Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Browser

► Journal Browser

Advanced Research on Fuel Cells and Hydrogen Energy Conversion

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A5: Hydrogen Energy".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 21853

Share This Special Issue

Special Issue Editors

Dr. Yanzhou Qin

E-Mail Website
Guest Editor

State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
Interests: fuel cell technology; hydrogen production and storage; hydrogen power system integration; electrochemistry; mass transfer and thermal management

Dr. Yulin Wang

E-Mail Website
Guest Editor

Tianjin Key Lab of Refrigeration Technology, Tianjin University of Commerce, Tianjin 300134, China
Interests: fuel cells; heat and mass transfer in porous medium; data center cooling technology
Special Issues, Collections and Topics in MDPI journals

Dr. Xiao Ma

E-Mail Website
Guest Editor

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100190, China
Interests: ICE; combustion; PM; fuel; optical diagnostics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As environmental problems arising from excessive emissions become a worldwide concern, more attention is given to the development of clean energy technologies. Hydrogen is regarded as the ultimate energy source among several candidates since it has zero emissions and high utilization efficiency. Water electrolysis converts renewable energy sources such as wind, solar and ocean sources into hydrogen. On the other hand, fuel cells consume hydrogen to generate electricity and heat. From production to utilization, both ends determine the overall efficiency of the hydrogen industry. Hence, the continuous progress of hydrogen conversion technologies is the key to realizing the future hydrogen economy and society. For fuel cells, we are facing a series of technical challenges. Critical materials such as membrane, catalyst and membrane electrode assembly still require further low-cost and large-scale preparation solutions. Flow fields, cooling plates and assembled stacks need further optimal designs to solve the problems of hydrothermal management and performance uniformity. Additionally, in different application scenarios, fuel cell system construction and control strategy also need to be proposed, updated and optimized according to actual requirements. Similar problems in materials, components and systems also hamper the development of other hydrogen conversion technologies. With numerical simulation, experimental characterization and policy planning, more original and meaningful work is giving contributions to the competitiveness improvement of hydrogen energy.

This Special Issue welcomes extensive topics on hydrogen energy conversion technologies, including fuel cells, electrolysis, hydrogen internal combustion engines, etc. Numerical and experimental studies on advanced fuel cell technologies are especially encouraged.

Dr. Yanzhou Qin
Dr. Yulin Wang
Dr. Xiao Ma
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. Energies 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 2600 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
electrolysis
fuel reforming
hydrogen storage
hydrogen internal combustion engines
advanced numerical simulation methods
advanced experimental designs and characterizations
advanced water/vapor and thermal management strategy
integrated hydrogen energy systems
low-carbon and sustainable energy policy and strategy

Published Papers (12 papers)

Download All Papers

Research

Jump to: Review

20 pages, 21596 KiB

Open AccessArticle

Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) Parameters Using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC Converter for Improving the Performance of Proton-Exchange Membrane Fuel Cells

by Yurdagül Benteşen Yakut

Energies 2024, 17(4), 890; https://doi.org/10.3390/en17040890 - 14 Feb 2024

Viewed by 450

Abstract

In this article, the control of a DC/DC converter was carried out using the proposed methods of conventional PI, PSO-based PI, PSO-based FOPI, GA-based PI, and GA-based FOPI controllers in order to improve the performance of PEMFCs. Simulink models of a PEMFC model with two inputs—hydrogen consumption and oxygen air flow—and with controllers were developed. Then, the outputs of a system based on conventional PI were compared with the proposed methods. IAE, ISTE, and ITAE were employed as fitness functions in optimization algorithms such as PSO and GA. Fitness function value, maximum overshoot, and rising time were utilized as metrics to compare the performance of the methods. PI and FOPI parameters were optimized with the proposed methods and the results were compared with traditional PI in which the optimum parameters were determined by an empirical approach. This research study indicates that the proposed methods perform better than the conventional PI method. However, it becomes apparent that the GA-FOPI approach outperforms the others. The simulation result also shows that the PEMFC model with conventional PI and FOPI controllers in which the controller parameters are tuned using PSO and GA has an acceptable control performance. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

18 pages, 5370 KiB

Open AccessArticle

Steady-State Voltage Modelling of a HT-PEMFC under Various Operating Conditions

by Sylvain Rigal, Amine Jaafar, Christophe Turpin, Théophile Hordé, Jean-Baptiste Jollys and Paul Kreczanik

