A Systematic and Comparative Study of Electrolyte Additives on LiCoO<sub>2</sub>/Graphite and Li[Ni<sub>1/3</sub>Mn<sub>1/3</sub>Co<sub>1/3</sub>]O<sub>2</sub>/Graphite Pouch Cells
Bibliographic record
Abstract
Introduction Electrolyte additives are used to improve the properties and performance of Li-ion cells [1]. However, the way that electrolyte additives and combinations of additives function in Li-ion cells has not been well explained in the literature. The ultra high precision charger (UHPC) at Dalhousie University, which can measure the coulombic efficiency (CE) to an accuracy of ± 0.003% [2], was used to investigate the effects of electrolyte additives singly or in combination on LiCoO2 (LCO)/graphite and Li[Ni1/3Mn1/3Co1/3]O2(NMC)/graphite pouch cells. It is believed that precision measurements of CE and other factors during the first weeks of cycling can point to the best additive combinations. Experimental Machine-made LiCoO2/graphite and Li[Ni1/3Mn1/3Co1/3]O2/graphite dry pouch cells (402030 size, 220 mAh) were supplied by reputable manufacturers and were filled and sealed at Dalhousie University. Cells were filled with 0.75 g (for LCO/graphite cells) or 0.85 g (NMC/graphite cells) of 1 M LiPF6in ethylene carbonate (EC):ethylmethyl carbonate (EMC) (3:7 in weight ratio, BASF) as control electrolyte. Vinyl ethylene carbonate (VEC), vinylene carbonate (VC), lithium bis(oxalato) borate (LiBOB), fluoroethylene carbonate (FEC), trimethoxyboroxine (TMOBX), ethylene sulfate (DTD), 1,3-Propanediol cyclic sulfate (TMS), propylene sulfate (PLS) and methylene methanedisulfonate (MMDS) were used as electrolyte additives. The cells were cycled using the UHPC between 2.8 and 4.2 V at 40.0 ± 0.1°C using currents corresponding to C/15 for 15 cycles where comparisons were made. After the UHPC cycling, electrochemical impedance spectroscopy was used to measure the combined charge transfer resistance (Rct) of both electrodes in each cell. Before the impedance tests, cells were held at 3.8 V until the current dropped below the corresponding C/1000 current, so that all cells were measured under the same conditions. All impedance data were collected at 10.0 ± 0.1°C, in order to separate the impacts of the various additives better. An automated storage system [3] was used to measure the self-discharge of cells stored under open circuit conditions at 4.2 V. The open circuit potential of each cell was automatically measured every 6 hours (for 500 hours) at a fixed temperature of 40.0 ± 0.1°C. Results and discussion The coulombic inefficiency per hour, charge endpoint capacity slippage, charge transfer resistance at 10°C after the UHPC cycling (600h) and voltage drop during storage are four important parameters that can be used to predict the lifetime of Li-ion cells. Cells with low CIE/h, low charge slippage, low Rctand low voltage drop are much more desired and believed to have long lifetime. Much more detailed information about each of the four parameters will be presented in the lecture. In order to easily distinguish and compare the effectiveness of electrolyte additives, we use one formula to combine the effects of the four parameters into a “Figure of Merit” (FOM). Figure 1 shows the FOM as a function of electrolyte additives. The smaller the FOM, the better the overall performance that additives can bring to cells. There are many interesting things to note in Figure 1 which will be discussed in the lecture. Most notable is that DTD is the only single additive that has a similar FOM as VC. Figure 1. Figure of merit (FOM) consisting of CIE/h (A), charge slippage (B), Rct (C) after UHPC cycling and voltage drop (D) using the formula for (a) LCO/graphite pouch cells FOM=2×105A+50B+C/16 (b) NMC/graphite pouch cells FOM=2×105A+10×B+2×0.05C+40D. The bars without labels are for proprietary additives. References [1] S.S. Zhang, J. Power Sources, 162, 1379 (2006). [2] T. M. Bond, et. al., J. Electrochem. Soc, 160, A521 (2013). [3] N.N. Sinha, et al., J. Electrochem. Soc. 158 A1194 (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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".