Abstract
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.
Original language | American English |
---|---|
Article number | 100530 |
Number of pages | 16 |
Journal | Journal of the Electrochemical Society |
Volume | 168 |
Issue number | 10 |
DOIs | |
State | Published - 2021 |
Bibliographical note
Publisher Copyright:© 2021 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.
NREL Publication Number
- NREL/JA-5700-79499
Keywords
- battery
- cathode cracking
- cathode pulverization
- degradation
- electrochemical
- life model
- lithium-ion
- mechanical
- prognostic model