Calendar Aging of Lithium-Ion Batteries I. Impact of the Graphite Anode on Capacity Fade

By Peter Keil, Simon F. Schuster, Jörn Wilhelm, Julian Travi, A. Hauser, R. C. Karl, Andreas Jossen. 2016

572 citations recorded by the source corpus.

In this study, the calendar aging of lithium-ion batteries is investigated at different temperatures for 16 states of charge (SoCs) from 0 to 100%. Three types of 18650 lithium-ion cells, containing different cathode materials, have been examined. Our study demonstrates that calendar aging does not increase steadily with the SoC. Instead, plateau regions, covering SoC intervals of more than 20%–30% of the cell capacity, are observed wherein the capacity fade is similar. Differential voltage analyses conﬁrm that the capacity fade is mainly caused by a shift in the electrode balancing. Furthermore, our study reveals the high impact of the graphite electrode on calendar aging. Lower anode potentials, which aggravate electrolyte reduction and thus promote solid electrolyte interphase growth, have been identiﬁed as the main driver of capacity fade during storage. In the high SoC regime where the graphite anode is lithiated more than 50%, the low anode potential accelerates the loss of cyclable lithium, which in turn distorts the electrode balancing. Aging mechanisms induced by high cell potential, such as electrolyte oxidation or transition-metal dissolution, seem to play only a minor role. To maximize battery life, high storage SoCs corresponding to low anode potential should be avoided.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7a5bc37cc4a33c0ca3ffa7c36de228026ec33e6c

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Citations in the corpus

Review—"Knees" in Lithium-Ion Battery Aging Trajectories (2022, unknown): “…do not exhibit high resistance growth due to their lower positive electrode operating voltages (well above the electrolyte stability window), 104,105 even moderate resistance growth coupled with high discharge rates and high lower cutoff voltages could lead to dramatic cell failure…”
Closed-loop optimization of fast-charging protocols for batteries with machine learning (2020, methodology): “This method could extend to other fast-charging design spaces, such as pulsed 26,28 and constant-power 8 charging, as well as to other objectives, such as slower charging and calendar ageing.”
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “Through the progression of each peak in IC/DV curves throughout ageing, and observing the change of the active materials over time, degradation mechanism can be distinguished [52].”