Leveraging Machine Learning for Next-Generation Battery Design
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: 30 April 2026 | Viewed by 17
Special Issue Editors
Interests: modeling and fault monitoring of complex industrial systems; computational energy intelligence
Special Issues, Collections and Topics in MDPI journals
2. Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul 34467, Turkey
Interests: next generation batteries
Interests: clean energy; electrochemical energy storage; artificial intelligence; solid-state batteries; advanced manufacturing
Interests: modeling and control of complex systems; process monitoring; fault detection and diagnosis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning has significant potential to enable a more economic, efficient, and reliable low-carbon transition of energy systems, for example, by accelerating the design of next-generation battery chemistries, enhancing distributed energy resource coordination, and advancing battery management systems, including battery lifetime prediction, capacity fade estimation, and optimal charge design. This Special Issue aims to provide an overview of the state of the art, to present new research results, and to discuss promising future research directions at the interface between the fields of batteries and machine learning.
Potential topics include, but are not limited to, the following:
- Machine learning for battery management system including battery lifetime prediction, capacity fade estimation, and optimal charge design;
- Machine learning and reinforcement learning for distributed optimization and control of large-scale battery energy storage systems;
- Physics-informed machine learning for battery system optimization;
- Machine learning-based time aggregation method for energy system planning;
- Electrochemical energy system optimization with machine learning;
- Battery material design with generative AI.
Dr. Benben Jiang
Dr. Mustafa Ergen
Dr. Jiayu Wan
Dr. Qiugang Lu
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
- machine learning
- reinforcement learning
- generative AI
- energy systems
- battery management systems
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Related Special Issue
- Machine Learning for Advanced Battery Systems in Batteries (11 articles)
