Andrew M. Colclasure

Modeling Electrochemical Oxidation of Hydrogen on Ni–YSZ Pattern Anodes

A computational model is developed to represent the coupled behavior of elementary chemistry, electrochemistry, and transport in the vicinity of solid-oxide fuel cell three-phase boundaries. The model is applied to assist the development and evaluation of H_2 charge-transfer reaction mechanisms for Ni–yttria-stabilized zirconia (YSZ) anodes. Elementary chemistry and surface transport for the Ni and YSZ surfaces are derived from prior literature. Previously published patterned-anode experiments [J. Mizusaki et al., Solid State Ionics, 70/71, 52 (1994)] are used to evaluate alternative electrochemical charge-transfer mechanisms. The results show that a hydrogen-spillover mechanism can explain the Mizusaki polarization measurements over wide ranges of gas-phase composition with both anodic and cathodic biases.

Control-Oriented Modeling of a Solid-Oxide Fuel Cell Stack Using an LPV Model Structure

For efficient operation, as well as to avoid operating conditions that can cause damage, fuel cells require a control system to balance fuel and air supply and electrical load. The need to maintain signal constraints during operation, combined with importance of unmeasured variables such as internal stack temperature or fuel utilization, indicate the need for control-oriented models that can be used for estimation and model predictive control. In this paper, we discuss the development of a control-oriented dynamic model of a solid oxide fuel cell stack. Using a detailed physical model as a starting point, we demonstrate the utility of a linear parameter varying (LPV) model structure as a mechanism for model reduction. A novel feature is a non-parametric method for determining the scheduling functions in this model.Copyright

Physically Based Model-Predictive Control for SOFC Stacks and Systems

This paper discusses model-predictive controllers (MPC) that can incorporate physical knowledge of fuel-cell behavior into real-time multiple-input–multiple-output (MIMO) process-control strategies. The controller development begins with a high-fidelity, transient, physical model that represents the physical and chemical processes responsible for fuel-cell function. However, because such large nonlinear models cannot be solved in real time as part of the controller logic, linear reduced-order state-space models are required. The model reduction is accomplished via a process called system identification. The controller is designed to interpret sensors in the context of the reduced-order model and determine optimal actuation sequences that cause the system to follow a desired output trajectory. The process is demonstrated for a tubular SOFC stack that could be used for auxiliary-power unit (APU) applications.

FUEL CELLS – SOLID OXIDE FUEL CELLS | Cells and Stacks

A thermodynamic analysis is developed to predict the maximum possible conversion efficiency (i.e., from available energy in a fuel to electrical output) for solid-oxide fuel cells (SOFCs). Efficiency is represented as the product of three factors (reversible efficiency, part-load efficiency, and fuel utilization), all of which can be evaluated thermodynamically and are independent of the particular membrane–electrode assembly (MEA) structure. Power density, however, depends upon details of the MEA architecture. Optimal SOFC design and operation usually depend upon practical trade-offs between efficiency and power density. The functioning of a complete fuel cell system depends upon the contributions of supporting balance-of-plant components (e.g., fuel-processing reactors and heat exchangers). A thermodynamic analysis, based upon energy availability (exergy), is used to evaluate the integrated system performance. Because of unavoidable losses in the balance-of-plant components, the system-level efficiency is significantly lower than the efficiency of the SOFC itself. Careful integration of the SOFC with supporting components is necessary for overall system performance.