Energies 2024, 17(3), 573; https://doi.org/10.3390/en17030573 - 24 Jan 2024

Viewed by 512

Abstract

In this work, a commercially available membrane electrode assembly from Advent Technology Inc., developed for use in high-temperature proton exchange membrane fuel cells, was tested under various operating conditions (OCs) according to a sensibility study with three OCs varying on three levels: hydrogen gas over-stoichiometry (1.05, 1.2, 1.35), air gas over-stoichiometry (1.5, 2, 2.5), and temperature (140 °C, 160 °C, 180 °C). A polarization curve (V-I curve) was performed for each set of operating conditions (27 V-I curves in total). A semi-empirical and macroscopic (0D) model of the cell voltage was developed in steady-state conditions to model these experimental data. With the proposed parameterization approach, only one set of parameters is used in order to model all the experimental curves (simultaneous optimization with 27 curves). Thus, an air over-stoichiometry-dependent model was developed. The obtained results are promising between 0.2 and 0.8 A·cm⁻²: an average error less than 1.5% and a maximum error around 7% between modeled and measured voltages with only 9 parameters to identify. The obtained parameters appear consistent, regardless of the OCs. The proposed approach with only one set of parameters seems to be an interesting way to converge towards the uniqueness of consistent parameters. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

16 pages, 4120 KiB

Open AccessArticle

Predicting the Performance of PEM Fuel Cells by Determining Dehydration or Flooding in the Cell Using Machine Learning Models

by Jaydev Chetan Zaveri, Shankar Raman Dhanushkodi, C. Ramesh Kumar, Jan Taler, Marek Majdak and Bohdan Węglowski

Energies 2023, 16(19), 6968; https://doi.org/10.3390/en16196968 - 06 Oct 2023

Cited by 1 | Viewed by 895

Abstract

Modern industries encourages the use of hydrogen as an energy carrier to decarbonize the electricity grid, Polymeric Electrolyte membrane fuel cell which uses hydrogen as a fuel to produce electricity, is an efficient and reliable ‘power to gas’ technology. However, a key issue obstructing the advancement of PEMFCs is the unpredictability of their performance and failure events caused by flooding and dehydration. The accurate prediction of these two events is required to avoid any catastrophic failure in the cell. A typical approach used to predict failure modes relies on modeling failure-induced performance losses and monitoring the voltage of a cell. Data-driven machine learning models must be developed to address these challenges. Herein, we present a machine learning model for the prediction of the failure modes of operating cells. The model predicted the relative humidity of a cell by considering the cell voltage and current density as the input parameters. Advanced regression techniques, such as support vector machine, decision tree regression, random forest regression and artificial neural network, were used to improve the predictions. Features related to the model were derived from cell polarization data. The model’s results were validated with real-time test data obtained from the cell. The statistical machine learning models accurately provided information on the flooding- and dehydration-induced failure events. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

31 pages, 6578 KiB

Open AccessArticle

Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems

by Thangaraj Yuvaraj, Thirukoilur Dhandapani Suresh, Arokiasamy Ananthi Christy, Thanikanti Sudhakar Babu and Benedetto Nastasi

Energies 2023, 16(19), 6869; https://doi.org/10.3390/en16196869 - 28 Sep 2023

Cited by 3 | Viewed by 952

Abstract

The research presented in this article aims at the modelling and optimization of hydrogen-fuel-cell-based distributed generation (HFC-DG) to minimize the effect of electric vehicle charging stations (EVCSs) in a radial distribution system (RDS). The key objective of this work is to address various challenges that arise from the integration of EVCSs, including increased power demand, voltage fluctuations, and voltage stability. To accomplish this objective, the study utilizes a novel spotted hyena optimizer algorithm (SHOA) to simultaneously optimize the placement of HFC-DG units and EVCSs. The main goal is to mitigate real power loss resulting from the additional power demand of EVCSs in the IEEE 33-bus RDS. Furthermore, the research also investigates the influence of HFC-DG and EVCSs on the reliability of the power system. Reliability is crucial for all stakeholders, particularly electricity consumers. Therefore, the study thoroughly examines how the integration of HFC-DG and EVCSs influences system reliability. The optimized solutions obtained from the SHOA and other algorithms are carefully analyzed to assess their effectiveness in minimizing power loss and improving reliability indices. Comparative analysis is conducted with varying load factors to estimate the performance of the presented optimization approach. The results prove the benefits of the optimization methodology in terms of reducing power loss and improvising the reliability of the RDS. By utilizing HFC-DG and EVCSs, optimized through the SHOA and other algorithms, the research contributes to mitigating power loss caused by EVCS power demand and improving overall system reliability. Overall, this research addresses the challenges associated with integrating EVCSs into distribution systems and proposes a novel optimization approach using HFC-DG. The findings highlight the potential benefits of this approach in terms of minimizing power loss, enhancing reliability, and optimizing distribution system operations in the context of increasing EV adoption. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

