Tutorial

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Tutorial I

“Managing Li-Ion Battery Systems in Electric Vehicles” by Dr. Ryan Ahmed, McMaster University, Canada.

Dr. Ryan Ahmed is an Assistant Professor at McMaster University, deputy director of the Center for Mechatronics and Hybrid Technologies (CMHT), and co-lead faculty advisor for the Battery Workforce Challenge (BWC) . He received his M.A.Sc., Ph.D., and MBA from McMaster University in 2011, 2014, and 2018 respectively. He has held several senior positions in Electric and Autonomous vehicles at General Motors, Samsung, and Stellantis in Canada and the United States. Dr. Ahmed has taught over half a million learners from 160 countries on Udemy and Coursera , and he has over 250,000 subscribers on his YouTube channel titled “Prof. Ryan Ahmed,” where he teaches people AI, data science, and ML fundamentals. Dr. Ahmed is a Udemy Instructor Partner, Professional Engineer (P.Eng.) in Ontario, and Stanford Certified Program Manager. He is the principal author/co-author of over 40 journal and conference papers in artificial intelligence, battery systems, electric and hybrid powertrains, and autonomous systems. Dr. Ahmed is the co-recipient of two best papers awards at the IEEE Transactions on Industrial Electronics (2018) and the IEEE Transportation Electrification Conference and Expo (ITEC 2012) in Detroit, MI, USA.

 

Course Summary:
This short course is designed to provide learners with a comprehensive understanding of managing Li-Ion battery systems in Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs). The course covers essential topics in Battery Management Systems (BMS), including battery modeling, analysis, aging, thermal management, and the estimation of state of charge and state of health. Learners will explore advanced concepts such as artificial intelligence and machine learning (AI/ML), parameter estimation, system identification, optimization, filtering, and control theory, all applied to battery systems. The course emphasizes a hands-on approach, ensuring students learn through practical experience and real-world applications.

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Tutorial II

“Energy Transition towards Renewable and Green Hydrogen Energy Systems: Technologies and Prospectives” by Dr. Muhammad Bakr Abdelghany,  Khalifa University, UAE.

Muhammad Bakr Abdelghany (Senior, IEEE) received the B.Sc. degree in Computer and Systems Engineering and the M.Sc. degree in Electrical Engineering from the Faculty of Engineering, Minia University, Minia, Egypt, in 2010 and 2015, respectively, and the Ph.D. degree in Systems and Control Engineering from the University of Sannio, Benevento, Italy, in 2022. In 2010, he served as a Teaching Assistant with the Department of Computer and Systems Engineering at Minia University, Egypt. He is currently with Khalifa University of Science and Technology (Tenure-track Researcher) and on leave from Minia University (Assistant Professor). His research interests include control synthesis, cyber-physical systems, computer-controlled systems, green hydrogen production, renewable energy systems, and embedded systems. Dr. Abdelghany has supervised/co-supervised 15 Ph.D./Master’s students. He is a reviewer for various reputed journals, including the Control Community, Power and Energy Society, and Robotics and Automation Society. He is also a guest editor for a special issue under IEEE Trans. on Industry Applications on control applications in hydrogen energy systems. He was honored with prestigious academic awards, such as an outstanding Reviewer for IEEE Trans. on Sustainable Energy in 2023. Dr. Abdelghany is a senior researcher in several international projects, such as HAEOLUS, H2GLASS, and H2STEEL.

 

Course Summary:
The course ‘’Optimal control of renewable and green hydrogen energy systems for grid services’’ provides an in-depth technical foundation for understanding the role of hydrogen in achieving net zero emissions. It addresses the integration of renewable energy sources and the decarbonization of the transportation, industrial, and heating sectors. Participants will engage with the economic considerations of green hydrogen production via water electrolysis, examining cost drivers such as technological advancements and economies of scale. The course delves into key hydrogen technologies, including electrolyzers for hydrogen production, storage systems for gaseous hydrogen, and fuel cells for power and heat generation. Learners will explore advanced modeling methodologies, focusing on the time-dependent dynamics, accuracy constraints, and computational requirements necessary for efficient system design. Optimization strategies, including hybrid dynamical systems and model predictive control (MPC), will be applied to hydrogen energy systems, with in-depth case studies demonstrating their application in real-world scenarios. By combining theoretical knowledge with practical examples, this course equips participants with the technical expertise to optimize hydrogen systems for effective renewable integration and grid service management, contributing to the development of sustainable energy infrastructures.

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