Prof. Sheldon Williamson
Professor and Canada Research Chair in Electric Energy Storage Systems for Transportation Electrification
Electrical, Computer and Software Engineering
Ontario Tech University, Oshawa, ON, Canada
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Prof. Sheldon Williamson (Fellow, IEEE) received the B.E. degree (Hons.) in electrical engineering from the University of Mumbai, Mumbai, India, in 1999, and the M.S. and Ph.D. degrees (Hons.) in electrical engineering from the Illinois Institute of Technology, Chicago, IL, USA, in 2002 and 2006, respectively. He is currently a Professor with the Department of Electrical, Computer and Software Engineering and the Director of Smart Transportation Electrification and Energy Research (STEER) Group, Faculty of Engineering and Applied Sciences, Ontario Tech University, Oshawa, ON, Canada. His current research interests include advanced power electronics, electric energy storage systems, and motor drives for transportation electrification. He holds the prestigious NSERC Canada Research Chair position in electric energy storage systems for transportation electrification.
Keynote Speech: Design and Development of Smart Battery Management Systems using Advanced Digital-twin/AI/ML Techniques
Abstract: It has become imperative to find a solution to manage energy production and usage accurately, especially within the context of future electric energy storage for e-transportation systems. Enhancing the life of Lithium-ion (Li-ion) battery packs has been the topic of much interest. In this framework, the role of on-board cell voltage balancing of Li-ion batteries will be highlighted in this talk. This is a very important topic in the context of battery energy storage cost and life/state-of-charge, SOC/state-of-health, SOH monitoring. Li-ion batteries, although popularly proposed for electric transport, have been highly uneconomic for energy storage, overshooting cost requirements by a large margin. This talk will also introduce a first-of-its-kind closed-loop cell charge (voltage) balancing and extreme fast charging technique. The technique uses instantaneous cell voltage and/or temperature rise (ΔT) as a control parameter. Finally, this talk will also address the opportunities of applying Machine learning (ML), Internet-of-things (IoT), and Digital Twin technologies in the domain of Li-ion battery management systems (BMS). Several practical examples of BMS, industry standards, and testing facilities will be shown to encourage more research and developments in the focused field.
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