Invited Presentation: The Impact of High Voltage Electrolyte Additives and Solvents on Parasitic Heat Determined Using Isothermal Microcalorimetry
Bibliographic record
Abstract
Parasitic reactions that occur in lithium ion batteries are well known to ultimately cause cell failure, and therefore being able to measure and reduce these reactions is of utmost importance. The use of electrolyte additives in can extend cycle life, increase coulombic efficiency, and reduce these parasitic reactions [1]. The technique of isothermal microcalorimetry has been previously used to determine the relative contribution of parasitic heat flow between cells varying in electrolyte composition [2]. Here, this technique is used to determine the impact of high voltage electrolyte additives and solvents as a function of voltage with a variety of cell chemistries. Additionally how these additive combinations compare to commercially available “high-voltage electrolytes” will be shown. Transitioning to increasingly higher upper cutoff voltages has proved difficult as many additives and solvents are unstable at such high potentials and the parasitic degradation of these components result in severely decreased lifetimes. The voltage-dependent impact of electrolyte additives, additive blends, and solvents that have shown to result in high coulombic efficiencies, reduced electrolyte oxidation, and other parasitic reactions, will be explored. Such components include additives such as vinylene carbonate (VC), methylene methanedisulfonate (MMDS) [3], tris(trimethylsilyl)phosphite (TTSPi) [4], and others; commercially available high voltage electrolytes from two electrolyte suppliers, A and B; as well as fluorinated solvent systems. By comparing the heat flow of cells that vary only in electrolyte composition during cycling, the effect of the additive on the parasitic heat for one cell chemistry during the entire potential range is obtained in a short, simple experiment. Furthermore, simple modeling of the heat flow response results in the determination of the absolute magnitude of the voltage-dependent parasitic heat flow for individual cells, allowing for comparisons across different cell chemistries [5]. A TA instruments TAM III isothermal calorimeter equipped with twelve microcalorimeters was used for these measurements. The accuracy of the microcalorimeter used (< ±1 mW) allows for a highly sensitive differentiation between cells. Figure 1 shows an example of such a heat flow measurement for five machine-made 220 mAh Li[Ni1/3Mn1/3Co1/3]O2/graphite pouch cells cycling at 10 mA (C/22) at 40.0oC with various additive blends. At this current, the contributions of entropy and polarization to the total heat flow are nearly identical between cells, such that any differences in measured heat are attributable to differences in parasitic heat. The bottom panel of Figure 1 shows the differences when subtracting the heat flow of the control cell (no additive) from the heat flow of the additive-containing cells as a function of potential. These differences are good measures of the reduction in parasitic heat due to the addition of an additive. This figure shows that the inclusion of electrolyte additives can greatly reduce the parasitic reactions, especially so at high voltage. The effect of a variety of additive systems on a variety of cell chemistries such as high-voltage LiCoO2/graphite operating to 4.4 V, and Li[Ni0.4Mn0.4Co0.2]O2/graphite operating to 4.5 and 4.7 V will be presented. While these electrolyte additives and solvents can reduce the heat due to parasitic reactions, the overall parasitic heat is still substantial at high potentials, as seen in Figure 1, for example, by the dramatically increasing heat flow above 4.3 V. The results presented here clearly show the impact of high voltage electrolyte additives and solvents and directly determine their optimal potential ranges to minimize parasitic reactions and therefore extend cell lifetimes. The feasibility of operating cells above 4.4 or 4.5 V will also be discussed in this context. References [1] J.C. Burns, A. Kassam, N.N. Sinha, L.E. Downie, L. Solnickova, B.M. Way, and J.R. Dahn, J. Electrochem. Soc., 160, A1451 (2013). [2] L.E. Downie, K.J. Nelson, R. Petibon, V.L. Chevrier, and J.R. Dahn, ECS Electrochem. Lett., 2, A106 (2013). [3] J. Xia, N.N. Sinha, L.P Chen, G.Y. Kim, D.J. Xiong, and J.R. Dahn, J. Electrochem. Soc., 161, A84 (2014). [4] L. Ma, D.Y. Wang, L.E. Downie, J. Xia, K.J. Nelson, N.N. Sinha, and J.R. Dahn, J. Electrochem. Soc., submitted (#JES-14-1306). [5] L.E. Downie and J.R. Dahn, IMLB 2014 Abstract P3-0671; manuscript in preparation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.022 |
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".