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State of Charge and Parameter Estimation of Electric Vehicle Batteries

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Title: State of Charge and Parameter Estimation of Electric Vehicle Batteries
Author: Bustos Bueno, Richard Alfonso
Department: School of Engineering
Program: Engineering
Advisor: Gadsden, Andrew
Abstract: Lithium-ion batteries have gained enormous popularity as energy storage elements for the vast majority of rechargeable electric systems, including electric vehicles. To make Lithium-Ion batteries safer and more reliable, a large number of battery models have been developed to estimate their current state of charge (SOC). The SOC determines the driving distance of electric vehicles. The primary objective of this thesis is the analysis of popular battery models found in literature, and to provide a foundation for developing intelligent control and estimation strategies. Four equivalent circuit models (ECMs) are analyzed with a thermal model: The Rint, Thevenin, PNGV, and DP models. Furthermore, three electrochemical models are presented: full order, single particle, and 3-parameter single particle models. Estimation techniques such as the Kalman, extended Kalman, and unscented Kalman filters are applied to the ECMs to increase estimation accuracy. Additionally, interactive multiple models are analyzed for fault detection and diagnosis.
URI: http://hdl.handle.net/10214/13035
Date: 2018-03
Rights: Attribution-NonCommercial 2.5 Canada


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Attribution-NonCommercial 2.5 Canada Except where otherwise noted, this item's license is described as Attribution-NonCommercial 2.5 Canada