Artificial Intelligence for Battery Health, Safety, and Lifetime Management in Electric Vehicles
A Special Issue of Batteries (ISSN 2313-0105) belonging to the section "Energy Storage System Aging, Diagnosis and Safety".
Deadline for manuscript submissions: 25 January 2027 | Viewed by 73
Editors
Interests: artificial intelligence; lithium-ion batteries; electric vehicle batteries; battery health prognostics; intelligent battery management
Interests: power batteries; battery energy storage systems; distributed parameter systems; intelligent battery management
Special Issues, Collections and Topics in MDPI journals
Interests: metal batteries; anode materials; artificial intelligence; anode-free batteries; artificial soild electrolyte interphase; battery health prognostics; intelligent battery management
Special Issue Information
Dear Colleagues,
The rapid expansion of battery-powered transportation, grid-scale energy storage, and portable electronics has placed increasing demands on reliable health assessment, accurate lifetime prediction, and effective safety management of battery systems. Battery degradation is governed by complex electrochemical, thermal, and mechanical processes, while practical applications involve diverse cycling protocols, dynamic operating conditions, temperature variations, measurement noise, and limited access to reliable health labels. These challenges call for advanced artificial intelligence methods capable of extracting health-related information, modeling degradation trajectories, predicting battery lifetime, and supporting safer and more reliable battery operation.
This Special Issue aims to present and disseminate recent advances in artificial intelligence for battery health prognostics, safety, and management. We welcome original research articles, reviews, and technical communications addressing machine learning, deep learning, physics-informed modeling, hybrid modeling, interpretable learning, and uncertainty-aware methods for battery state estimation, lifetime prediction, ageing diagnosis, fault diagnosis, thermal safety monitoring, digital twins, and intelligent battery management systems. Contributions demonstrating methodological innovation, cross-condition generalization, real-world applicability, or practical deployment in battery systems are particularly encouraged.
Topics of interest for publication include, but are not limited to, the following:
- Artificial intelligence for battery health prognostics and management;
- State-of-health estimation and remaining-useful-life prediction;
- Early-stage lifetime prediction and health indicator extraction;
- Battery ageing diagnosis and degradation modeling;
- Physics-informed and hybrid AI for battery prognostics;
- Transfer learning and domain adaptation for battery applications;
- Battery health assessment using real-world operation data;
- Battery fault diagnosis and anomaly detection;
- Thermal safety monitoring and thermal runaway early warning;
- Digital twins and AI-enabled battery management systems.
- AI-driven screening of battery materials
Dr. Zicheng Fei
Dr. Peng Wei
Dr. Qicheng Zhang
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
- artificial intelligence
- battery health prognostics
- state-of-health estimation
- remaining useful life prediction
- degradation modeling
- deep learning
- transfer learning
- real-world operation data
- battery fault diagnosis
- thermal runaway early warning
- battery management systems
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