Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes. Issue 10 (22nd October 2021)

• Record Type: Journal Article
• Authors: Smith, Kandler; Gasper, Paul; Colclasure, Andrew M.; Shimonishi, Yuta; Yoshida, Shuhei
• Is Part Of: Journal of the Electrochemical Society. Volume 168:Issue 10(2021)
• Publication Date: 2021-10-22
• Subjects: Batteries—Li-ion -- Electrochemical Engineering -- Energy Storage -- Theory and Modelling; Electrochemistry -- Periodicals; 541.3705
• DOI: 10.1149/1945-7111/ac2ebd
• Languages: English
• ISSNs: 0013-4651

Abstract

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.