Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SO...

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Bibliographic Details
Published in:Journal of the Electrochemical Society 2021-10, Vol.168 (10), p.100530
Main Authors: Smith, Kandler, Gasper, Paul, Colclasure, Andrew M., Shimonishi, Yuta, Yoshida, Shuhei
Format: Article
Language:English
Subjects:
ADVANCED PROPULSION SYSTEMS
Batteries—Li-ion
battery
cathode cracking
cathode pulverization
degradation
electrochemical
Electrochemical Engineering
ENERGY STORAGE
life model
lithium-ion
mechanical
prognostic model
Theory and Modelling
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Summary:This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.
ISSN:0013-4651
1945-7111
DOI:10.1149/1945-7111/ac2ebd