Motivation and Scope
Increasingly severe fuel consumption and carbon pollution have been significantly pushing the academia and transportation sector to actively deploy transportation electrification and develop advanced energy management solutions. Numerous computational issues have been formulated to efficiently reduce the economic cost and benefit the energy conversion efficiency from energy storage systems and transportation application side. However, several key issues are of extreme non-convex, non-smooth or mixed integer characteristics, resulting in huge challenges for energy and transportation users and operators. Due to the superiorities such as being immune from complicated issue modelling formulation, artificial intelligence (AI) method therefore becomes a promising and powerful tool to solve complex tasks in transportation electrification and energy management solutions, further helping to reduce the fuel consumptions and carbon pollutions.
This special session aims at bringing together the state-of-the-art advances of AIs for solving recently emerging issues in complicated transportation electrification and related energy storage system. The submissions are encouraged to be focus on battery management, wireless charging, smart grid scheduling with integration of new participants such as renewable generations, plug-in electric vehicles, distribution generations and energy storages, multiple time-spacial energy reductions and other energy optimisation topics.
- AIs for energy management, intelligent coordination and control of vehicles/ships
- AIs for life cycle analysis and optimization of energy storage systems
- AIs for charging and discharging strategies for energy storage battery systems
- AIs for internal and whole scale management for single and hybrid energy storage systems
- AIs for the integration of communications and sensing in intelligent transportation systems
- AIs for spatial-temporal data in smart energy and transportation systems
- AIs for sequencing and scheduling techniques on transportation systems
- AIs for intelligent transportation systems
Dr. Zhile Yang obtained his BSc in Electrical Engineering and the MSc degree in Control Engineering both from Shanghai University (SHU) in 2010 and 2013 respectively, and he then received Ph.D. degree at the School of Electrical, Electronics and Computer Science, Queen’s University Belfast (QUB), UK. He is currently an associate professor in Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. His research interests focus on bio-inspired modeling and optimization methods and their applications on smart grid, machine vision, and advanced manufacturing. He is the founding chair of IEEE QUB student branch and an active member of IEEE PES, CIS and SMC societies. He is serving as the Editor of Complexity, and Guest Editor of several international journals, as the author or co-author of more than 80 articles in peer reviewed international journals and conferences, and as an active reviewer for over 40 international journals. Dr. Yang has been awarded China New Development Award by Springer-Nature press and several best paper awards in international journal and conferences.
Dr. Shaojun Gan is currently an assistant professor with the College of Metropolitan Transportation, Beijing University of Technology, China. He received the Ph.D. degree from the School of Automation, Chongqing University, China. From 2014 to 2016, He was a visiting Ph.D. student at the School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, UK. From 2017 to 2019, He was a research fellow at the School of Electronic and Electrical Engineering, University of Leeds, UK. His main research interests include system modelling and machine learning methods, with the applications to manufacturing energy systems and intelligent transportation systems.
Dr. Kailong Liu is currently a senior research fellow with the Warwick Manufacturing Group (WMG), University of Warwick. He received the Ph.D. degree from the Energy, Power and Intelligent Control group, Queen's University Belfast, UK, in 2018. He was a Visiting Student Researcher at the Tsinghua University, China, in 2016. His research interests include system modelling and probabilistic machine learning methods, with the applications to batteries and clean energy; The development of new and advanced evolutionary computation technologies for energy management in electric vehicles and battery-based energy storage systems, with the aim to improve their efficiency, safety and sustainability. He was the chair of IEEE QUB student branch and an active member of IEEE society. He has produced more than 40 peer reviewed papers on IEEE transactions, Energy Conversion and management, Journal of Power source and other related journals and conferences. He has been served as an outstanding or active reviewer for over 25 international journals.
Paper submission:
Prospective authors are invited to submit full-length papers before the submission deadline through the online submission system at https://easychair.org/conferences/?conf=lsms2020icsee2020.Further, proposals for special sessions within the technical scopes of the conference are most welcome. Papers submitted for special sessions will be peer-reviewed with the same criteria as used with regular papers. A special session proposal should include the session title, a brief description, and the names, contact details and bio-sketch of the organizers.
See also the Author's kit for paper format.
Accepted papers will be published in the Springer Communications in Computer and Information Science (CCIS) proceedings (EI Compendex). Some high-quality papers will be recommended for possible publication in SCI indexed international journals after expansion and further review, such as Journal of Modern Power Systems and Clean Energy (MPCE), Transactions of the Institute of Measurement and Control (TIMC), Enterprise information system (EIS), Energies, Cognitive Computation, Journal of Control and Decision, Advances in Manufacturing (AIM), etc.
