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Machines
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Smart Electrified Energy–Motion Systems: Design, Control, and Intelligence from Machines...
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Smart Electrified Energy–Motion Systems: Design, Control, and Intelligence from Machines and Vehicles to the Grid

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1219

Special Issue Editor

Prof. Dr. Chao Gong

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710021, China
Interests: motor control; robot motor; fault-tolerant control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, which focuses on the intelligent design, control, and system-level techniques of electrified energy–motion systems. Today, machines, transportation, manufacturing, and energy infrastructures are increasingly interconnected. This coupling introduces new challenges in the design and operation of individual components, distributed subsystems, and large-scale integrated systems, while also creating opportunities for data-driven and artificial intelligence–based methodologies. Addressing these challenges is critical for improving the efficiency, reliability, and flexibility of next-generation electrified systems.

This Special Issue aims to provide a focused forum for research on electric energy conversion systems that integrate advanced design, control, intelligence, and grid-interactive operation. The subject is well aligned with the scope of Machines, which covers energy–motion system design, modeling, control, and applications, particularly in electromechanical and energy conversion systems. By emphasizing system-level integration and intelligent methodologies, the Special Issue bridges traditional machine research with emerging grid-aware and data-driven technologies, while maintaining a clear focus on machine-centered engineering problems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advanced design and optimization of electric machines;
  • High-performance motor control and drive systems;
  • Multi-physics modeling and reliability-oriented machine design;
  • Artificial intelligence and data-driven methods for machine design and control;
  • Electric drive systems for vehicles and mobile platforms;
  • Intelligent control and energy management of electric vehicles;
  • Intelligent analysis and generation of power system schematics;
  • Data-driven modeling and planning of grid infrastructure supporting electrified systems;
  • Grid-interactive electric drives and machine-based energy management;
  • Vehicle-to-Grid (V2G) and bidirectional charging technologies involving electric machines;
  • Design and control of machines, vehicles, and power grid infrastructures;
  • System-level optimization and intelligence for coupled energy-motion systems.

We look forward to receiving your contributions.

Prof. Dr. Chao Gong
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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • electrified energy–motion systems
  • design
  • control
  • system-level
  • intelligent methodologies

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Published Papers (2 papers)

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Review

42 pages, 3554 KB  
Review
Towards Reliable Power Grid Modeling from Drawings: A Review of Intelligent Understanding, Topology Inference, and Model Generation
by Congying Wu, Haozheng Yu, Yu Liu and Chao Gong
Machines 2026, 14(4), 371; https://doi.org/10.3390/machines14040371 - 27 Mar 2026
Viewed by 466
Abstract
This paper presents a comprehensive review of the intelligent understanding of power grid drawings, with the aim of enabling reliable and executable grid modeling. First, a unified pipeline is established to describe the transformation from drawings to grid models, covering visual understanding, topology [...] Read more.
This paper presents a comprehensive review of the intelligent understanding of power grid drawings, with the aim of enabling reliable and executable grid modeling. First, a unified pipeline is established to describe the transformation from drawings to grid models, covering visual understanding, topology inference, and consistency validation. Second, existing methods are systematically analyzed within this framework, where visual understanding extracts components and textual information and topology inference reconstructs electrical connectivity and network structure. Third, model generation methods are investigated as a critical yet underexplored component, focusing on topology correctness and physical constraint verification. Compared with existing review studies that primarily focus on perception-level tasks such as detection and recognition, this paper explicitly emphasizes the reliability of the resulting models. It highlights that errors in connectivity inference and the lack of validation mechanisms significantly limit practical deployment. Key challenges, including connectivity ambiguity, error propagation, and the absence of standardized validation frameworks, are analyzed. Furthermore, emerging directions such as topology-aware learning and physics-constrained validation are discussed. This review provides a structured perspective on transforming power grid drawings into reliable models and offers insights for future research into power system digitalization. Full article
(This article belongs to the Special Issue Smart Electrified Energy–Motion Systems: Design, Control, and Intelligence from Machines and Vehicles to the Grid)
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30 pages, 3865 KB  
Review
Advanced Temperature Prediction for Electric Motors: A Review from Physical Foundations to Physics-Informed Intelligence
by Yaofei Han, Qian Zhang, Yongfeng Liu, Shaofeng Chen, Zhixun Ma, Yawei Li and Jianping Sun
Machines 2026, 14(3), 305; https://doi.org/10.3390/machines14030305 - 7 Mar 2026
Viewed by 482
Abstract
Motor temperature prediction is critical for ensuring the reliability and safe operation of high-power-density electric drives. Since direct measurement of internal temperatures, especially rotor and magnet temperatures, is often impractical, virtual sensing has become an important research direction. This review provides a structured [...] Read more.
Motor temperature prediction is critical for ensuring the reliability and safe operation of high-power-density electric drives. Since direct measurement of internal temperatures, especially rotor and magnet temperatures, is often impractical, virtual sensing has become an important research direction. This review provides a structured clarification of motor temperature prediction technologies. First, the physical foundations of motor thermal behavior are revisited, emphasizing multi-source loss generation, electro-thermal coupling mechanisms, and the dominant influence of time-varying boundary conditions. Second, existing estimation methodologies are systematically categorized into physics-based thermal models, observer- and identification-based approaches, and data-driven machine learning frameworks. Their mathematical principles, information bottlenecks, computational trade-offs, and deployment constraints are comparatively analyzed. Particular attention is given to hybrid and physics-informed methods, where reduced-order thermal networks, parameter adaptation, and learning-based residual correction are integrated to enhance robustness. Future developments should focus on uncertainty-aware estimation, lifecycle-adaptive modeling, and reliable temperature field inference under sparse sensing conditions. Full article
(This article belongs to the Special Issue Smart Electrified Energy–Motion Systems: Design, Control, and Intelligence from Machines and Vehicles to the Grid)
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