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Advanced Technologies for New (Clean) Energy Ships—2nd Edition
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Advanced Technologies for New (Clean) Energy Ships—2nd Edition

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 25 May 2026 | Viewed by 8142

Special Issue Editors

Dr. Qiuwan Shen

E-Mail Website
Guest Editor
Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Interests: hydrogen energy; new energy technology; PEMFC; lithium-ion; PEMEC; energy materials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. He Miao

E-Mail Website
Guest Editor
Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Interests: perovskite-type oxides; new energy technology; hydrogen production
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, in order to meet the increasingly strict greenhouse gas emission requirements of the International Maritime Organization, major countries around the world have begun to attach importance to the development of green (clean) ships and actively promote the application of new energy in the field of ships. The ability to achieve the green transformation of ships lies with the application of green power technology, which is divided into two types: low-carbon and zero-carbon power. This can be achieved with different fuels, including LNG, liquid ammonia, methanol, hydrogen power, and lithium-ion-based electric drive.

We invite original research, reviews, and perspectives involving experimental/simulation investigations, recent developments, and future directions in the field of advanced technologies for new (clean) energy ship applications.

Dr. Qiuwan Shen
Prof. Dr. He Miao
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 250 words) can be sent to the Editorial Office for assessment.

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. Journal of Marine Science and Engineering 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

  • new energy ship
  • clean energy ship
  • hydrogen power
  • fuel cell power/PEM fuel cell/SOFC
  • LNG
  • ammonia-powered ships
  • lithium-ion power
  • hybrid power
  • methanol-powered ships
  • maritime decarbonization
  • energy efficiency and optimization
  • energy savings and emission reductions
  • exhaust emission reductions
  • carbon capture technologies

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Related Special Issue

Published Papers (4 papers)

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Research

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58 pages, 15734 KB  
Article
Study on Combustion Characteristics of Compression Ignition Marine Methanol/Diesel Dual-Fuel Engine
by Zhongcheng Wang, Jie Zhu, Xiaoyu Liu, Jingjun Zhong and Xin Jiang
J. Mar. Sci. Eng. 2025, 13(11), 2213; https://doi.org/10.3390/jmse13112213 - 20 Nov 2025
Cited by 2 | Viewed by 1353
Abstract
With the increasing global demand for environmental protection and sustainable energy utilization, methanol, as a clean and renewable fuel, has become a research focus in the field of marine engines. However, its application in compression ignition engines faces bottlenecks such as low combustion [...] Read more.
With the increasing global demand for environmental protection and sustainable energy utilization, methanol, as a clean and renewable fuel, has become a research focus in the field of marine engines. However, its application in compression ignition engines faces bottlenecks such as low combustion efficiency and poor stability. Taking the L23/30H marine diesel engine as the research object, this paper establishes a combustion simulation model for a methanol/diesel dual-fuel direct-injection engine. The reliability of the model is ensured through grid independence verification and model calibration, and a coupled chemical reaction kinetic mechanism containing 126 species and 711 elementary reactions is constructed. A systematic study is conducted on the effects of injection strategies, including fuel operating modes, spray development patterns, injection intervals, and injection timing, on combustion characteristics. The results show that under the optimized injection strategy (vertical cross spray + synchronous injection) proposed in this study and operating conditions with a high methanol substitution ratio, the combustion efficiency, dynamic performance, and soot emission control effect of the dual-fuel mode are superior to those of the pure diesel mode. Simulation results show that the combined strategy of vertical cross injection and synchronous injection can significantly increase the indicated thermal efficiency (ITE) by 3.2%, reduce the brake specific fuel consumption (BSFC) by approximately 4.5%, advance the peak heat release by 2 °CA, and remarkably improve the combustion efficiency, while earlier injection timing is beneficial to air–fuel mixing. Further comparison of combustion and emission characteristics under different boundary conditions such as methanol energy ratios and injection pressures reveals that increasing methanol injection pressure, compression ratio, and initial pressure can improve combustion uniformity and reduce soot emissions, but NOx emissions increase, which requires the coordination of after-treatment technologies. Through the comprehensive optimization of multiple parameters, efficient and clean combustion under a high methanol substitution rate is achieved. This paper provides theoretical support and practical guidance for the technological development of marine methanol dual-fuel engines. In the future, industrial applications can be promoted by combining actual engine tests and after-treatment technologies. Full article
(This article belongs to the Special Issue Advanced Technologies for New (Clean) Energy Ships—2nd Edition)
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22 pages, 7199 KB  
Article
Deep Reinforcement Learning-Based Energy Management Strategy for Green Ships Considering Photovoltaic Uncertainty
by Yunxiang Zhao, Shuli Wen, Qiang Zhao, Bing Zhang and Yuqing Huang
J. Mar. Sci. Eng. 2025, 13(3), 565; https://doi.org/10.3390/jmse13030565 - 14 Mar 2025
Cited by 7 | Viewed by 2804
Abstract
Owing to the global concern regarding fossil energy consumption and carbon emissions, the power supply for traditional diesel-driven ships is being replaced by low-carbon power sources, which include hydrogen energy generation and photovoltaic (PV) power generation. However, the uncertainty of shipboard PV power [...] Read more.
Owing to the global concern regarding fossil energy consumption and carbon emissions, the power supply for traditional diesel-driven ships is being replaced by low-carbon power sources, which include hydrogen energy generation and photovoltaic (PV) power generation. However, the uncertainty of shipboard PV power generation due to weather changes and ship motion variations has become an essential factor restricting the energy management of all-electric ships. In this paper, a deep reinforcement learning-based optimization algorithm is proposed for a green ship energy management system (EMS) coupled with hydrogen fuel cells (HFCs), lithium batteries, PV generation, an electric power propulsion system, and service loads. The focus of this study is reducing the total operation cost and improving energy efficiency by jointly optimizing power generation and voyage scheduling, considering shipboard PV uncertainty. To verify the effectiveness of the proposed method, real-world data for a hybrid hydrogen- and PV-driven ship are selected for conducting case studies under various sailing conditions. The numerical results demonstrate that, compared to those obtained with the Double DQN algorithm, the PPO algorithm, and the DDPG algorithm without considering the PV system, the proposed DDPG algorithm reduces the total economic cost by 1.36%, 0.96%, and 4.42%, while effectively allocating power between the hydrogen fuel cell and the lithium battery and considering the uncertainty of on-board PV generation. The proposed approach can reduce energy waste and enhance economic benefits, sustainability, and green energy utilization while satisfying the energy demand for all-electric ships. Full article
(This article belongs to the Special Issue Advanced Technologies for New (Clean) Energy Ships—2nd Edition)
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17 pages, 13729 KB  
Article
Effect of Flow Field with Baffles on Performance of High Temperature Proton Exchange Membrane Fuel Cells
by Shian Li, Shuqian Zhang and Qiuwan Shen
J. Mar. Sci. Eng. 2025, 13(3), 456; https://doi.org/10.3390/jmse13030456 - 27 Feb 2025
Cited by 2 | Viewed by 1317
Abstract
With the implementation of strict emission regulations, new energy technologies are widely used in the field of maritime transportation. Fuel cells can be used as the power sources of ships due to the advantages of high efficiency, low noise and zero emissions. In [...] Read more.
With the implementation of strict emission regulations, new energy technologies are widely used in the field of maritime transportation. Fuel cells can be used as the power sources of ships due to the advantages of high efficiency, low noise and zero emissions. In this study, a three-dimensional non-isothermal numerical model of a high temperature proton exchange membrane fuel cell (HT-PEMFC) is established and used to investigate the effect of a flow field with baffles on cell performance. The effects of the number, height and length of baffles in the flow field on the species concentration distribution, current density and power density are comprehensively studied. Compared with the traditional straight channel, the baffles in the channel can effectively improve cell performance. When the number of baffles is nine, the height of the baffles is 0.75 mm and the length of the baffles is 1 mm, the current density is increased from 1.390 A/cm2 to 1.524 A/cm2 at a voltage of 0.4 V, which is an increase of 9.64%. This study can provide guidelines for flow channel design. Full article
(This article belongs to the Special Issue Advanced Technologies for New (Clean) Energy Ships—2nd Edition)
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Review

