State-of-Health Estimation of Batteries
A special issue of Batteries (ISSN 2313-0105).
Deadline for manuscript submissions: 31 December 2024 | Viewed by 178
Special Issue Editors
Interests: lithium-ion batteries; lithium-sulfur batteries; energy storage; battery management systems; battery state estimation; degradation and lifetime; battery testing and modelling; ancillary grid services provided by an energy storage
Interests: the application of reduced-order physics-based models for fast model calibration and estimation; control of hybrid battery systems; electrical and module/pack-level thermal modelling and state estimation; and prognostic/diagnostic techniques for predicting and assessing battery health and remaining useful life
Special Issues, Collections and Topics in MDPI journals
Interests: electric vehicle; sustainable transport systems; battery; energy management; optimization; control; artificial intelligence and machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
State-of-health (SOH) estimation of batteries remains a challenging goal. The typical behavior of lithium-ion batteries changes when the anode is doped with a high amount of silicon, highly affecting the accuracy of the estimation. There are novel chemistries worked on or being launched to the market, such as lithium-sulfur or sodium-ion batteries. Concepts of cloud battery management systems open new possibilities, especially in the trending area of machine learning and artificial intelligence. There are new and more demanding applications in the area of aerospace and second-life use. Moreover, ‘smart’ cells or packs are being proposed enhanced with additional sensors to provide extra information. These and more are making the topic of SOH estimation interesting and in high demand. Thus, we would like to encourage you to submit your contributions on the following SOH estimation topics covering:
- Modern lithium-ion batteries;
- Lithium-sulfur batteries;
- Sodium-ion batteries;
- Machine learning;
- Artificial intelligence;
- Cloud BMS;
- Smart cells and packs;
- Novel approaches;
- Remaining useful life.
Dr. Vaclav Knap
Prof. Dr. Daniel Auger
Dr. Abbas Fotouhi
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
- state-of-health
- remaining useful life
- lithium-ion batteries
- lithium-sulfur batteries
- sodium-ion batteries
- machine learning
