SYNTHETIC VOLTAGE DATASETS FOR ARTIFICIAL INTELLIGENCE-BASED LI-ION DIAGNOSIS AND PROGNOSIS: INVESTIGATION OF THREE DIFFERENT BLENDING CONDITIONS

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dc.contributor.advisorDubarry, Matthieu MD
dc.contributor.authorBeck, David
dc.contributor.departmentMechanical Engineering
dc.date.accessioned2024-07-02T23:42:08Z
dc.date.available2024-07-02T23:42:08Z
dc.date.issued2024
dc.description.degreePh.D.
dc.identifier.urihttps://hdl.handle.net/10125/108365
dc.subjectEnergy
dc.subjectMaterials Science
dc.subjectMechanical engineering
dc.subjectBatteries
dc.subjectInhomogeneities
dc.subjectLithium ion
dc.subjectLithium ion batteries
dc.subjectLithium ion battery degradation
dc.subjectLithium ion testing
dc.titleSYNTHETIC VOLTAGE DATASETS FOR ARTIFICIAL INTELLIGENCE-BASED LI-ION DIAGNOSIS AND PROGNOSIS: INVESTIGATION OF THREE DIFFERENT BLENDING CONDITIONS
dc.typeThesis
dcterms.abstractLithium-ion batteries are a cornerstone of modern energy storage systems and play a crucial role in the transition towards a sustainable energy future. Their performance and longevity are impacted by various parameters such as composition, architecture, environment, and degradation mechanisms. In addition, the degradation might not be uniformly distributed across the components of the battery, leading to inhomogeneities.This research delves into the effect of three specific blending conditions on the voltage response of lithium-ion batteries: active material blends, lithium plating, and inhomogeneous degradation. This aspect is key as the voltage response of a cell is used for conducting diagnoses and prognoses. By integrating experimental data with simulations from the alawa model, we aim to enhance our understanding of battery behavior, particularly focusing on effects these blending conditions have on the overall voltage response. Through experimental tests, we have gained an understanding of the observable effects, ultimately aiming to improve state of the art battery models to make them more accurate for generating synthetic data. The latter is essential to properly validate battery diagnosis and prognosis methodologies.
dcterms.extent161 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:12185

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Ph.D. - Mechanical Engineering