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36 pages, 2483 KB  
Review
Machine Learning Applications in Fuel Reforming for Hydrogen Production in Marine Propulsion Systems
by Yexin Chen, Xinyu Liu, Xu Liu, Hao Lu and Ziqin Wang
J. Mar. Sci. Eng. 2026, 14(1), 85; https://doi.org/10.3390/jmse14010085 - 31 Dec 2025
Viewed by 1595
Abstract
In the context of the shipping industry’s transition towards low-carbon solutions, hydrogen energy exhibits substantial application potential in marine propulsion systems. Fuel reforming for hydrogen production represents one of the key technologies for efficient hydrogen production in maritime applications. Nevertheless, this process involves [...] Read more.
In the context of the shipping industry’s transition towards low-carbon solutions, hydrogen energy exhibits substantial application potential in marine propulsion systems. Fuel reforming for hydrogen production represents one of the key technologies for efficient hydrogen production in maritime applications. Nevertheless, this process involves complex multi-scale reaction mechanisms, challenges in catalyst design, and difficulties in system optimization. This paper conducts a comprehensive review of the recent progress in the application of machine learning in fuel reforming hydrogen production technology. In the realm of catalysts, machine learning has expedited the design of efficient catalysts via high-throughput screening, performance prediction, and active site regulation. In reaction modeling, machine learning has facilitated the development of multi-scale kinetic models, enhancing the interpretability and predictive accuracy of reaction pathways. Regarding equipment and system optimization, machine learning has enabled innovations in reactor design, collaborative optimization of process parameters, and intelligent system control. This review aims to provide theoretical foundations and practical guidance for the technological development of ship propulsion systems. Moreover, it explores the future directions for the deep integration of machine learning and hydrogen energy technologies, thereby promoting the low-carbon and intelligent transformation of the shipping industry. Full article
(This article belongs to the Special Issue Advanced Technologies for New (Clean) Energy Ships—2nd Edition)
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