16 pages, 5264 KiB

Open AccessArticle

Hydrogen Production System through Dimethyl Ether Autothermal Reforming, Based on Model Predictive Control

by Tie-Qing Zhang, Seunghun Jung and Young-Bae Kim

Energies 2022, 15(23), 9038; https://doi.org/10.3390/en15239038 - 29 Nov 2022

Cited by 1 | Viewed by 1584

Abstract

In this study, a thermodynamic analysis of the low temperature autothermal reforming (ATR) of dimethyl ether (DME) for hydrogen production was conducted. The Pd/Zn/γ-Al₂O₃ catalyst coated on the honeycomb cordierite ceramic was applied to catalyze the reaction, and the optimum activity temperature of this catalyst was demonstrated experimentally and through simulations to be 400 °C. Furthermore, an optimal model predictive control (MPC) strategy was designed to control the hydrogen production rate and the catalyst temperature. Experimental and simulation results indicated that the controller was automated and continuously reliable in the hydrogen production system. By establishing the state-space equations of the autothermal reformer, it can precisely control the feed rates of DME, high-purity air and deionized water. Ultimately, the hydrogen production rate can be precisely controlled when the demand curve changed from 0.09 to 0.23 mol/min, while the catalyst temperature was maintained at 400 °C, with a temporary fluctuation of 4 °C during variations of the hydrogen production rate. Therefore, the tracking performance of the hydrogen production and the anti-disturbance were satisfactory. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

19 pages, 3795 KiB

Open AccessArticle

Coupled FEM and CFD Modeling of Structure Deformation and Performance of PEMFC Considering the Effects of Membrane Water Content

by Zizhe Dong, Yuwen Liu and Yanzhou Qin

Energies 2022, 15(15), 5319; https://doi.org/10.3390/en15155319 - 22 Jul 2022

Cited by 4 | Viewed by 1537

Abstract

Based on a coupled finite element method (FEM) and computational fluid dynamics (CFD) model, the structural deformation and performance of a proton exchange membrane fuel cell (PEMFC) under different membrane water contents are studied. The water absorption behavior of the membrane is investigated experimentally to obtain its expansion coefficient with water content, and the Young’s modulus of the membrane and catalyst (CL) are obtained through a tensile experiment. The simulation results show that the deformation of the membrane increases with water content, and membrane swelling under the channel is larger than that under the rib, forming a surface bump under the channel. The structural changes caused by the membrane water content have little effect on the performance of PEMFC in the low-current density range; while its influence is significant in the medium- and high-current density range. A medium membrane water content value of 12 achieves the best fuel cell performance due to the balance of membrane resistance and mass transport. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

13 pages, 3210 KiB

Open AccessArticle

High Refractive Index Diphenyl Sulfide Photopolymers for Solar Cell Antireflection Coatings

by Jingran Zhang, Baozhu Li, Heran Song, Chen Zhao, Songfeng Liang, Zhurong Dong and Jie Yu

Energies 2022, 15(11), 3972; https://doi.org/10.3390/en15113972 - 27 May 2022

Cited by 4 | Viewed by 2303

Abstract

The anti-reflection film can effectively reduce the surface reflectivity of solar photovoltaics, increase the transmittance of light, and improve the photoelectric conversion efficiency. The high refractive index coating is an important part of the anti-reflection film. However, the traditional metal oxide coating has poor stability and complicated processes. To address this issue, we prepared two organic high refractive index (HRI) photopolymers by modifying epoxy acrylic acid with 4,4′-thiodibenzenethiol, which can be surface patterned by nanoimprinting to prepare antireflection coatings. As a result, two modified photopolymers with high refractive index (n > 1.63), high optical transmittance (T > 95%), and thermal stability (T_g > 100 °C) are obtained after curing. In particular, the diphenyl sulfide photopolymer modified by ethyl isocyanate acrylate has a refractive index up to 1.667 cured by UV light. Our work confirms that the organic HRI photopolymer can be obtained by introducing high molar refractive index groups, with potential to be applied as a PV cell power conversion efficiency material. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Graphical abstract

16 pages, 1919 KiB

Open AccessArticle

A Lumped-Mass Model of Membrane Humidifier for PEMFC

by Hoang Nghia Vu, Xuan Linh Nguyen and Sangseok Yu

Energies 2022, 15(6), 2113; https://doi.org/10.3390/en15062113 - 14 Mar 2022

Cited by 3 | Viewed by 3428

Abstract

Maintaining the performance of a fuel cell stack requires appropriate management of water in the membrane electrode. One solution is to apply an external humidifier to the supply gases. However, the operating conditions change continuously, which significantly affects the humidifier performance and supply gas characteristics. A straightforward humidifier module is needed for integration with the fuel cell system model. In this study, a lumped-mass model was used to simulate a hollow-fiber membrane humidifier and investigate the effects of various input conditions on the humidifier performance. The lumped-mass model can account for heat transfer and vapor transport in the membrane bundle without losing simplicity. The humidifier module was divided into three parts: a heat and mass exchanger in the middle and two manifolds at the ends of the exchanger. These components were modeled separately and linked to each other according to the flow characteristics. Simulations were performed to determine the humidifier response under both steady-state and transient conditions, and water saturation was observed in the outlet manifold that may affect the humidifier performance. The findings on the effects of the operating conditions and humidifier dimensions on the cathode gas can be used to improve humidifier design and control. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

