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A vehicular proton exchange membrane fuel cell system co-simulation modeling method based on the stack internal distribution parameters monitoring.
- Source :
-
Energy Conversion & Management . Oct2019, Vol. 197, pN.PAG-N.PAG. 1p. - Publication Year :
- 2019
-
Abstract
- • Fuel cell system-level modeling is achieved by Simulink and Fluent co-simulation. • Variable load processes in this paper are designed based on the DST protocol. • Simulate impacts of subsystem's response characteristics and operating parameters. • Analyze mechanism behind these impacts, combining internal distribution changes. • Can not only obtain response of subsystems, but also monitor internal distribution. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01968904
- Volume :
- 197
- Database :
- Academic Search Index
- Journal :
- Energy Conversion & Management
- Publication Type :
- Academic Journal
- Accession number :
- 138342117
- Full Text :
- https://doi.org/10.1016/j.enconman.2019.111898