Special Session SS17

Edge-Intelligent Electric Drives and Physics-Informed Neural Network Identification

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Introduction:

With the rapid advancement of electrification and intelligent technologies, advanced motor drive systems are evolving from traditional control-centric architectures toward adaptive paradigms that integrate online modeling, real-time sensing, and intelligent decision-making. In key areas such as electric vehicles, aerospace, intelligent manufacturing, and renewable energy, motor systems must not only achieve high efficiency and reliability but also support online parameter updating, state reconstruction, and adaptive control under complex and extreme operating conditions. Recent progress in edge computing and physics-informed neural networks (PINNs) has provided important technological support for next-generation motor drives. Edge-intelligent algorithms implemented on DSP, FPGA, and embedded platforms enable local real-time identification and control, while physics-informed models incorporating electromagnetic, thermal, and mechanical mechanisms offer new pathways for trustworthy modeling and parameter estimation. Driven by these advances, motor drives are moving toward an “edge-intelligent and physics-informed” paradigm that tightly integrates modeling, data, and control. This special issue aims to gather recent advances in this emerging area and promote the development of high-performance, reliable, and intelligent motor drive systems.
Topics including, but not limited to:
1. Edge computing architectures for motor drives;
2. Embedded AI and TinyML for motor control ;
3. Real-time learning on DSP/FPGA platforms;
4. Hardware–software co-design for edge-intelligent drives;
5. Distributed and networked drive control ;
6. Physics-informed modeling and identification for motor systems;
7. PINN-based parameter and state estimation;
8. Multi-physics PINN modeling for electric machines;
9. Hybrid physics–data-driven modeling;
10. Trustworthy parameter and state reconstruction;
11. Learning-in-the-loop motor drive control;
12. Adaptive and self-evolving drive control;
13. Uncertainty quantification and probabilistic identification;
14. AI-assisted stability and auto-tuning control;
15. Virtual sensing and soft sensing for motor systems;
16. Intelligent fault diagnosis and predictive maintenance

SS17's Session Chair:

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Assoc. Prof. Yajie Jiang, Institute of Electrical Engineering, Chinese Academy of Sciences, China

SS17's Session Co-chairs:

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Prof. Minghao Zhou, Harbin University of Science and Technology, China

Assoc. Prof. Jinquan Zhu, Institute of Electrical Engineering, Chinese Academy of Sciences, China

Senior Engineer Wenshan Li, Institute of Electrical Engineering, Chinese Academy of Sciences, China

Lecturer Fengrui Cui, Naval University of Engineering, China

Paper Review

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1. All papers will undergo a thorough peer review process. Accepted full papers that are presented at the conference will be published in proceedings.

2. The corresponding author is responsible for ensuring that the article’s publication has been approved by all other co-authors and takes responsibility for the paper during submission and peer review.

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Call for Reviewers

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Plagiarism Policy

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The content of any submissions should be original and must not be submitted simultaneously for consideration towards publication in any other conference or journal. Reuse of material previously published by the authors is possible under the conditions that the authors fully disclose (cite) the reference and clearly highlight the innovative contribution of the IEECSC 2026 submission and the significance of this contribution.