Reducing Impedance Growth in Li[Ni<sub>0.4</sub>Mn<sub>0.4</sub>Co<sub>0.2</sub>]O<sub>2</sub>/Graphite Pouch Cells Using Optimized Cell Components and Electrolytes
Bibliographic record
Abstract
Introduction It has been shown that Li[Ni0.4Mn0.4Co0.2]O2 (NMC442)/graphite cells exhibit severe capacity fade due to impedance growth at the positive electrode when exposed to high potential for extended periods of time.1 An increase in the diameter of the AC impedance spectra is primarily due to increases in the resistance of the positive electrode solid electrolyte interphase (SEI) layer, whereas shifts in the high frequency intercept of the impedance spectra correspond to increases in the electronic and/or ionic path resistances.1,2Impedance increase is accelerated during aggressive cycling conditions, such as cycling to high potential (4.5 V) or cycling that includes extended periods of time at high potential (4.4 V and above), but can be improved through the use of adequate electrolyte solvents and electrolyte additives. However, irreversible changes to the impedance occur in all cells, and it is necessary to investigate the cause of irreversible impedance growth in cells and determine possible solutions. Experimental Machine-made NMC442/graphite pouch cells with a variety of cell components were filled with 1M LiPF6 in EC:EMC 3:7 (by weight) with 2% each of prop-1-ene-1,3-sultone, ethylene sulfate, and tris-(trimethyl-silyl) phosphite.3,4 These cells contained either a ceramic coated polypropylene (PP) or ceramic filled polyethylene terephthalate (PET) separator, carbon black (CB) or carbon nanotubes (CNT) as the conducting diluent in the positive electrode, and a regular Al foil current collector (Al) or a carbon-coated Al foil current collector (CC-Al). Cells with ceramic coated PP separator, carbon black conducting diluent and regular Al foil are the standard configuration which was used in cells described in our previous work.1,2 The different separators were chosen to explore their resistance to oxidation at high potential. The different conducting diluents were chosen to explore whether nanotubes could limit carbon degradation.5 The carbon-coated current collector (from 3M) was used in an attempt to improve the positive electrode/collector interface. The eight possible combinations of cells were cycled between 2.8 V and 4.45 V at 40oC, with a 24 hour hold at the top of charge using equipment capable of cycling and automatic electrochemical impedance spectroscopy measurements. Impedance spectra were measured every 0.1 V during every 6thcycle to monitor impedance growth as a function of voltage, time, and cycle number. Results Figures 1A-H show the preliminary raw impedance spectra at different cycle numbers as measured at 3.8 V for the eight types of cells, as labelled in each panel. Figures 1A-H show that cells with the highest impedance growth are those which previously had been our standard recipe: i.e. cells with carbon black conducting diluent and regular aluminum foil (third column in Figure 1). Switching to carbon coated Al (first two columns in Figure 1) or replacing carbon black by carbon nanotubes in cells made with standard Al foil (4thcolumn in Figure 1) reduced impedance compared to the standard recipe. Detailed simulations of these and other impedance spectra, especially those measured at higher voltage, are underway to learn how the various components affect the various parameters of an equivalent circuit model. Figures 1I-L show the discharge capacity as a function of cycle number for the two cells corresponding to the spectra shown above each panel. This experiment is in progress and final results will be presented at the meeting. This work suggests that, in addition to choice of electrolyte, cell components such as separators, foil coatings and conductive diluent can have a significant effect on the impedance growth, capacity retention and lifetime performance of pouch cells cycled to high potential. References 1. K. J. Nelson, D. W. Abarbanel, J. Xia, Z. Lu, and J. R. Dahn, J. Electrochem. Soc., 163, A272–A280 (2016). 2. D. W. Abarbanel, K. J. Nelson, and J. R. Dahn, J. Electrochem. Soc., 163, A522–A529 (2016). 3. K. J. Nelson, G. L. D’Eon, A. T. B. Wright, L. Ma, J. Xia, and J. R. Dahn, J. Electrochem. Soc., 162, A1046–A1054 (2015). 4. L. Ma, J. Xia, and J. R. Dahn, J. Electrochem. Soc., 161, A2250–A2254 (2014). 5. M. Metzger, C. Marino, J. Sicklinger, D. Haering, and H. A. Gasteiger, J. Electrochem. Soc., 162, A1123–A1134 (2015). 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".