Advances in Prognostics and Health Management for Battery Energy Storage Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".
Deadline for manuscript submissions: 27 March 2026 | Viewed by 22
Special Issue Editors
Interests: battery prognostics and health management
Interests: power electronics; more electric aircraft
Special Issues, Collections and Topics in MDPI journals
Interests: battery management systems; battery state estimation; fast charging control; battery impedance measurement
Special Issues, Collections and Topics in MDPI journals
Interests: power battery management system; power battery modeling; state estimation and health management; echelon utilization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
It is becoming increasingly evident that battery energy storage systems (BESSs) are a cornerstone of the transition towards electrified and sustainable technologies. Their critical role is rapidly expanding across a spectrum of modern applications, most notably in electric vehicles, electric aircraft, and electric ships, in which they are fundamental to propulsion, power management, and overall system reliability. This surge in demand for high-performance, safe, and durable batteries has accelerated research into advanced prognostics and health management (PHM) methodologies. The complexity of battery degradation mechanisms, influenced by varying operational profiles and environmental conditions, necessitates intelligent approaches that move beyond traditional monitoring.
This Special Issue aims to present and disseminate cutting-edge research and innovative developments in the field of PHM for battery energy storage systems. We seek contributions that address the challenges of state estimation, fault diagnosis, remaining useful life (RUL) prediction, and health-conscious management, with a particular emphasis on data-driven and artificial intelligence (AI) techniques. The integration of AI, including machine learning and deep learning models, is proving transformative, enabling the analysis of vast datasets for accurate state-of-health (SOH) assessment, early anomaly detection, and the prediction of long-term performance.
Topics of interest for publication include, but are not limited to:
- AI and machine learning for battery state estimation (SOC, SOH, RUL);
- Digital twin technologies for BESSs;
- Advanced fault diagnosis and failure prognosis algorithms;
- Data-driven and model-based fusion approaches for PHM;
- Thermal runaway prediction and safety management;
- Cloud-based and edge-computing solutions for BESS management;
- Optimal charging strategies informed by health status;
- Novel sensors and embedded monitoring systems;
- Lifetime prediction and ageing mitigation techniques;
- Other cross-disciplinary research in the field of batteries;
- Other cross-disciplinary research in the field of PHM.
We look forward to receiving your high-quality contributions.
Dr. Da Li
Dr. Yang Qi
Dr. Jinhao Meng
Dr. Junfu Li
Dr. Qi 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 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. Energies is an international peer-reviewed open access semimonthly 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 2600 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
- battery
- prognostics and health management
- fault diagnosis
- thermal runaway
- machine learning
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
