Studies of the Effect of High Voltage on the Impedance and Cycling Performance of Li[Ni<sub>0.4</sub>Mn<sub>0.4</sub>Co<sub>0.2</sub>]O<sub>2</sub>/Graphite Lithium-Ion Pouch Cells
Bibliographic record
Abstract
Introduction Electrolyte additives are the most effective way to improve the calendar life and cycling performance of lithium-ion batteries. Vinylene carbonate (VC) is perhaps the most famous and widely used additive and has been shown to improve cycle and calendar life of Li-ion cells [1]. VC is less effective, however, when used in cells cycling to potentials above 4.2 V [2] or at elevated temperatures [3]. Sulfur-containing additives have recently been investigated by several research groups in the hopes of overcoming the temperature sensitivity of VC and extending the usable voltage range of Li-ion cells [4-6]. LiCoO2/graphite cells with upper cutoff potentials of 4.4 V are now common in the marketplace, but Li[Ni0.4Mn0.4Co0.2]O2 (NMC442)/graphite cells that can be charged to 4.4 V are not, since NMC/graphite cells normally do not function well when charged to high potential. One possible factor leading to severe capacity fade in NMC cells, particularly when charged to high potential, is large impedance growth. It is therefore important and valuable to measure cell impedance as a function of cycle number, time, and voltage. Experimental NMC442/graphite pouch cells containing various electrolyte additives, either singly or in combination, were studied using cycling experiments up to 4.4 and 4.5 V coupled with simultaneous electrochemical impedance spectroscopy (EIS) measurements using a frequency response analyzer (FRA). The impacts of adding prop-1-ene-1,3-sultone (PES), VC, triallyl phosphate (TAP), methylene methane disulfonate (MMDS), ethylene sulfate (DTD) and/or tris(-trimethyl-silyl)-phosphite (TTSPi) to 1M LiPF6 ethylene carbonate:ethyl methyl carbonate (EC:EMC) electrolyte were studied. The additive combination 2% PES + 1% MMDS + 1% TTSPi, named PES211, is highlighted. Results Cells underwent either continuous charge-discharge cycling or charge-hold-discharge cycling in which the cells were held at either 4.4 or 4.5 V for 20 hours. The FRA measured cell impedance every 0.1 V of one cycle during regular intervals. Figure 1 shows a comparison of the discharge capacity (top panels) and Rct, a combination of the charge transfer resistances from both the positive and negative electrodes as well as resistance due to the motion of ions through the SEI layers at both the positive and negative electrodes, as a function of voltage (bottom panels) for NMC442/graphite cells containing PES211. Figure 1a shows the discharge capacity as a function of cycle number for cells containing PES211 undergoing continuous cycling and cycling with a 24 hr hold at 4.4 V. Despite the excellent capacity retention of the PES211–containing cell during continuous cycling, the hold at high voltage leads to severe discharge capacity fade. This effect is also seen in Figure 1b, which shows the discharge capacity as a function of time. Figure 1c shows Rct as a function of voltage for the PES211–containing cell undergoing charge-hold-discharge cycling. This cell exhibited severe impedance growth over 50 cycles (~1700 hours). Interestingly, the impedance was reversible over one cycle, while the impedance was irreversible over several cycles. Figure 1d shows Rct as a function of voltage for the PES211–containing cell undergoing continuous cycling. This cell had very low impedance at all voltages and all 285 cycles (~3000 hours). Figure 1 dramatically demonstrates that holding NMC442/graphite cells at high potentials for extended periods (as might be expected in real-life charging situations in portable electronics, for example) leads to severe impedance growth issues, at least with PES211 electrolyte, which might be overcome through the use of better additive combinations. The importance of performing experiments representative of “real-life” Li-ion cell use is demonstrated. Practical applications that use Li-on batteries often leave the cells at high voltages for extended periods of time after charging. Therefore, studying the effect of extended periods of time at high voltage on the performance and impedance growth of Li-ion cells is imperative. References [1] M. Broussely, P. Biensan, F. Bonhomme, P. Blanchard, S. Herreyre, K. Nechev, and R.J. Staniewicz, J. Power Sources, 146, 90–96 (2005). [2] J. Jeon, S. Yoon, T. Park, J.-J. Cho, S. Kang, Y.-K. Han, and H. Lee, J.Mater.Chem., 22, 21003 (2012). [3] H. M. Jung, S.-H. Park, J. Jeon, Y. Choi, S. Yoon, J.-J. Cho, S. Oh, S. Kang, Y.-K. Han, and H. Lee, J. Mater.Chem.A, 1, 11975 (2013). [4] J. Xia, N. N. Sinha, L. P. Chen, G. Y. Kim, D.J. Xiong, and J. R. Dahn, J.Electrochem. Soc., 161, A84–A88 (2013). [5] B. Li, M. Xu, B. Li, Y. Liu, L. Yang, W. Li, and S. Hu, Electrochim.Acta, 105, 1–6 (2013). [6] B. Li, Y. Wang, H. Rong, Y. Wang, J. Liu, L. Xing, M. Xu, and W. Li, J.Mater.Chem.A, 1, 12954 (2013). Figure 1
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".