CITES 2026 proposals for Special Sessions should fall within the technical scope of the conference. Special Sessions serve as an important supplement to the regular program, aiming to showcase state-of-the-art research from academia and industry on specialized, innovative, challenging, and emerging topics. Proposals should be submitted by the prospective organizer(s), who are expected to serve as the Chair or Co-Chair and take responsibility for the overall organization of the session, including promotion, coordination of submissions, and management of the review process. Each Special Session is scheduled for 2 hours and typically accommodates 6–10 oral presentations; if the number of accepted contributions exceeds the available capacity, some presentations may be arranged as posters. Accepted and presented papers will be included in the conference proceedings and submitted to relevant databases in accordance with the conference policy.
Important Dates for Special Session Proposal Submissions
• Proposal Submission Deadline: April. 25, 2026
• Acceptance Notification Deadline: April. 30, 2026
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• Proposals Submission Guidelines
Proposal Template is provided indicating: Title; Introduction and Topics; Organizer's Information, etc.
Please complete the special session proposal form and send to cites@hkaasr.org before the deadline.
Accepted Special Session
Title of Special Session
AI-Empowered Next-Generation Battery Energy Storage Systems: From Intelligent Management to Safe Operation
Brief Description of the Session
With the large-scale deployment of electrochemical energy storage in electric vehicles, smart grids, and renewable energy integration, the safety, reliability, and economics of battery systems have become core challenges to high-quality industrial development. Traditional management methods struggle to address complex, nonlinear, and coupled issues such as battery aging, inconsistency, and thermal runaway under diverse operating conditions. This special session aims to explore how advanced artificial intelligence (AI), machine learning, and big data analytics can revolutionize the management and operation of battery energy storage systems.
The session focuses on cutting-edge technologies for precise battery state sensing, intelligent fault diagnosis, safety early warning, and life-cycle operation optimization. It emphasizes the integration of data-driven approaches with physical models to develop next-generation battery management systems for complex scenarios. We cordially invite researchers, experts, and engineers from academia and industry to share their latest findings and foster interdisciplinary collaboration, aiming to advance energy storage technology toward greater intelligence, safety, and efficiency.
Related Topics
- • Data-driven Multi-dimensional State Estimation of Batteries
- • Fault Diagnosis and Safety Early Warning for Battery Systems
- • AI-Empowered System Coordination and Optimization
- • Digital Twins and Multi-Scale Modeling
- • Data-driven Reliability, Safety, and Emerging Methods
 Session Chair Prof. Hui Hou Wuhan University of Technology husthou@126.com |
Biography Prof. Hui Hou received the B.S. degree from Wuhan University, Wuhan, in 2003, and the Ph.D. degree from the Huazhong University of Science and Technology, Wuhan, in 2009. During 2015-2016, she was a visiting scholar at the University of Sydney. She is currently associate professor and Ph.D supervisor, as well as the Department Head of Electrical Engineering, School of Automation, Wuhan University of Technology. Her research interests include risk assessment of power system, energy internet, electric vehicles, etc. She has been AE or Young professional AE for a number of journals such as Protection and Control of Modern Power Systems (PCMP), Electric Power Construction, etc. She has been nominated as World's Top 2% Scientists by Stanford and Elsevier in 2024. |
 Session Vice-Chair Dr. Peng Wei Associate Researcher Wuhan University of Technology pengwei7@whut.edu.cn |
Biography Peng Wei is an Associate Researcher at the School of Automation, Wuhan University of Technology, and is also affiliated with the Hubei Key Laboratory of Advanced Technology for Automotive Components. Dr. Wei received his Ph.D. degree in Systems Engineering from City University of Hong Kong in 2023 and his M.E. degree in Mechanical Engineering from Huazhong University of Science and Technology in 2019. He is dedicated to theoretical and technological research on energy storage systems and power batteries for new energy vehicles. In 2023, he was selected for the National Overseas Talent Introduction Program (Ministry of Education). He has led projects including the National Natural Science Foundation of China (NSFC) Youth Program, the Hubei Provincial Natural Science Foundation General Program, the Hubei Provincial Postdoctoral Science Foundation Project, and the Wuhan Municipal Youth Foundation Project. |