Wednesday, April 24, 2019 - 3:00pm to 3:30pm
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LIDS & Stats Tea
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Bayesian models are applied to probabilistic analysis of phenomena which deal with multiple external stochastic factors and unmeasurable variables. Considering the large amount of available data for the EV driving, recharging and grid services such as solar charging which contains uncertainties and measurement errors, and their hierarchical effect on the battery life, this application of Bayesian models can be useful for the aging probabilistic analysis. Causality is of utmost importance for batteries as their aging is affected by a high number of hierarchical variables that depend upon external factors to the battery. Acknowledging the advantages of Bayesian models, we propose a hierarchical Bayesian model for the probabilistic battery degradation evaluation. Priors distributions are defined based on expert knowledge and Marco Chain Monte Carlo (MCMC) sampling is used to draw the posteriors. This modeling approach reflects the uncertainties of measurements and process, provides more informative results, and it is applicable to any type of input data with proper training.
Mehdi Jafari (Ph.D. Michigan Technological University, 2018; M.Sc. University of Tabriz, 2011; B.Sc. University of Tabriz, 2008; all in Electrical Engineering) is a postdoctoral associate in the Laboratory for Information and Decision Systems (LIDS) at MIT. He is working on Energy Storage solutions for the power system applications and renewables integration. He also has worked on probabilistic analysis of the battery energy storage aging behavior, especially in the electrified transportation and vehicle-to-grid applications. He has authored more than 30 journal and conference papers in the energy storage, electric vehicles, renewable energy and power system fields. His current research interests include energy storage role in renewables integration, battery energy storage performance and degradation in power system and transportation electrification applications.