4.7 Article

A vehicular proton exchange membrane fuel cell system co-simulation modeling method based on the stack internal distribution parameters monitoring

Journal

ENERGY CONVERSION AND MANAGEMENT
Volume 197, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2019.111898

Keywords

Proton exchange membrane fuel cell; Dynamic response; Simulink and Fluent co-simulation; Internal distribution

Funding

  1. National Natural Science Foundation of China [21805210]
  2. Shanghai Sailing Program [18YF1424300]
  3. Ministry of Science and Technology of China [2018YFB0105405, 2017YFB0103102]
  4. Fundamental Research Funds for the Central Universities [1700219153]

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The lifetime of vehicular proton exchange membrane fuel cell is one of the key factors restricting the commercialization of fuel cell vehicles. It's well recognized variable load conditions have the greatest impact on fuel cell degradation. Studying dynamic load characteristics is very crucial for fuel cell long-life design and optimal control. Since experiments are not easy to monitor fuel cell internal distribution, the dynamic response studying is commonly implemented in model simulation. The fuel cell system has complicated structures and large differences in length scale, to make up for the insufficient precision and limited research content in existing models, this paper uses an innovative modeling method, Simulink and Fluent co-simulation method to establish a fuel cell system-level model. It can obtain not only response characteristics of auxiliary subsystems and the system dynamic performance, but also the internal physical quantities distribution changes. Multiple simulations and comparisons are made to observe voltage dynamic response and internal concentration distribution. Impacts of subsystem's response characteristics and system's critical operational parameters and mechanism behind them are analyzed. The co-simulation method and obtained results in this paper can be used for future research of fuel cell system-level modeling and provide theoretical basis for dynamic capacity optimization.

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