Battery Health Management for Cyber-Physical Energy Storage Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 384
Special Issue Editors
Interests: IoT; IIOT; AI applications; smart chargers; power applications
Interests: digital power; power electronics; battery management systems; renewable energy; machine/deep learning application
Special Issue Information
Dear Colleagues,
With the rapid evolution of electric vehicles, distributed renewable energy, and decentralized energy systems, there is a growing demand for intelligent solutions that enhance stability, efficiency, and resilience. This Special Issue seeks high-quality submissions focused on the modeling, control, and real-world application of intelligent energy and predictive systems for sustainable technological advancement.
A key topic is the accurate estimation of a battery’s state of charge (SOC) and state of health (SOH), essential for ensuring safety and extending battery lifespan. Contributions are invited on fault diagnosis, battery aging modeling, and electrochemical analysis techniques, such as electrochemical impedance spectroscopy (EIS), which enable real-time diagnostics and predictive maintenance.
We also welcome innovative works on battery management system (BMS) architecture and implementation, including cell balancing, system-level optimization, and integration into hybrid or renewable-based power platforms. Of growing importance are smart chargers and wireless charging systems, which support dynamic power control, adaptive charging protocols, and user- or grid-oriented strategies. These smart charging solutions play a vital role in enabling safe, rapid, and efficient charging for both stationary energy storage systems and electric mobility platforms while also enhancing interoperability in IoT-connected infrastructures.
Moreover, contributions on renewable energy systems and photovoltaic (PV) integration, especially under fluctuating environmental conditions and hybrid configurations, are encouraged.
In parallel, the rise in artificial intelligence (AI), machine learning (ML), and deep learning (DL) opens up new possibilities in energy system optimization, health monitoring, and environmental forecasting. Submissions that explore predictive models and algorithms applied to energy control, healthcare diagnostics, and smart environmental systems are particularly relevant. Studies that integrate computer science, image processing, or intelligent sensors into energy or health-focused applications are also highly valued.
- SOC and SOH estimation.
- Fault diagnosis and battery aging modeling.
- BMS architecture and implementation.
- Charging algorithms and wireless charger and smart charger development.
- Renewable energy systems and PV integration.
- Applications of AI, machine learning, and deep learning in energy, health, and environmental prediction.
- Computer science and engineering and image processing.
- We welcome contributions that bridge theory and real-world application for sustainable and human-centered technological development.
Prof. Dr. Chung-Wen Hung
Dr. Chun-Liang Liu
Dr. Guan-Jhu Chen
Guest Editors
Manuscript Submission Information
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Keywords
- optimization
- machine learning
- deep learning
- battery management systems (BMS)
- electrochemical impedance spectroscopy (EIS)
- AC impedance
- state of charge (SOC)
- state of health (SOH)
- charging methods
- artificial intelligence
- computer science and engineering
- wireless charger
- smart charger
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