Automating Battery Model Parameter Estimation using Experimental Data
In this webinar, MathWorks engineers will demonstrate how to speed up and automate fitting measured data to a parameterized battery model of a lithium iron phosphate (LFP) battery cell. Battery models often contain dimensionally large lookup tables, making manually conditioning and fitting data time consuming. Attendees will learn how to efficiently estimate model parameters by combining optimization methods and parallel computing with a layered approach to partition the problem into smaller tasks.
About the Presenters:
Robyn Jackey received his B.S. and M.S. degrees in electrical engineering from Clarkson University in 2001 and 2002. He is a senior technical consultant and specializes in modeling, simulation, and automatic code generation primarily for clients in the automotive and aerospace industries. His recent work includes modeling and simulation of chemical batteries, software development, and implementation of simulation tools using Model-Based Design in large organizations.
Javier Gazzarri is a senior application engineer at MathWorks in Novi, Michigan, focusing on the use of physical modeling tools as an integral part of Model-Based Design. Much of his work gravitates around battery modeling, from cell level to system level; parameter estimation for model correlation; battery management system design; cell balancing; aging; and state-of-charge estimation. Before joining MathWorks, Javier worked on fuel cell modeling at the National Research Council of Canada in Vancouver, British Columbia. He received a bachelor’s degree in mechanical engineering from the University of Buenos Aires (Argentina) and M.A.Sc. (inverse methods applied to surface sensor design) and Ph.D. (solid oxide fuel cell degradation modeling) degrees from the University of British Columbia (Canada).
Recorded: 29 Aug 2013
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