Development and modeling for process control purposes in PEMs

Saygili Y., Kincal S., Eroglu I.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, vol.40, no.24, pp.7886-7894, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 24
  • Publication Date: 2015
  • Doi Number: 10.1016/j.ijhydene.2014.10.116
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.7886-7894
  • Keywords: PEMFC, Fuel cell piping and instrumentation diagram, Integrated fuel cell modeling, Fuel cell control, MEMBRANE FUEL-CELL, EXCHANGE, DEGRADATION, HUMIDIFIER, SYSTEM
  • Middle East Technical University Affiliated: Yes


To maintain suitable operating conditions, polymer electrolyte membrane (PEM) fuel cell stacks require additional equipment and control systems. Fuel supply, power and thermal management, purge strategy and individual cell voltage control must be in place and operate reliably for a fuel cell system to achieve similar levels of performance as conventional energy generators. System design, auxiliary equipment selection and selection of control strategies have effects on fuel cell efficiency, durability and reliability. In this study we report on our efforts to develop the piping and instrumentation diagram of a 3 kW PEM fuel cell, including the control instrumentation. A semi-empirical model was put together to understand dynamic system behavior for purpose of evaluating possible operating scenarios, in an effort to have useful insight into the system during the equipment selection stage. The model complexity was reduced by ignoring the spatial variations and assuming isothermal stack operation. The stack, cooling system, humidifier, compressor, inlet and outlet manifold were modeled and integrated to formulate a comprehensive prototype model. This model was subsequently used to generate predictions for the responses of the compressor, humidifier, humidification of the stack, power and heat generation for a multitude of dynamic changes in load. With the predictive capability enabled by the model, equipment and algorithm selections can be made in a more directed fashion, reducing the initial design and development costs by delivering a hardware configuration that is close to an ideal one with minimal iterations. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.