Physics-Informed Artificial Intelligence for Battery Energy Storage Systems
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: 30 December 2025 | Viewed by 1084
Special Issue Editors
Interests: energy intelligent management; AI for battery; fractional-order modeling
Special Issues, Collections and Topics in MDPI journals
Interests: battery energy management; fractional-order system
Special Issues, Collections and Topics in MDPI journals
Interests: data-driven modeling, learning, and optimization; control theory of fractional systems and their applications; distributed measurement and distributed control; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In order to meet the demand of the energy revolution for carbon neutralization, batteries have become one of the most important components in all modes of electrified transportation and the supporting electric energy stations. The performance of a battery energy storage system affects the efficiency and safety of the operation of a power system significantly. Despite the widespread use of traditional modeling mechanisms and state estimation methods for battery energy storage systems, machine learning, physics-informed knowledge, and intelligent control have attracted increasing attention recently.
Therefore, this Special Issue is focused on recent advances in battery energy storage systems that address the above-mentioned aspects and go beyond the state of the art. Prospective authors are invited to submit original contributions/articles for review and possible publication in this Special Issue. Topics of interest include (but are not limited to) the following:
- The design of BMSs (battery management systems);
- Physics-informed and data-driven methods for battery modeling;
- State estimation of battery energy storage systems;
- Innovative methods of SOX (SOC, SOH, SOP, SOE, SOS, SOT) and other states estimations methodologies;
- Intelligent battery management systems with advanced algorithms;
- Battery safety management, including safety diagnostics, prognostics, and warning methods;
- Battery life management, including degradation estimation, aging prediction, and health management;
- Temperature control and thermal management of battery energy storage systems;
- Methods for battery optimization, control, and balancing;
- Energy management of advanced power systems.
Dr. Yanan Wang
Prof. Dr. Liping Chen
Prof. Dr. Yangquan Chen
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
- fractional-order modeling and control
- intelligent management
- energy storage systems
- design of BMSs (battery management systems)
- artificial intelligence for batteries
- battery safety diagnostics
- battery degradation estimation
- physics-informed machine learning
- data-driven modeling
- edge–cloud collaboration
- edge computing and cloud computing
- digital twins
- battery optimization and control
- battery prognostics
- battery pre-warning
- cell balancing
- battery lifetime monitoring
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