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Record W4239400650 · doi:10.1149/ma2014-02/5/471

Determination of the Voltage-Dependence of Parasitic Heat Flow in Lithium Ion Cells Using Isothermal Microcalorimetry

2014· article· en· W4239400650 on OpenAlexaff
Laura E. Downie, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsJoule heatingThermodynamicsHeat generationChemistryIsothermal processMaterials scienceMechanicsPhysics

Abstract

fetched live from OpenAlex

Parasitic reactions that occur in lithium ion batteries are well known to result in cell failure [1], and therefore being able to measure and reduce these reactions is of utmost importance. Isothermal microcalorimetry has been previously used to determine the relative contribution of parasitic heat flow between cells varying in electrolyte composition [2]. However, the absolute magnitude of the voltage-dependent parasitic heat flow for individual cells has not previously been determined. Here, by varying the current over narrow voltage ranges, the relative contributions of each of the heat flow sources as a function of state of charge can be isolated. When a current is applied to a cell there are three sources of heat flow [3]: Joule heating due to polarization, changes in entropy, and parasitic reactions. Polarization produces a non-reversible heat flow that is proportional to the square of the current, changes in entropy during intercalation and deintercalation produces a reversible heat flow that is proportional to the current, and parasitic reactions produce a heat flow that is thought to be independent of the current. The relative effect of entropy, polarization, and parasitics can therefore be determined over small voltage ranges by varying the current. The data can be fit using a simplistic model where each contribution is modeled with a simple function, typically linear, of the state of charge. The fitting results then give the relative contributions of each term, with particular importance to the function associated with the parasitic heat flow for an individual cell, allowing for the extraction of the voltage-dependence of the parasitic heat flow for an individual cell. Figure 1 shows an example of such an analysis for a 180 mAh machine-made high voltage-LiCoO2/graphite pouch cell with a 1 M LiPF6 in 3:7 EC:EMC electrolyte. The heat flow was measured using a TA instruments TAM III isothermal calorimeter equipped with twelve microcalorimeters with an accuracy of < ±1 mW. There is an excellent agreement between the simplistic model and the experimental data, showing the ability of isothermal microcalorimetry to accurately extract the voltage-dependence of parasitic heat flow in individual cells. This analysis is particularly useful for drawing conclusions about the behaviours of electrolyte additives across different cell chemistries where the method of relative differences in heat flow cannot be used. Results will be presented for three different machine-made pouch cell chemistries: high voltage-LiCoO2/graphite to 4.4 V, Li[Ni0.33Mn0.33Co0.33]O2 (NMC)/graphite to 4.2 V, and NMC/graphite to 4.4 V. The effect of a variety of different additives such as vinylene carbonate, methylene methanedisulfonate, ethylene sulfate, trimethylene sulfate, etc, will also be explored. References [1] J.C. Burns, et al., Electrochem. Solid State Lett., 13, A177 (2010). [2] L.E. Downie, et al., ECS Electrochem. Lett., 2, A106 (2013). [3] J.R. Dahn, et al., Phys. Rev. B, 32, 3316 (1985).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.014
GPT teacher head0.256
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations37
Published2014
Admission routes1
Has abstractyes

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