Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
Browse
2 results
Search Results
Item Electric terminal performance and characterization of solid oxide fuel cells and systems(Montana State University - Bozeman, College of Engineering, 2013) Lindahl, Peter Allan; Chairperson, Graduate Committee: Steven R. ShawSolid Oxide Fuel Cells (SOFCs) are electrochemical devices which can effect efficient, clean, and quiet conversion of chemical to electrical energy. In contrast to conventional electricity generation systems which feature multiple discrete energy conversion processes, SOFCs are direct energy conversion devices. That is, they feature a fully integrated chemical to electrical energy conversion process where the electric load demanded of the cell intrinsically drives the electrochemical reactions and associated processes internal to the cell. As a result, the cell's electric terminals provide a path for interaction between load side electric demand and the conversion side processes. The implication of this is twofold. First, the magnitude and dynamic characteristics of the electric load demanded of the cell can directly impact the long-term efficacy of the cell's chemical to electrical energy conversion. Second, the electric terminal response to dynamic loads can be exploited for monitoring the cell's conversion side processes and used in diagnostic analysis and degradation-mitigating control schemes. This dissertation presents a multi-tier investigation into this electric terminal based performance characterization of SOFCs through the development of novel test systems, analysis techniques and control schemes. First, a reference-based simulation system is introduced. This system scales up the electric terminal performance of a prototype SOFC system, e.g. a single fuel cell, to that of a full power-level stack. This allows realistic stack/load interaction studies while maintaining explicit ability for post-test analysis of the prototype system. Next, a time-domain least squares fitting method for electrochemical impedance spectroscopy (EIS) is developed for reduced-time monitoring of the electrochemical and physicochemical mechanics of the fuel cell through its electric terminals. The utility of the reference-based simulator and the EIS technique are demonstrated through their combined use in the performance testing of a hybrid-source power management (HSPM) system designed to allow in situ EIS monitoring of a stack under dynamic loading conditions. The results from the latter study suggest that an HSPM controller allows an opportunity for in-situ electric terminal monitoring and control-based mitigation of SOFC degradation. As such, an exploration of control-based SOFC degradation mitigation is presented and ideas for further work are suggested.Item Simulation, design and validation of a solid oxide fuel cell powered propulsion system for an unmanned aerial vehicle(Montana State University - Bozeman, College of Engineering, 2009) Lindahl, Peter Allan; Chairperson, Graduate Committee: Steven R. ShawThis thesis presents a physically-based model for design and optimization of a fuel cell powered electric propulsion system for an Unmanned Aerial Vehicle (UAV). Components of the system include a Solid Oxide Fuel Cell (SOFC) providing power, motor controller, Brushless DC (BLDC) motor, and a propeller. Steady-state models for these components are integrated into a simulation program and solved numerically. This allows an operator to select constraints and explore design trade-offs between components, including fuel cell, controller, motor and propeller options. We also presents a graphical procedure using the model that allows rapid assessment and selection of design choices, including fuel cell characteristics and hybridization with multiple sources. To validate this simulation program, a series of experiments conducted on an instrumented propulsion system in a low-speed wind tunnel is provided for comparison. These experimental results are consistent with model predictions.