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Synthetic Voltage Datasets for Artificial Intelligence-Based Li-Ion Diagnosis and Prognosis : = Investigation of Three Different Blending Conditions.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Synthetic Voltage Datasets for Artificial Intelligence-Based Li-Ion Diagnosis and Prognosis :/
Reminder of title:	Investigation of Three Different Blending Conditions.
Author:	Beck, David.
Description:	1 online resource (162 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Energy. -
Online resource:	click for full text (PQDT)
ISBN:	9798383112380

Synthetic Voltage Datasets for Artificial Intelligence-Based Li-Ion Diagnosis and Prognosis : = Investigation of Three Different Blending Conditions.
Beck, David.

Synthetic Voltage Datasets for Artificial Intelligence-Based Li-Ion Diagnosis and Prognosis :Investigation of Three Different Blending Conditions. - 1 online resource (162 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of Hawai'i at Manoa, 2024.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383112380Subjects--Topical Terms:

784773
Energy.
Subjects--Index Terms:

BatteriesIndex Terms--Genre/Form:

554714
Electronic books.

Synthetic Voltage Datasets for Artificial Intelligence-Based Li-Ion Diagnosis and Prognosis : = Investigation of Three Different Blending Conditions.
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