Review

Jump to: Research

17 pages, 4265 KiB

Open AccessReview

Topology and Control of Fuel Cell Generation Converters

by Jinghua Zhou, Qi Zhang and Jin Li

Energies 2023, 16(11), 4525; https://doi.org/10.3390/en16114525 - 05 Jun 2023

Cited by 3 | Viewed by 1535

Abstract

Fuel cell power generation is one of the important ways of utilizing hydrogen energy, which has good prospects for development. However, fuel cell volt-ampere characteristics are nonlinear, the output voltage is low and the fluctuation range is large, and a power electronic converter matching its characteristics is required to achieve efficient and stable work. Based on the analysis of the fuel cell’s characteristic mechanism, maximum power point tracking algorithm, fuel cell converter characteristics, application and converter control strategy, the paper summarizes the general principles of the topology of fuel cell converters. In addition, based on the development status of new energy, hydrogen energy is organically combined with other new energy sources, and the concept of 100% absorption system of new energy with green hydrogen as the main body is proposed to provide a reference for the development of hydrogen energy. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

32 pages, 12311 KiB

Open AccessReview

Application of Machine Learning in Fuel Cell Research

by Danqi Su, Jiayang Zheng, Junjie Ma, Zizhe Dong, Zhangjie Chen and Yanzhou Qin

Energies 2023, 16(11), 4390; https://doi.org/10.3390/en16114390 - 29 May 2023

Cited by 7 | Viewed by 2290

Abstract

A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

54 pages, 12829 KiB

Open AccessReview

Review of Flow Field Designs for Polymer Electrolyte Membrane Fuel Cells

by Yulin Wang, Xiangling Liao, Guokun Liu, Haokai Xu, Chao Guan, Huixuan Wang, Hua Li, Wei He and Yanzhou Qin

Energies 2023, 16(10), 4207; https://doi.org/10.3390/en16104207 - 19 May 2023

Cited by 4 | Viewed by 2323

Abstract

The performance of a polymer electrolyte membrane fuel cell (PEMFC) closely depends on internal reactant diffusion and liquid water removal. As one of the key components of PEMFCs, bipolar plates (BPs) provide paths for reactant diffusion and product transport. Therefore, to achieve high fuel cell performance, one key issue is designing BPs with a reasonable flow field. This paper provides a comprehensive review of various modifications of the conventional parallel flow field, interdigitated flow field, and serpentine flow field to improve fuel cells’ overall performance. The main focuses for modifications of conventional flow fields are flow field shape, length, aspect ratio, baffle, trap, auxiliary inlet, and channels, as well as channel numbers. These modifications can partly enhance reactant diffusion and product transport while maintaining an acceptable flow pressure drop. This review also covers the detailed structural description of the newly developed flow fields, including the 3D flow field, metal flow field, and bionic flow field. Moreover, the effects of these flow field designs on the internal physical quantity transport and distribution, as well as the fuel cells’ overall performance, are investigated. This review describes state-of-the-art flow field design, identifies the key research gaps, and provides references and guidance for the design of high-performance flow fields for PEMFCs in the future. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures

Figure 1

26 pages, 5743 KiB

Open AccessReview

Progress of Performance, Emission, and Technical Measures of Hydrogen Fuel Internal-Combustion Engines

by Wenzhi Gao, Zhen Fu, Yong Li, Yuhuai Li and Jiahua Zou

Energies 2022, 15(19), 7401; https://doi.org/10.3390/en15197401 - 09 Oct 2022

Cited by 10 | Viewed by 2677

Abstract

To achieve the goals of low carbon emission and carbon neutrality, some urgent challenges include the development and utilization of low-carbon or zero-carbon internal combustion engine fuels. Hydrogen, as a clean, efficient, and sustainable fuel, has the potential to meet the abovementioned challenges. Thereby, hydrogen internal combustion engines have been attracting attention because of their zero carbon emissions, high thermal efficiency, high reliability, and low cost. In this paper, the opportunities and challenges faced by hydrogen internal-combustion engines were analyzed. The progress of hydrogen internal-combustion engines on the mixture formation, combustion mode, emission reduction, knock formation mechanism, and knock suppression measures were summarized. Moreover, possible technical measures for hydrogen internal-combustion engines to achieve higher efficiency and lower emissions were suggested. Full article

(This article belongs to the Special Issue Advanced Research on Fuel Cells and Hydrogen Energy Conversion)

► Show Figures