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Interpretable Data-Driven Modeling Reveals Complexity of Battery Aging

Authors :
Bruis van Vlijmen
Patrick A. Asinger
Vivek Lam
Xiao Cui
Devi Ganapathi
Shijing Sun
Patrick K. Herring
Chirranjeevi Balaji Gopal
Natalie Geise
Haitao D. Deng
Henry L. Thaman
Stephen Dongmin Kang
Amalie Trewartha
Abraham Anapolsky
Brian D. Storey
William E. Gent
Richard D. Braatz
William C. Chueh
Publication Year :
2023
Publisher :
American Chemical Society (ACS), 2023.

Abstract

To reliably deploy lithium-ion batteries, a fundamental understanding of cycling and aging behavior is critical. Battery aging, however, consists of complex and highly coupled phenomena, making it challenging to develop a holistic interpretation. In this work, we generate a diverse battery cycling dataset with a broad range of degradation trajectories, consisting of 363 high energy density commercial Li(Ni,Co,Al)O$_2$/Graphite + SiO$_x$ cylindrical 21700 cells cycled under 218 unique cycling protocols. We consolidate aging via 16 mechanistic state-of-health (SOH) metrics, including cell-level performance metrics, electrode-specific capacities/state-of-charges (SOCs), and aging trajectory descriptors. Through the use of interpretable machine learning and explainable features, we deconvolute the underlying factors that contribute to battery degradation. This generalizable data-driven framework reveals the complex interplay between cycling conditions, degradation modes, and SOH, representing a holistic approach towards understanding battery aging.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....26a131a3b97bd2cfe2dfb06d4e5b8e44