Presentation + Paper
10 June 2024 Comparing life-cycle dynamics of Li-ion batteries (LIBs) clustered by operating conditions with SINDy
Author Affiliations +
Abstract
Lithium-ion batteries (LIBs) play a big part in the vision of a net-zero emission economy, yet it is commonly reported that only a small percentage of LIBs are recycled worldwide. An outstanding barrier to making recycling LIBs economical throughout the supply chain pertains to the uncertainty surrounding their remaining useful life (RUL). How do operating conditions impact initial useful life of the battery? We applied sparse identification of nonlinear dynamics method (SINDy) to understand the life-cycle dynamics of LIBs with respect to sensor data observed for current, voltage, internal resistance and temperature. A dataset of 124 commercial lithium iron phosphate/graphite (LFP) batteries have been charged and cycled to failure under 72 unique policies. Charging policies were standardized, reduced to PC scores, and clustered by a k-means algorithm. Sensor data from the first cycle was averaged within clusters, characterizing a ”good as new” state. SINDy method was applied to discover dynamics of this state and compared amongst clusters. This work contributes to the effort of defining a model that can predict the remaining useful life (RUL) of LIBs during degradation.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Kristen L. Hallas, Md Shahriar Forhad, Tamer Oraby, Benjamin Peters, and Jianzhi Li "Comparing life-cycle dynamics of Li-ion batteries (LIBs) clustered by operating conditions with SINDy", Proc. SPIE 13036, Big Data VI: Learning, Analytics, and Applications, 1303607 (10 June 2024); https://doi.org/10.1117/12.3013519
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KEYWORDS
Batteries

Neural networks

Dynamical systems

Sensors

Lithium

Manufacturing

Mathematical optimization

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