Intelligent Control, Optimization and Management of Sustainable Battery Energy Storage System
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".
Deadline for manuscript submissions: 22 November 2026 | Viewed by 1195
Special Issue Editors
Interests: battery management technology; intelligent control theory and method; optimal control of complex nonlinear systems; power electronics
Special Issues, Collections and Topics in MDPI journals
Interests: electrochemical energy storage system; electric vehicles; battery management system
Interests: electric vehicle; lithium-Ion battery; battery management system; electrochemical energy storage system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Lithium-ion batteries play a crucial role in modern battery energy storage systems, and their rational utilization is vital for the global energy landscape shifting towards a sustainable development direction. However, due to the extremely complex application environment and the strong non-linearity and time-varying characteristics of lithium-ion batteries, the safety, durability, and reliability of the battery system remain significant challenges in real-world scenarios. In general, these objectives can be achieved by taking measures in the following areas, including, but not limited to, optimized battery charging and heating, accurate state estimation of batteries, real-time fault diagnosis, early warning, and inhibition of battery thermal runaway. Progresses in these areas can not only significantly enhance battery efficiency and service life but also ensure operational safety, thereby accelerating the development of modern energy storage technology and enhancing its sustainability.
To this end, we propose a Special Issue titled "Intelligent Control, Optimization and Management of Sustainable Battery Energy Storage System". This issue aims to bring together researchers and engineers working on related topics, providing a cutting-edge platform to share innovative methods and significant research findings. We cordially invite submissions of original research papers, review articles, and interdisciplinary reports.
Prof. Dr. Bin Duan
Dr. Rui Zhu
Dr. Qi Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- battery energy storage system
- fault diagnosis
- battery state estimation
- advanced machine learning
- artificial intelligence
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