Artificial Intelligence for Motor Drive Systems and Its Applications
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 1895
Special Issue Editors
Interests: design, modeling, and optimization of motor topologies; machine-learning-based control strategies for motor drives; application of motor drives in robots and electrified transportation; smart grid with renewable energy
Special Issues, Collections and Topics in MDPI journals
Interests: electric vehicle technologies; renewable energy systems; machines and drives; power electronics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the advances of artificial intelligence (AI) and related semiconductor technology, “AI for Science” has gradually become a new paradigm for cutting-edge scientific research. However, in the domain of a traditional discipline, motor drive systems, scholars have not yet constructed a mature theoretical system where AI can be feasibly applied. Several crucial problems urgently need to be solved to promote the cross-disciplinary integration of AI and motor drive systems: (1) Explainable AI (XAI) during the optimization and control processes of motor drive; (2) Stability and reliability of AI-assisted motor control; (3) Hardware's computational burden reduction for AI-assisted algorithm implementation.
This Special Issue welcomes manuscript submissions related to the integration of AI with motor drive systems, from electric machine material determination, topology design, structural parameter optimization, and motor control to fault diagnosis. Topics of interest for publication include, but are not limited to, the following:
- Neural networks for optimal material composition determination of motors;
- Physics-informed neutral networks for multi-physics analysis in motor drives;
- Acceleration techniques for heuristic optimization algorithms in motor design;
- Surrogate models for the motor’s structural parameter optimization;
- Neural networks and Gaussian processes for motor control;
- Computationally efficient AI-assisted algorithms for motor control;
- Identification and compensation of nonlinear characteristics in motor drives based on AI algorithms;
- AI-based motor condition monitoring algorithms and AI-assisted fault-tolerant control;
- Intelligent sensor fusion and cooperative control in multi-motor systems;
- AI-assisted motor drives for electric vehicle and robot applications.
Dr. Hang Zhao
Prof. Dr. K. T. Chau
Guest Editors
Manuscript Submission Information
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Keywords
- electric machine
- motor control
- condition monitoring
- sensor fusion
- neural network
- gaussian process
- reinforcement learning
- surrogate model
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