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Article

Abstract

Porous electrode is the critical component of solid-oxide fuel cells (SOFCs) and provides a functional material backbone for multi-physicochemical processes. Model based electrode designs could significantly improve SOFC performance. This task is usually performed via parameter studies for simple case and assumed property distributions for graded electrodes. When nonlinearly coupled multiparameters of electrodes are considered, it could be very difficult for the model based parameter study method to effectively and systematically search the design space. In this research, the optimization approach with a genetic algorithm is demonstrated for this purpose. An anode-supported proton conducting SOFC integrated with a fuel supply system is utilized as a physical base for the model development and the optimization design. The optimization results are presented, which are difficult to obtain for parametric study method.

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©Journal of The Electrochemical Society 2011, The Electrochemical Society.

© The Electrochemical Society, Inc. 2011. All rights reserved. Except as provided under U.S. copyright law, this work may not be reproduced, resold, distributed, or modified without the express permission of The Electrochemical Society (ECS). The archival version of this work was published in Journal of The Electrochemical Society.

Publisher’s Version: http://dx.doi.org/10.1149/1.3517476

Shi, J. & Xue, X. (7 December 2010). Optimization Design of Electrodes for Anode-Supported Solid Oxide Fuel Cells via Genetic Algorithm. Journal of The Electrochemical Society, 158 (2), B143 – B151. http://dx.doi.org/10.1149/1.3517476

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