Document Type
Conference Proceeding
Publication Date
6-10-2024
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.
Recommended Citation
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
Publication Title
Proceedings Volume 13036, Big Data VI: Learning, Analytics, and Applications
DOI
https://doi.org/10.1117/12.3013519
Comments
© 2024 SPIE
SPIE grants to authors (and their employers) of papers, posters, and presentation recordings published in Proceedings of SPIE the right to post an author-prepared version or the officially published version (preferred) on an internal or external repository controlled exclusively by the author/employer, or the entity funding the research, provided that (a) such posting is noncommercial and the publication is made available to users without charge; (b) an appropriate SPIE attribution and citation appear with the publication; and (c) a DOI link to SPIE’s official online version of the publication is provided. This authorization does not extend to third-party websites not owned and maintained by the author/employer such as ResearchGate, Academia.edu, YouTube, etc.