The Department of Chemical Engineeirng welcomes you to hear Professor Balakumar Balasingam discuss an important element in the move to mass electric vehicle adoption: management of the battery packs. He will introduce Cramer-Rao Lower bound (CRLB) and its variations as the achievable limit on equivalent circuit modeling parameter estimation, discuss some practical difficulties in achieving them and propose alternate solutions.
All graduate ChE students will receive an Outlook calendar event with webinar access details.
Everyone is welcome. If you are not a graduate ChE student, contact the Manager of Graduate Studies for the access information you need to join the webinar.
Abstract
Electric vehicles (EV) are on their way to replace vehicles powered by internal combustion engines (ICEs) soon. Many nations have set an end date to sell ICE powered vehicles; most other nations are in the process of setting those deadlines. Consequently, all major automakers have started to prioritize electric vehicle production.
The battery pack is one of the most important components of an EV; Lithium-ion based batteries are becoming an attractive choice for battery chemistry in EVs due to their high energy density. One of the disadvantages of the Li-ion batteries, and rechargeable batteries in general, is that they need to be constantly monitored and maintained in order to ensure safety, efficiency and reliability. Equivalent circuit modeling (ECM) approaches have been widely adopted in practical battery management systems. An important goal in ECM based approaches is to correctly identify the ECM that is representative of the battery behavior and to estimate its parameters.
In this talk, Professor Balasingam will introduce the Cramer-Rao Lower bound (CRLB) and its variations as the achievable limit on ECM parameter estimation, discuss some practical difficulties in achieving them and propose alternate solutions.
Biographical Sketch
Balakumar Balasingam is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Windsor. From 2012 to 2017, he was an Assistant Research Professor in the Department of Electrical and Computer Engineering at the University of Connecticut. He received his PhD in Electrical Engineering from McMaster University in 2008. After his PhD, he held a postdoctoral position at the University of Ottawa from 2008 to 2010, and then a University Postdoctoral position at the University of Connecticut from 2010 to 2012.
His research interests are in signal processing, machine learning, and distributed information fusion and their applications in autonomous systems; particularly, his close interests are in battery management systems, human-machine systems, and surveillance and tracking systems. In these areas, Professor Balasingam has published over 75 research papers. In 2017, Professor Balasingam won the ISIF Jean-Pierre Le Cadre Best Paper Award (second runner-up) for his paper titled “Maximum likelihood detection in images,” at the International Conference on Information Fusion in Xi’an, China.