Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/101139

Adaptive state of charge estimation for battery packs

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Item Summary

Title: Adaptive state of charge estimation for battery packs
Authors: Sepasi, Saeed
Keywords: battery packs
Issue Date: Dec 2014
Publisher: [Honolulu] : [University of Hawaii at Manoa], [December 2014]
Abstract: Rechargeable batteries as an energy source in electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids are receiving more attention with the worldwide demand for greenhouse reduction. In all of these applications, the battery management system needs to have an accurate online estimation of the state of charge (SOC) of the battery pack. This estimation is difficult, especially after substantial aging of batteries. In order to overcome this problem, this work addresses SOC estimation of Li-ion battery packs using fuzzy-improved extended Kalman filter (fuzzy-IEKF) from new to aged cells. In the proposed approach, a fuzzy method with a new class of membership function has been introduced and used to make the approximate initial value to estimate SOC. Later on, the IEKF method, considering the unit single model for the battery pack, is applied to estimate the SOC for the long working time of the pack. This approach uses a model adaptive algorithm to update each single cell's model in the battery pack. The algorithm's fast response and low computational burden, makes on-board estimation practical. A LiFePO4 single cell/battery pack consists of single/120 cells connected in series with a nominal voltage 3.6V/432 V is used to implement the experiments/simulations to verify the SOC estimation method's accuracy. The obtained results by the federal test procedure (FTP75) and the new European driving cycle (NEDC) reveal that the proposed approach's SOC and voltage estimation error do not exceed 1.5%.
Description: Ph.D. University of Hawaii at Manoa 2014.
Includes bibliographical references.
URI/DOI: http://hdl.handle.net/10125/101139
Rights: All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections:Ph.D. - Mechanical Engineering



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