10th Anniversary of Batteries: Battery Diagnostics and Prognostics
A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Performance, Ageing, Reliability and Safety".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 71
Special Issue Editors
Interests: battery modeling; AI-based battery states estimation; battery health assessment and lifetime prediction; feature engineering and machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: electric vehicles; lithium-ion batteries; smart battery management; renewable energy; energy storage
Special Issues, Collections and Topics in MDPI journals
Interests: optimal control of alternative-energy vehicles for sustainable transportation; management and control of energy storage systems; connected HEVs and PHEVs; vehicle–traffic–grid–home nexus; battery management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid evolution of electric vehicles, renewable energy storage, and portable electronics, ensuring the reliability, safety, and lifetime of battery systems has become a critical challenge. Celebrating the 10th anniversary of Batteries, this Special Issue focuses on advancements in battery diagnostics and prognostics, covering multi-physics modeling, real-time diagnostics, and predictive analytics to enable next-generation battery management.
Key topics include electrochemical–thermal–mechanical coupling for accurate state estimation, degradation-aware digital twins, and AI-driven battery health assessment. Furthermore, stochastic modeling and uncertainty quantification play a crucial role in prognostics of remaining useful life, while embedded sensing and electrochemical impedance spectroscopy (EIS) provide real-time insights into battery health. This collection also explores cloud-integrated and edge computing architectures for predictive battery management systems (BMSs), driving the transition towards intelligent battery technologies. By bridging fundamental research and applied innovations, this Special Issue aims to accelerate the development of more reliable and efficient energy storage solutions.
Dr. Xin Sui
Prof. Dr. Remus Teodorescu
Prof. Dr. Xiaosong Hu
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. 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
- electrochemical–thermal–mechanical coupling for battery state estimation
- multi-physics modeling and degradation-aware digital twins
- embedded sensing and EIS for real-time diagnostics
- AI-driven battery health assessment: interpretable and physics-informed approaches
- stochastic modeling and uncertainty quantification in battery prognostics
- cloud-integrated and edge computing architectures for predictive BMS
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