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Special Issue "Advances in Battery Management Storage for Electric Vehicles: When Models Meet Data"
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".
Deadline for manuscript submissions: 15 October 2023 | Viewed by 277
Special Issue Editors
Interests: modeling, estimation, control and optimization for lithium-ion batteries
Interests: design, analysis and application of emerging electric machines and drives
Interests: modeling, estimation, control, and optimization for lithium-ion batteries
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Electric vehicles are emerging as the backbone of the sustainable development of transportation electrification. Their performance, safety, and reliability rely heavily on the energy storage system and battery management control strategies. Inappropriate battery operations may cause premature failures and even catastrophic hazards. In recent decades, model-driven battery management strategies have gained considerable attention from various academic and industrial communities due to their closed-loop design and high robustness. Meanwhile, data-driven methods are also at the forefront of applications for battery modeling, estimation, and optimization. With the rapidly increasing uptake of electric vehicles with high degrees of connectivity, there has been growing interest in fusing model-driven and data-driven approaches into hybrid models to improve the system-level performance in terms of long lifetime, safety, and high reliability. However, the fusion of model-driven and data-driven strategies is still a challenge due to the complexity of battery mechanisms and the increasing volume and diversity of battery data.
This Special Issue, therefore, seeks to inspire ideas related to all aspects of recent advances in model-driven and data-driven battery management technologies, and the ideas on how to fuse model-driven and data-driven frameworks into hybrid models that combine the best aspects of both. Prospective authors are invited to submit original works for review and publication in this Special Issue. Both high-quality original research and review articles are welcome. Potential topics include, but are not limited to, the following:
- Modeling, estimation, control, and optimization for lithium-ion batteries;
- Battery health/aging modeling, diagnosis, and prognostics;
- Optimal, fast, health-aware charging, balancing control, etc.;
- Failure detection and fault tolerance control in battery management;
- Applications of machine learning and artificial intelligence in battery management.
Dr. Guangzhong Dong
Dr. Jincheng Yu
Dr. Ji Wu
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. Electronics 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 2000 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.
- modeling, estimation, control, and optimization for lithium-ion batteries
- battery health/aging modeling, diagnosis, and prognostics
- optimal, fast, health-aware charging, balancing control
- failure detection and fault tolerance control in battery management
- applications of machine learning and artificial intelligence in battery management