Advanced Control and AI Methods for Future Battery Diagnostics and Prognostics
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 57
Special Issue Editors
Interests: energy systems; reinforcement learning; dynamic control; optimization
Interests: batteries; energy systems; AI for science; AI for sustainability; controls
Interests: lithium-Ion batteries; battery management; electrified vehicles
Special Issue Information
Dear Colleagues,
(1) The rapid expansion of battery applications, ranging from electric vehicles and renewable energy storage to grid stabilization, has made battery diagnostics and prognostics a critical research frontier. The accurate, efficient, and real-time evaluation of battery states—such as state of charge (SOC), state of health (SOH), and state of safety (SOS)—is essential for ensuring the reliability, safety, and sustainability of future energy systems. However, traditional model-based and data-driven approaches face significant challenges in coping with the increasing complexity, heterogeneity, and aging dynamics of modern battery systems.
Recent advances in control theory and artificial intelligence (AI) offer transformative opportunities to enhance battery management across its lifecycle. Advanced control strategies can provide adaptive and robust system optimization under uncertainty, while AI methods, including machine learning, deep learning, and physics-informed models, enable predictive insights and decision-making from complex, high-dimensional battery data. The synergy of advanced control and AI thus holds great promise for enabling next-generation battery diagnostics and prognostics.
(2) This Special Issue aims to provide an international platform for researchers and practitioners to present cutting-edge developments at the intersection of advanced control methods and AI-driven techniques for battery health assessment, lifetime prediction, anomaly detection, and operational optimization.
The scope of this Special Issue aligns with the journal’s mission to advance theoretical innovation, technological development, and practical applications in the field of energy storage and management. We particularly welcome interdisciplinary works that integrate control, AI, electrochemistry, and system engineering to address critical challenges in battery diagnostics and prognostics.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:
- Advanced control strategies (e.g., model predictive control, adaptive control, robust control) for battery management systems;
- AI-enhanced battery state estimation, SOH/SOC/SOS/RUL/degradation trajectory/anomaly prediction, and failure prognosis;
- Physics-informed machine learning and hybrid modeling approaches for batteries;
- Data generation, augmentation, and synthetic battery datasets for training AI models;
- Optimal charging/discharging strategies based on predictive diagnostics;
- Real-time implementation and embedded system development for AI-based battery monitoring;
- Diagnostics and prognostics for second-life and recycled batteries;
- Benchmarking studies and validation frameworks for battery diagnostics algorithms.
We look forward to receiving your contributions.
Dr. Junzhe Shi
Dr. Shengyu Tao
Dr. Wenxue Liu
Dr. Xingjun Li
Guest Editors
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Electronics is an international peer-reviewed open access semimonthly 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
- batteries
- controls
- AI
- modeling
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
