The 2020 IEEE World Congress on Computational Intelligence

Congress on Evolutionary Computation Special Session

July 19-24, 2020, Glasgow, UK

Special Session on “Evolutionary Computation for Transportation Electrification and Advanced Energy Management”


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 optimization 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 transportation users and storage system operators. Due to the superiorities such as being immune from complicated issue modelling formulation, evolutionary computation therefore becomes a promising and powerful tool to solve formidable optimization 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 evolutionary optimization strategies for solving recently emerging issues in complicated transportation electrification and related energy storage system. The submissions are encouraged to be focus on energy storage 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 optimization topics.

A brief list of potential submission topic is shown below:

1. Energy storage management system

2. Wireless power transfer

3. Unit commitment, economic dispatch and optimal power flow

4. Optimal smart grid scheduling and integration with renewable energy generations

5. Energy management, intelligent coordination and control of electric vehicles/ships

6. Life cycle analysis and optimization of energy storage systems

7. Charging and discharging strategies for energy storage or battery systems

8. Internal and whole scale management for single and hybrid energy storage systems

9. Energy reduction strategies for food and chemical process industry

10. Energy reduction strategies for energy intensive manufacturing processes

11. Parameters identification for photovoltaic models and PEM fuel cells

12. Thermodynamic optimization for heat exchanger design and Organic Rankine Cycle

Paper submission:

Potential authors may submit their manuscripts for presentation consideration through WCCI2020 submission system. All the submissions will go through peer review. Details on manuscript submission can be found from WCCI 2020 Website.

Important dates:

Paper submission deadline: January 15, 2020

Notification of acceptance: March 15, 2020

Final paper submission and early registration deadline: April 15, 2020

Organizers:

Kailong Liu, WMG, University of Warwick, Coventry, United Kingdom. Email id: Kailong.Liu@warwick.ac.uk

Zhile Yang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , China. Email id: zl.yang@siat.ac.cn

Kunjie Yu, School of Electrical Engineering, Zhengzhou University, Zhengzhou, China. Email id: yukunjie@zzu.edu.cn

Zhou Wu, School of Automation, Chongqing University, Chongqing, China. Email id: zhouwu@cqu.edu.cn

Zong Woo Geem, Department of Energy IT, Gachon University, Seongnam-si, South Korea. Email id: zwgeem@yahoo.com

Organizer Biography:

Dr. Kailong Liu is currently a 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 30 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.

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 organized special sessions in WCCI 2018, CEC 2019, ISGT 2018 and many other conferences, and awarded China New Development Award by Springer-Nature press and several best paper award in international journal and conferences.

Dr. Kunjie Yu received the B.S. degree in automation from the Zhengzhou University of Light Industry, Zhengzhou, China, in 2012, and the Ph.D. degree in control science and engineering from the East China University of Science and Technology, Shanghai, China, in 2017. Currently, he is a Lecture with the School of Electrical Engineering, Zhengzhou University. He has published more than 20 peer-reviewed papers on Applied Energy, Energy, Energy Conversion and management and other related journals. His current research interests include evolutional computation, constrained optimization, multi-objective optimization, and their applications in chemical process, photovoltaic system, and energy system.

Prof. Zhou Wu is a full professor in College of Automation, Chongqing University. His research interests include renewable energy system, demand side management, and green building technology. He has been working in these areas for about 10 years, and has set up many collaborations with South Africa and Hong Kong. He has published more than 30 peer-reviewed papers on Applied Energy, Energy, Solar Energy and other related journals. He is a member of IEEE and ACM society. He serves as an associate editor for IEEE/CAA Journal of Automatica Sinica, and International Journal of Compute Science and Mathematics.

Prof. Zong Woo Geem is a Professor in Department of Energy IT at Gachon University, South Korea. He has obtained B.Eng. in Chung-Ang University, Ph.D. in Korea University, and M.Sc. in Johns Hopkins University, and researched at Virginia Tech, University of Maryland - College Park, and Johns Hopkins University. He invented a music-inspired optimization algorithm, Harmony Search, which has been applied to various scientific and engineering problems. His research interest includes phenomenon-mimicking algorithms and their applications to energy, environment and water fields. He has served for various journals as an editor (Associate Editor for Engineering Optimization; Guest Editor for Swarm & Evolutionary Computation, Int. Journal of Bio-Inspired Computation, Journal of Applied Mathematics, Applied Sciences, and Sustainability).