Voltage Response Insights into Lithium-Ion Battery Diagnostic Techniques

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dc.contributor.advisorDubarry, Matthieu
dc.contributor.authorFernando, Alexa
dc.contributor.departmentElectrical Engineering
dc.date.accessioned2024-02-26T20:14:20Z
dc.date.available2024-02-26T20:14:20Z
dc.date.issued2023
dc.description.degreeM.S.
dc.identifier.urihttps://hdl.handle.net/10125/107943
dc.subjectElectrical engineering
dc.subjectBatteries
dc.subjectLithium-Ion
dc.subjectVoltage Relaxation
dc.titleVoltage Response Insights into Lithium-Ion Battery Diagnostic Techniques
dc.typeThesis
dcterms.abstractEfficient lithium-ion batteries are increasingly necessary, especially with the growing demand for energy storage. To enhance their efficiency, these batteries require an accurate and reliable method for online diagnosis. This thesis explores improving online diagnosis by examining the battery voltage response of commercial cells under varied cycling conditions. The first study investigates battery voltage relaxation, which is the gradual process of voltage equalization following the cutoff of current flow. Voltage relaxation could be pivotal in online cell state determination, however, research in this area is currently underdeveloped. This study reveals that voltage relaxation behavior is complex and is influenced by the depth of discharge, charge rate, temperature, and cell chemistry. The results provide a unique, comprehensive dataset for further research into voltage relaxation. This dataset is used to validate the efficiency of three voltage relaxation models and three voltage relaxation characterization techniques. The second study examines the effect of temperature on battery voltage response and looks at how it can be integrated into a mechanistic model. This model simulates the battery’s voltage response by quantifying the interactions between a cell’s positive and negative electrodes. The results of this study prove that it is possible to emulate the behavior of a cell at temperatures outside of the typical room temperature testing conditions by altering the charge/discharge rate. Lastly, this thesis analyzes the accuracy of optimization methods for generating synthetic battery data using mechanistic modeling. The results indicate that an exhaustive search technique outperforms optimization algorithms.
dcterms.extent56 pages
dcterms.languageen
dcterms.publisherUniversity of Hawai'i at Manoa
dcterms.rightsAll 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.
dcterms.typeText
local.identifier.alturihttp://dissertations.umi.com/hawii:12018

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