Evaluation and Optimization of Fuel Cell Performance

Dear Colleagues,

Fuel cells, particularly proton exchange membrane (PEM) fuel cells, have become an important part of the energy mix schemes that can be used to replace some of the conventional fossil-fuels-fed energy devices due to their higher power densities, lower operating temperatures, and zero emission. PEM fuel cells have higher efficiencies in direct electrical energy conversion as they can directly convert the chemical energy of the fuel into productive work without involving any thermodynamic cycle. Moreover, their higher power densities and lower operating temperatures make them appropriate for automotive power systems as well as power generation devices for portable electronics and stationary units.

However, the high costs associated with manufacturing and performance testing of PEM fuel cells often make experimental studies uneconomical. Therefore, most research work on fuel cells is concerned with improving cell performance by maximizing efficiency while minimizing manufacturing and test costs through computational fluid dynamics (CFD) analyses. Therefore, modeling and simulation play a particularly important role in the development of fuel cell systems.

This Special Issue on “Evaluation and Optimization of Fuel Cell Performance” will curate novel advances in research which either use modeling and simulation as an important component of the analysis of fuel cell systems using either commercial or open source software or present the development of new and better models of fuel cell systems or fuel cell components. Advances obtained using experimental methods are also welcomed.  

In order to maximize impact, cell-scale multiphase flow modelling of PEMFC based on fundamental processes of the complex two-phase transport, full-scale PEM fuel cell models as well as research articles using open-source software are particularly welcomed.

Topics include, but are not limited to:

• The development of models or simulations of the electrochemical performance of fuel cell units.
• Simulation techniques, software, algorithms, or other tools for the modeling and simulation of transport phenomena in fuel cell units.
• Water management in PEM fuel cells.
• Design, analysis, control, preparation, optimization, operation, and manufacturing innovation of fuel cell systems, including fuel cell units and their auxiliary equipment.

Thank you; I hope you consider participating in this Special Issue.

Dr. Xinyu Zhang
Guest Editor

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• modeling
• simulation
• multiphase flows
• fuel cells
• tools
• algorithms
• software
• design
• catalyst layer
• manufacture innovation

Title: Machine learning-based assessment technique of state-of-health estimation of proton exchange membrane fuel cells
Authors: Hasan Çınar 1* and Süleyman Tunçel 2*
Affiliation: 1 Faculty of Aeronautics and Astronautics, İskenderun Technical University, İskenderun 31200, Turkey; 2 Electrical Electronic Engineering, Faculty of Engineering and Natural Sciences, İskenderun Technical University, İskenderun 31200, Turkey;
Abstract: Proton exchange membrane fuel cells (PEMFC) can be used as the main source of energy in fully electric ground and air vehicles because they provide high energy density and have fast charging time. Additionally, PEMFCs do not have any greenhouse emissions. However, despite these advantages of fuel cells, problems with state of health predictions are one of the obstacles to their commercialization in electric vehicles. Therefore, the health of PEMFC should carefully be mon-itored for the safe and healthy missions of the electrical vehicles. In this context, the current study implements state-of-the-art data-driven machine learning algo-rithms for PEMFC status estimation. For this purpose, we employ Bagging re-gression, Catboost, Decision Tree, and Random Forest algorithms to estimate the output power of the PEMFC by using a publicly available dataset. The results show that data-driven machine learning algorithms achieve low error rates in output power estimation of PEMFC. Additionally, this study provides an exten-sive comparison of data-driven machine learning algorithms for fuel cell health status prediction.