Studies of Rollover Failure in Lithium-Ion Cells
Bibliographic record
Abstract
Sometimes lithium-ion cells show a very insidious type of failure where they display close to 100% of their capacity for about 1000 charge-discharge cycles and then lose most of their capacity in only 100 cycles or so with very little warning to the user. This is called “rollover failure” [1]. Experimental observations show that the likelihood of rollover failure increases with upper cut-off potential of lithium-ion cells and decreases with LiPF 6 concentration in a reasonable range. Since increasing the upper cut-off potential is essential to increase the energy density of lithium-ion cells, a full understanding of the causes of rollover failure is essential, but this is proving very difficult to attain. The phenomenon of rollover failure during long-term cycling will be discussed based on a comparison among Li(Ni 0.5 Mn 0.3 Co 0.2 )O 2 /graphite pouch cells with different electrolyte and electrode designs undergoing different testing protocols. A few facts can be gleaned from the data: For cells charged to the same upper cut-off potential, those showing the highest rates of electrolyte oxidation at the positive electrode (due to electrolyte or cell chemistry changes) are most prone to rollover failure. Any cell is more prone to rollover failure if charged to a higher potential. This increases the rate of electrolyte oxidation at the positive electrode. When rollover occurs, the impedance at the positive electrode always increases significantly while the impedance at the negative electrode side is relatively stable. Lithium metal plating at the negative graphite electrode surface is not always observed during the initial stages of rollover failure. Increasing the LiPF 6 concentration properly can delay the occurrence of rollover failure. For example Figure 1 shows the impact of increasing salt concentration from 1.2 M to 1.5 M. Based on these and other observations, some simple models that integrate electrolyte oxidation, impedance growth and lithium ion diffusion can be postulated but further experimental studies using a variety of methods are required for full understanding. [1] J. C. Burns, A. Kassam, N. N. Sinha, L. E. Downie, L. Solnickova, B. M. Way, J. R. Dahn, J. Electrochem. Soc. , 160 , A1451-A1456 (2013). Figure 1. Capacity (top panels), normalized capacity (middle panels) and ΔV (bottom panels) versus cycles of Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 /graphite pouch cells filled with 1.2 M LiPF 6 or 1.5 M LiPF 6 in EC:EMC:DMC (25:5:70 vol.%) and 2 wt.%VC+1 wt.%DTD. Cycling was performed between 3V and 4.1V or 4.3V at 20 o C with charging/discharging rates at 1C/1C. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".