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Advanced Technologies and Applications of Fuel Cells for Clean Energy

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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 15631

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Special Issue Editors

Dr. Jasna Jankovic

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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

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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

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Keywords

fuel cells
hydrogen
clean energy
electrochemistry

Published Papers (3 papers)

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Research

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14 pages, 8584 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app12073421 - 28 Mar 2022

Cited by 1 | Viewed by 1324

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. 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)

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18 pages, 4113 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app11146348 - 09 Jul 2021

Cited by 7 | Viewed by 2592

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. 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)

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Review

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41 pages, 6368 KiB

Open AccessReview

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://0-doi-org.brum.beds.ac.uk/10.3390/app12083776 - 08 Apr 2022

Cited by 18 | Viewed by 10433

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 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)

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