization of CL composition. This work demonstrates a deep optimization of CL composition for improving the PEMFC performance, including the platinum (Pt) loading, Pt percentage of carbon-supported Pt and ionomer to carbon ratio of the anode and the cathode,. The simulation results by a PEMFC three-dimensional (3D) computation fluid dynamics (CFD) model coupled with the CL agglomerate model is used to train the artificial neural network (ANN) which can efficiently predict the current density under different CL composition. Squared correlation coefficient (R-square) and mean percentage error in the training set and validation set are 0.9867, 0.2635% and 0.9543, 1.1275%, respectively.