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Research on Intelligent Operation and Maintenance, Intelligent Manufacturing and Energy Management in the New Energy Equipment Industry

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

Deadline for manuscript submissions: 10 March 2026 | Viewed by 341

Special Issue Editors


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Guest Editor
Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, School of Mechanical Engineering, Guangxi University, Nanning 530004, China
Interests: equipment transportation; intelligent operations and maintenance; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

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Guest Editor
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
Interests: rotating machinery; nonlinear dynamics; condition monitoring; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

With the growing global focus on environmental sustainability, the new energy equipment industry plays a crucial role in addressing traditional issues of energy consumption and resource depletion. New energy equipment primarily includes electric vehicles, wind power generation equipment, solar photovoltaic systems, energy storage systems, and other related equipment. The research, development, and application of these technologies are not only vital for driving national economic growth, enhancing people’s quality of life and safety, but also play a critical role in promoting environmental protection and combating climate change. With continuous advancements in new energy technologies, tackling key technological challenges in areas such as intelligent operation and maintenance (O&M) technologies, smart manufacturing and production techniques, and energy management has become a current research hotspot. The application of intelligent O&M technologies involves the in-depth analysis and optimization of real-time equipment status data through data mining, machine learning, and big data analytics, aiming to enhance operational efficiency, reliability, and environmental sustainability. Simultaneously, innovations in smart manufacturing and production technologies drive automation and lean production processes. Energy management technologies optimize resource utilization through efficient battery management systems and intelligent charging solutions, further elevating the overall performance of new energy equipment. This Special Issue aims to introduce and disseminate cutting-edge research in the field of new energy equipment, covering multiple technical directions, including intelligent operation and maintenance, smart manufacturing, and energy management. Topics of interest include, but are not limited to:

  • New energy equipment and technology;
  • Vehicle-to-everything technology and intelligent transportation systems;
  • Electric motor and energy storage equipment engineering;
  • Energy-saving optimization control;
  • Monitoring and control systems;
  • Batteries, fuel cells, and reliability analysis;
  • Battery management systems;
  • Automated and lean production of components;
  • Deep learning;
  • Power electronics technology.

Dr. Zhenzhen Jin
Prof. Dr. Deqiang He
Prof. Dr. Dechen Yao
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

  • intelligent operation and maintenance
  • fault diagnosis
  • new energy vehicle
  • intelligent transportation system
  • prediction, detection, and optimization
  • emerging technologies
  • internet of vehicles
  • health status monitoring
  • intelligent perception

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Related Special Issue

Published Papers (1 paper)

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Review

45 pages, 13450 KB  
Review
System Integration to Intelligent Control: State of the Art and Future Trends of Electric Vehicle Regenerative Braking Systems
by Bin Huang, Wenbin Yu, Zhuang Wu, Ansheng Yang and Jinyu Wei
Energies 2025, 18(19), 5109; https://doi.org/10.3390/en18195109 - 25 Sep 2025
Abstract
With the rapid development of the electric vehicle (EV) industry, the regenerative braking system (RBS) has become a pivotal technology for enhancing overall vehicle energy efficiency and safety. This article systematically reviews recent research advances, spanning macro-architecture, drive and energy-storage hardware, control strategies, [...] Read more.
With the rapid development of the electric vehicle (EV) industry, the regenerative braking system (RBS) has become a pivotal technology for enhancing overall vehicle energy efficiency and safety. This article systematically reviews recent research advances, spanning macro-architecture, drive and energy-storage hardware, control strategies, and evaluation frameworks. It focuses on comparing the mechanisms and performance of six categories of intelligent control algorithms—fuzzy logic, neural networks, model predictive control, sliding-mode control, adaptive control, and learning-based algorithms—and, leveraging the structural advantages of four-wheel independent drive (4WID) electric vehicles, quantitatively analyzes improvements in energy-recovery efficiency and coordinated vehicle-dynamics control. The review further discusses how high-power-density motors, hybrid energy storage, brake-by-wire systems, and vehicle-road cooperation are pushing the upper limits of RBS performance, while revealing current technical bottlenecks in high-power recovery at low speeds, battery thermal safety, high-dimensional real-time optimization, and unified evaluation standards. A closed-loop evolutionary roadmap is proposed, consisting of the following stages: system integration, intelligent control, scenario prediction, hardware upgrading, and standard evaluation. This roadmap emphasizes the central roles of deep reinforcement learning, hierarchical model predictive control (MPC), and predictive energy management in the development of next-generation RBS. This review provides a comprehensive and forward-looking reference framework, aiming to accelerate the deployment of efficient, safe, and intelligent regenerative braking technologies. Full article
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