SYNTHETIC VOLTAGE DATASETS FOR ARTIFICIAL INTELLIGENCE-BASED LI-ION DIAGNOSIS AND PROGNOSIS: INVESTIGATION OF THREE DIFFERENT BLENDING CONDITIONS
SYNTHETIC VOLTAGE DATASETS FOR ARTIFICIAL INTELLIGENCE-BASED LI-ION DIAGNOSIS AND PROGNOSIS: INVESTIGATION OF THREE DIFFERENT BLENDING CONDITIONS
Date
2024
Authors
Beck, David
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Dubarry, Matthieu MD
Department
Mechanical Engineering
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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.
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Keywords
Energy,
Materials Science,
Mechanical engineering,
Batteries,
Inhomogeneities,
Lithium ion,
Lithium ion batteries,
Lithium ion battery degradation,
Lithium ion testing
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161 pages
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