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

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dc.contributor.advisor Dubarry, Matthieu MD
dc.contributor.author Beck, David
dc.contributor.department Mechanical Engineering
dc.date.accessioned 2024-07-02T23:42:08Z
dc.date.available 2024-07-02T23:42:08Z
dc.date.issued 2024
dc.description.degree Ph.D.
dc.identifier.uri https://hdl.handle.net/10125/108365
dc.subject Energy
dc.subject Materials Science
dc.subject Mechanical engineering
dc.subject Batteries
dc.subject Inhomogeneities
dc.subject Lithium ion
dc.subject Lithium ion batteries
dc.subject Lithium ion battery degradation
dc.subject Lithium ion testing
dc.title SYNTHETIC VOLTAGE DATASETS FOR ARTIFICIAL INTELLIGENCE-BASED LI-ION DIAGNOSIS AND PROGNOSIS: INVESTIGATION OF THREE DIFFERENT BLENDING CONDITIONS
dc.type Thesis
dcterms.abstract Lithium-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.extent 161 pages
dcterms.language en
dcterms.publisher University of Hawai'i at Manoa
dcterms.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.
dcterms.type Text
local.identifier.alturi http://dissertations.umi.com/hawii:12185
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Ph.D. - Mechanical Engineering