AI-Powered Battery Management and Grid Integration for Smart Cities
A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".
Deadline for manuscript submissions: 27 February 2026 | Viewed by 93
Special Issue Editors
Interests: electric vehicles; energy management; batteries; model predictive control; deep learning methods; digital twins
Special Issue Information
Dear Colleagues,
The electrification of mobility, the rise of renewable energy integration, and the evolution of smart city infrastructures are driving new demands for intelligent battery management and grid interaction. Central to this transformation is the deployment of AI-powered approaches to optimise battery State of Health (SOH), Remaining Useful Life (RUL), and operational efficiency across electric vehicle (EV) fleets and distributed energy storage systems. This Special Issue invites original research and comprehensive reviews on advanced machine learning (ML), deep learning (DL), and digital twin methodologies that enhance battery diagnostics, predictive maintenance, and grid-aware optimisation. Particular attention is given to solutions that enable seamless Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) integration, improve grid resilience, and support sustainable energy management in urban environments. Contributions demonstrating real-world applications, interdisciplinary innovations, and explainable AI frameworks for battery systems are highly encouraged. The aim of this Special Issue is to advance knowledge at the intersection of battery intelligence, grid dynamics, and smart city mobility, in line with the mission of Batteries to foster sustainable and efficient energy storage technologies.
Expected topics:
- Battery SOH and RUL estimation using AI;
- Predictive maintenance of EV batteries;
- AI-enhanced V2G and G2V frameworks;
- ML/DL for EV fleet energy management;
- Digital twins for smart battery systems;
- Explainable AI for battery health monitoring;
- Grid resilience through intelligent battery control;
- Data-driven optimisation of smart grid energy flows;
- AI for ageing-aware charging and load scheduling;
- Case studies of AI-powered battery-grid interaction in smart cities.
Dr. Muhammed Cavus
Prof. Dr. Margaret Carol Bell
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
- AI-powered battery management
- machine learning
- deep learning
- battery SOH estimation
- predictive maintenance
- V2G and G2V integration
- digital twins
- smart grids
- electric vehicle fleets
- grid resilience
- smart cities
- sustainable energy storage
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