Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Advanced Technologies and Applications of Fuel Cells for Clean Energy
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Technologies and Applications of Fuel Cells for Clean Energy

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 19808

Special Issue Editors

Dr. Jasna Jankovic

E-Mail Website
Guest Editor
Department of Materials Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: fuel cells; electrolyzers; batteries; advanced characterization; nature inspired design
Dr. Nada Zamel

E-Mail Website
Guest Editor
Fraunhofer Institute for Solar Energy Systems ISE, Heidenhofstrasse 2, 79110 Freiburg, Germany
Interests: fuel cells; life cycle analysis; material design; data science

Special Issue Information

Dear Colleagues,

The worldwide shift to sustainable energy solutions is experiencing unparalleled momentum. As part of these solutions, hydrogen-based technologies, such as fuel cells, have been attracting increasing attention. Currently in the early stages of commercialization, with close to 800 megawatts in power shipped annually, fuel cells are projected to earn USD 2.5 trillion in annual global revenue, create 30 million new jobs, and reduce global annual CO2 production by 6 gigatons by 2050. Their applications span from portable devices, light duty and heavy duty transportation, all the way to backup and stationary power generation. Continuous development of advanced solutions for fuel cell technologies, in terms of novel chemistries, components, architectures and system design is crucial in order to achieve cost-effective and durable systems, competitive with current power generation solutions.

This Special Issue welcomes high-quality contributions from researchers in the areas of novel materials development, advanced component design and manufacturing, scale-up studies, characterization and degradation mitigation for all types of fuel cells. Case studies of conventional and advanced applications of fuel cells in, for example, drones, robotics, aviation and any other specific application are especially encouraged.

Dr. Jasna Jankovic
Dr. Nada Zamel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuel cells
  • hydrogen
  • clean energy
  • electrochemistry

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

14 pages, 8584 KiB  
Article
Statistical Analysis on Random Matrices of Echo State Network in PEMFC System’s Lifetime Prediction
by Zhiguang Hua, Zhixue Zheng, Marie-Cécile Péra and Fei Gao
Appl. Sci. 2022, 12(7), 3421; https://doi.org/10.3390/app12073421 - 28 Mar 2022
Cited by 3 | Viewed by 1705
Abstract
The data-driven method of echo state network (ESN) has been successfully used in the proton exchange membrane fuel cell (PEMFC) system’s lifetime prediction area. Nevertheless, the uncertainties of the randomly generated input and internal weight matrices in ESN have not been reported yet. [...] Read more.
The data-driven method of echo state network (ESN) has been successfully used in the proton exchange membrane fuel cell (PEMFC) system’s lifetime prediction area. Nevertheless, the uncertainties of the randomly generated input and internal weight matrices in ESN have not been reported yet. In view of this, an ensemble ESN structure is proposed in this paper to analyze the effects of random matrices from a statistical point of view. For each ESN, the particle swarm optimization (PSO) method is utilized to optimize the hyperparameters of the leaking rate, spectral radius, and regularization coefficient. The statistical results of each ensemble ESN are analyzed from 100 repeated tests whose weight matrices are generated randomly. The mean value of the ensemble ESN and a confidence interval (CI) of 95% are given during the long-term lifetime prediction. The effects of two different distribution shapes, i.e., uniform distribution and Gaussian distribution, are fully compared. Finally, the effects of the ensemble structure and two different distribution shapes are tested by three experimental datasets under steady-state, quasi-dynamic, and full dynamic operating conditions. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of Fuel Cells for Clean Energy)
►▼ Show Figures

Figure 1

18 pages, 4113 KiB  
Article
An Artificial Intelligence Solution for Predicting Short-Term Degradation Behaviors of Proton Exchange Membrane Fuel Cell
by Zijun Yang, Bowen Wang, Xia Sheng, Yupeng Wang, Qiang Ren, Shaoqing He, Jin Xuan and Kui Jiao
Appl. Sci. 2021, 11(14), 6348; https://doi.org/10.3390/app11146348 - 9 Jul 2021
Cited by 9 | Viewed by 3066
Abstract
The dead-ended anode (DEA) and anode recirculation operations are commonly used to improve the hydrogen utilization of automotive proton exchange membrane (PEM) fuel cells. The cell performance will decline over time due to the nitrogen crossover and liquid water accumulation in the anode. [...] Read more.
The dead-ended anode (DEA) and anode recirculation operations are commonly used to improve the hydrogen utilization of automotive proton exchange membrane (PEM) fuel cells. The cell performance will decline over time due to the nitrogen crossover and liquid water accumulation in the anode. Highly efficient prediction of the short-term degradation behaviors of the PEM fuel cell has great significance. In this paper, we propose a data-driven degradation prediction method based on multivariate polynomial regression (MPR) and artificial neural network (ANN). This method first predicts the initial value of cell performance, and then the cell performance variations over time are predicted to describe the degradation behaviors of the PEM fuel cell. Two cases of degradation data, the PEM fuel cell in the DEA and anode recirculation modes, are employed to train the model and demonstrate the validation of the proposed method. The results show that the mean relative errors predicted by the proposed method are much smaller than those by only using the ANN or MPR. The predictive performance of the two-hidden-layer ANN is significantly better than that of the one-hidden-layer ANN. The performance curves predicted by using the sigmoid activation function are smoother and more realistic than that by using rectified linear unit (ReLU) activation function. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of Fuel Cells for Clean Energy)
►▼ Show Figures

Figure 1

Review

Jump to: Research

41 pages, 6368 KiB  
Review
Effect of Catalyst Ink and Formation Process on the Multiscale Structure of Catalyst Layers in PEM Fuel Cells
by Huiyuan Liu, Linda Ney, Nada Zamel and Xianguo Li
Appl. Sci. 2022, 12(8), 3776; https://doi.org/10.3390/app12083776 - 8 Apr 2022
Cited by 34 | Viewed by 13703
Abstract
The structure of a catalyst layer (CL) significantly impacts the performance, durability, and cost of proton exchange membrane (PEM) fuel cells and is influenced by the catalyst ink and the CL formation process. However, the relationship between the composition, formulation, and preparation of [...] Read more.
The structure of a catalyst layer (CL) significantly impacts the performance, durability, and cost of proton exchange membrane (PEM) fuel cells and is influenced by the catalyst ink and the CL formation process. However, the relationship between the composition, formulation, and preparation of catalyst ink and the CL formation process and the CL structure is still not completely understood. This review, therefore, focuses on the effect of the composition, formulation, and preparation of catalyst ink and the CL formation process on the CL structure. The CL structure depends on the microstructure and macroscopic properties of catalyst ink, which are decided by catalyst, ionomer, or solvent(s) and their ratios, addition order, and dispersion. To form a well-defined CL, the catalyst ink, substrate, coating process, and drying process need to be well understood and optimized and match each other. To understand this relationship, promote the continuous and scalable production of membrane electrode assemblies, and guarantee the consistency of the CLs produced, further efforts need to be devoted to investigating the microstructure of catalyst ink (especially the catalyst ink with high solid content), the reversibility of the aged ink, and the drying process. Furthermore, except for the certain variables studied, the other manufacturing processes and conditions also require attention to avoid inconsistent conclusions. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of Fuel Cells for Clean Energy)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop