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research-article

Membrane Electrolyte Assembly Health Estimation Method for Proton Exchange Membrane Fuel Cells

[+] Author and Article Information
Alexander Headley

Diagnostic Science & Engineering, Sandia National Laboratories, Albuquerque, NM 87123, USA
ajhead2@gmail.com

Martha Gross

Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712, USA
mmgross@utexas.edu

Dongmei Chen

Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712, USA
dmchen@me.utexas.edu

1Corresponding author.

ASME doi:10.1115/1.4037772 History: Received September 06, 2016; Revised August 23, 2017

Abstract

Membrane electrolyte assembly aging is a major concern for deployed proton exchange membrane fuel cell stacks. Studies have shown that working conditions, such as the operating temperature, humidity, and open circuit voltage, have a major effect on degradation rates and also vary significantly from cell to cell. Individual cell health estimations would be very beneficial to maintenance and control schemes. Ideally, estimations would occur in response to the applied load to avoid service interruptions. To this end, this paper presents the use of an Extended Kalman Filter (EKF) to estimate the effective membrane surface area (EMSA) of each cell using cell voltage measurements taken during operation. The EKF method has a low computational cost, and can be applied in real time to estimate the EMSA of each cell in the stack. This yields quantifiable data regarding cell degradation. The EKF algorithm was applied to experimental data taken on a 23-cell stack. The load profiles for the experiments were based on the FTP-75 and HWFET standard drive cycle tests to test the ability of the algorithm to perform in realistic load scenarios. To confirm the results of the EKF method, low performing cells and an additional "healthy" cell were selected for SEM analysis. The images taken of the cells confirm that the EKF accurately identified problematic cells in the stack. The results of this study could be used to formulate online sate of health estimators for each cell in the stack that can operate during normal operation.

Copyright (c) 2017 by ASME
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