Battery Prognostics and Health Management
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: closed (10 October 2024) | Viewed by 702
Special Issue Editors
Interests: fault diagnosis and health management
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis; prognostics and health management; deep learning; ensemble learning; remaining useful life prediction
Special Issue Information
Dear Colleagues,
Batteries play a vital role in many fields, such as mobile devices, electric vehicles, and smart grids. The efficient PHM (Prognostics and Health Management) of batteries is crucial for maximizing their performance, lifespan, and safety. However, challenges persist in achieving precise, SoC and SoH estimation, fault diagnosis, and prognosis and health management due to factors like aging, temperature variations, and load profiles. Artificial Intelligence (AI) technologies and some other new methods have shown great promise in advancing PHM by leveraging machine learning algorithms, data analytics, and predictive modeling. This Special Issue aims to explore the latest research and developments in this field, showing efficient SoC and SoH estimation, PHM methods that exhibit good performance such as high accuracy, high robustness, and good generalization, etc. Through these methods, we hope to facilitate the advancement and development of battery PHM (Prognostics and Health Management) technology.
This Special Issue provides a platform to discuss novel algorithms, modeling techniques, experimental validations, and practical implementations related to PHM, and SoC and SoH estimation. Ultimately, the insights shared in this issue will contribute to enhancing the efficiency, durability, and sustainability of lithium-ion battery systems across various industries. Thus, we would like to encourage you to submit your original research articles and review articles to this Special Issue.
Potential topics include, but are not limited to, the following:
- Battery performance and degradation modeling;
- Novel algorithms for PHM, SoC and SoH estimation;
- Data-driven and AI-driven applications;
- Real-time PHM for dynamic operating conditions;
- Physics-informed AI method for battery SoH estimation;
- Verification and validation of PHM algorithms;
- SoC and SoH estimation in battery management systems (BMS).
Prof. Dr. Jian Ma
Dr. Yujie Cheng
Dr. Meiling Yue
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
- batteries
- prognosis and health management technology of batteries
- SoC and SoH estimation
- remaining useful life prediction
- artificial intelligence
- physics-informed AI
- data mining
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