Advances in Data-Driven and Learning Methods Applied to Battery Safety and Health
A Special Issue of Batteries (ISSN 2313-0105) belonging to the section "Battery Modelling, Simulation, Management and Application".
Deadline for manuscript submissions: 25 December 2026 | Viewed by 847
Editors
2. Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, UK
Interests: energy management; battery intelligence; artificial intelligence; autonomous driving
Interests: battery management and advanced sensing; battery safety management; fast charging optimization
Special Issue Information
Dear Colleagues,
The rapid electrification of transportation, grid-scale energy storage, portable electronics, and emerging battery-powered systems has created an urgent demand for safer, longer-lasting, and more reliable batteries. As battery systems become increasingly complex and operate under diverse real-world conditions, conventional model-based diagnostics and management strategies often face limitations in scalability, adaptability, and uncertainty handling. Recent advances in data-driven modelling, machine learning, artificial intelligence, digital twins, and physics-informed learning are opening new opportunities to improve battery safety assessment, health estimation, lifetime prediction, and intelligent management.
This Special Issue aims to present and disseminate the most recent advances in data-driven and learning methods applied to battery safety and health. We welcome original research articles, reviews, and perspectives addressing algorithms, models, datasets, sensing strategies, validation methods, and practical applications for batteries at cell, module, pack, and system levels. Contributions combining electrochemical knowledge, physical modelling, experimental data, and intelligent learning approaches are particularly encouraged.
Topics of interest for publication include, but are not limited to, the following:
- Data-driven state estimation, including SOC, SOH, SOP, and RUL prediction
- Machine learning and deep learning for battery safety diagnostics and prognostics
- Physics-informed, hybrid, and explainable AI methods for battery health assessment
- Thermal runaway warning, fault detection, and safety risk prediction
- Digital twins and cloud-based battery monitoring for real-world applications
- Battery ageing modelling, degradation mechanism identification, and lifetime prediction
- Advanced sensing, data fusion, and uncertainty quantification for battery management
- Data-efficient learning, transfer learning, and federated learning for battery systems
- Intelligent battery management systems for electric vehicles, grid storage, and portable devices
- Benchmark datasets, validation protocols, and reproducible tools for battery safety and health research
Dr. Jincheng Hu
Dr. Xiuwu Wang
Dr. Meng 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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Batteries is an international peer-reviewed open access monthly 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 2700 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
- battery safety
- battery health
- data-driven modelling
- machine learning
- artificial intelligence
- state-of-health estimation
- remaining useful life prediction
- thermal runaway
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