The Effect of High Charge Rate Cycling on Coulombic Efficiency Measurements
Bibliographic record
Abstract
Recent work has shown good correlation between short term measurements of the coulombic efficiency and long term capacity retention during relatively slow cycling (greater than 20 hours per cycle) and high temperature (above room temperature) [1-3] using the High Precision Charger built at Dalhousie University which is able to measure coulombic efficiency to < 0.01% error [4]. Smith et al. [5] studied how the charge rate impacts the coulombic efficiency in commercial cylindrical cells of different positive electrodes and graphite negative electrodes at different temperatures. This work showed that the departure of the coulombic efficiency from the ideal value of 1.00000 was dependant on the time of one cycle and independent of charge rate for slow rates and elevated temperatures. This work could not investigate the dependence of coulombic efficiency on high charge rates due to limitations of the equipment. Smith et al. [5] showed that as the charge rate of Li-ion cells was increased from ~C/100 to ~C/20, the coulombic efficiency (CE) increased such that (1 – CE) = k*t, where k is a constant that increases with temperature and t is the time of one charge-discharge cycle. Extending this idea to higher cycle rates means that at very high rates the coulombic efficiency approaches 1.00000 since there is very little time per cycle for degradation. However, it is well known that lithium plating can occur at high charge rates with graphite negative electrodes [6, 7] and this would lower the coulombic efficiency. It has been shown that the coulombic efficiency of plating and stripping metallic lithium from graphite electrodes is 0.97-0.98 [8] compared to the coulombic efficiency of intercalating and de-intercalating lithium from a graphite electrode which is > 0.995 [9]. Therefore as the charge rate continues to increase past the onset of lithium plating, the coulombic efficiency should begin to depart further from 1.00000 as a larger fraction of the lithium is involved in plating/stripping instead of intercalation/de-intercalation reactions. Figure 1 shows CE versus charge rate results for two temperatures that shows the time dependent degradation regime at low rates on the left (low rates) and the regime governed by lithium plating on the right (high rates). This study will show results of high charge rate cycling on Li[Ni1/3Mn1/3Co1/3]O2 (NMC)/graphite cells at different temperatures to more thoroughly understand the shape of these curves and the impact of lithium plating on precision measurements of coulombic efficiency and endpoint capacity slippage. This behavior needs to be well understood as many applications for Li-ion batteries, including electrified vehicles, desire high charge rates. References: [1] J.C. Burns, G. Jain, A.J. Smith, K.W. Eberman, E. Scott, J.P. Gardner, and J.R. Dahn, J. Electrochem. Soc., 158, A255-A261 (2011). [2] J.C. Burns, N.N. Sinha, D.J. Coyle, G. Jain, C.M. VanElzen, W.M. Lamanna, A. Xiao, E. Scott, J.P. Gardner, and J.R. Dahn, J. Electrochem. Soc., 159, A85-A90 (2012). [3] A.J. Smith, H.M. Dahn, J.C. Burns, and J.R. Dahn, J. Electrochem. Soc., 159, A705-A710 (2012). [4] HPC [5] A.J. Smith, J.C. Burns, and J.R. Dahn, Electrochem. Solid-State Lett., 13, A177-A179 (2010). [6] S.S. Zhang, J Power Source, 161, 1385-1391 (2006). [7] W. Lu, C.M. Lopez, N. Liu, J.T. Vaughey, A. Jansen, and D.W. Dees, J. Electrochem. Soc., 159, A566-A570 (2012). [8] L.E. Downie, L.J. Krause, J.C. Burns, L.D. Jensen, V.L. Chevrier, and J.R. Dahn, J. Electrochem. Soc., 160, A588-A594 (2013). [9] A.J. Smith, J.C. Burns, X. Zhao, D. Xiong, and J.R. Dahn, J. Electrochem. Soc., 158, A447-A452 (2011).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".