Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.0
2.8
Journals
JMSE
Special Issues
Advances in Marine Electric Propulsion: Technologies, Systems Integration and Sustainable...
share announcement

Advances in Marine Electric Propulsion: Technologies, Systems Integration and Sustainable Maritime Transportation

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: closed (25 February 2026) | Viewed by 1581

Special Issue Editor

Prof. Dr. Mohamad Issa

E-Mail Website1 Website2
Guest Editor
Department of Applied Sciences, Maritime Institute of Quebec, Rimouski, QC, Canada
Interests: marine electric propulsion; hydrogen fuel cells; autonomous vessel systems; AI-driven energy management; renewable energies; electrical engineering

Special Issue Information

Dear Colleagues,

The maritime sector faces unprecedented pressure to reduce greenhouse gas emissions and achieve carbon neutrality by 2050. Marine electric propulsion systems offer enormous potential for environmentally friendly maritime transportation and have emerged as a transformative technology. Advances in battery technology, electric motors, and power management systems have accelerated the adoption of electric propulsion, moving it from specialized applications to widespread use across a variety of vessel types.

The goal of this Special Issue is to highlight state-of-the-art studies on systems integration methodologies, marine electric propulsion technologies, and their application in sustainable maritime transportation. Topics of interest include, but are not limited to: electric motors, power electronics systems, energy storage systems, hybrid propulsion architectures, power management techniques, fuel cells, and electric autonomous vessel systems. We welcome original research articles, comprehensive reviews, and case studies that advance knowledge in these areas. Contributions addressing innovative strategies for energy efficiency, the integration of renewable energy, AI-driven optimization, and practical implementation issues in marine contexts will be given special consideration.

Prof. Dr. Mohamad Issa
Guest Editor

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

  • marine electric propulsion
  • maritime electrification
  • hybrid propulsion
  • energy storage
  • power management
  • sustainable shipping
  • electric motors
  • battery systems
  • fuel cells
  • green maritime

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 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

25 pages, 7740 KB  
Article
Deep Reinforcement Learning-Based Resilient Restoration of Ship Cyber–Physical Systems
by Yahui Liu, Shuli Wen, Qiang Zhao, Bing Zhang and Zhangchao Lu
J. Mar. Sci. Eng. 2026, 14(9), 765; https://doi.org/10.3390/jmse14090765 - 22 Apr 2026
Viewed by 268
Abstract
The rapid development of cyber–physical technologies has led to enhanced observability and controllability of shipboard power systems. However, the reliance of shipboard power systems on information networks undermines the traditional security provided by physical isolation; under malicious attacks, faults in the information domain [...] Read more.
The rapid development of cyber–physical technologies has led to enhanced observability and controllability of shipboard power systems. However, the reliance of shipboard power systems on information networks undermines the traditional security provided by physical isolation; under malicious attacks, faults in the information domain can propagate rapidly, causing physical power outages and reducing the resilience of shipboard power systems. To address this issue, this paper investigates the cascading failure reconstruction and resilience enhancement in shipboard cyber–physical systems (SCPSs) under uncertain network attacks. First, a cascading failure propagation model is established to capture the interaction between attack paths and system vulnerabilities, revealing how cyberattacks spread through communication links and infiltrate the power topology. Then, a reinforcement learning-based load recovery strategy is developed, in which a masked proximal policy optimization (masked-PPO) algorithm is employed to optimize reconfiguration decisions under operational constraints. The proposed approach enables adaptive and efficient recovery actions in complex cross-domain environments. Case studies based on representative SCPS scenarios demonstrate that the proposed method improves cascading-failure reconfiguration capability by 13.21% and reduces the average decision time by 18.6%, validating its effectiveness, real-time performance, and scalability. Full article
(This article belongs to the Special Issue Advances in Marine Electric Propulsion: Technologies, Systems Integration and Sustainable Maritime Transportation)
►▼ Show Figures

Figure 1

19 pages, 3953 KB  
Article
Intelligent Diagnosis of Ship Propulsion Motor Bearings Based on Dynamic Class Weights
by Guohua Yan, Xiaoding Wang, Kai Liu, Jingran Kang and Xinhua Yi
J. Mar. Sci. Eng. 2025, 13(11), 2204; https://doi.org/10.3390/jmse13112204 - 19 Nov 2025
Viewed by 540
Abstract
As an important part of the ship’s power system, the bearing operation status of the propulsion motor is directly related to the reliability and safety of the whole system. However, in the field of marine propulsion motor bearing fault diagnosis, the data imbalance [...] Read more.
As an important part of the ship’s power system, the bearing operation status of the propulsion motor is directly related to the reliability and safety of the whole system. However, in the field of marine propulsion motor bearing fault diagnosis, the data imbalance problem seriously affects the performance of the fault detection model. Due to the scarcity of fault data relative to normal operation data, traditional diagnostic methods are ineffective in dealing with unbalanced data. To solve this problem, a dynamic class weighting solution is proposed. The dynamic class weighting method introduces the weight coefficient λ on the basis of the traditional class weighting, which can adjust the class weight value in real time according to the training situation, and comprehensively considers the data distribution and the training situation to ensure that the model can learn better even in the case of insufficient data. Testing on the imbalanced distribution of bearing natural-failure data shows that the proposed method achieves a 5.25% improvement in diagnostic accuracy compared to direct training. Compared with traditional class-weighted approaches, diagnostic accuracy is enhanced by 3.56%, effectively mitigating the impact of scarce and unevenly distributed failure data on model training. Full article
(This article belongs to the Special Issue Advances in Marine Electric Propulsion: Technologies, Systems Integration and Sustainable Maritime Transportation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop