AI-Assisted Control and Monitoring of Power Electronics in Transportation Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".
Deadline for manuscript submissions: 15 March 2026 | Viewed by 198
Special Issue Editors
Interests: artificial intelligence; control; health monitoring; BESS; RES
Interests: power electronics; smart grid; renewable energy; nonlinear dynamics; memristive systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The electrification of transportation systems—ranging from electric vehicles (EVs) and railways to aerospace and marine platforms—has led to growing reliance on sophisticated power electronics for energy conversion, propulsion, and auxiliary systems. With increasing system complexity and demand for high efficiency, reliability, and adaptability, traditional control and monitoring methods are being redefined through artificial intelligence (AI) and machine learning (ML).
Recent advancements in AI algorithms, sensor technologies, and embedded computation have enabled real-time data-driven control, predictive maintenance, and intelligent fault management in power electronic systems. These capabilities are crucial in transportation environments where safety, resilience, and energy optimization are paramount.
This Special Issue of Electronics aims to bring together cutting-edge research and practical insights into how AI and ML techniques are enhancing the control, diagnostics, and operational intelligence of power electronics in modern transportation systems. We welcome submissions that present original research or comprehensive reviews in this interdisciplinary field. Topics of interest include but are not limited to the following:
- AI-assisted control strategies for onboard power converters;
- Real-time adaptive control using reinforcement learning or neural networks;
- Data-driven fault detection and self-healing control schemes;
- Predictive diagnostics and remaining useful life estimation of converter components;
- Physics-informed machine learning for the modeling and control of vehicular power electronics;
- Online parameter identification and model updating for adaptive converter control;
- Intelligent energy management strategies for electric propulsion systems;
- Digital twin architectures for real-time monitoring and control;
- Federated learning and distributed AI for fleet-level monitoring;
- Sensor fusion and AI-based signal processing for condition assessment;
- AI-enhanced control under uncertain or degraded conditions;
- Lightweight AI implementations for embedded systems in transportation platforms;
- Cyber-physical security and anomaly detection in vehicular power systems;
- AI-enabled strategies for EMI/EMC compliance and thermal stress mitigation.
We invite researchers and industry professionals to contribute their innovative solutions, theoretical developments, and application-driven studies to this Special Issue, which aims to advance the next generation of intelligent, robust, and efficient power electronic systems for transportation.
Dr. Tianhao Qie
Prof. Dr. Herbert Ho-Ching Iu
Dr. Yuji Zeng
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence-assisted control
- data-driven fault detection
- onboard power converter
- physics-informed machine learning
- transportation system
- energy management
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