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Record W3024279538 · doi:10.1149/ma2020-012427mtgabs

Investigation of Li-Ion Batteries for Fast Charging at Low Temperature

2020· article· en· W3024279538 on OpenAlexaffabout
Alexis Laforgue, Xiao‐Zi Yuan, Alison Platt, Shawn Brueckner, Mathieu Toupin, Florence Perrin‐Sarazin, Minh Tri Nguyen, V. Gauthier, Raymond Kintak Yu, Jean-Yves Huot, Asmae Mokrini

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBattery (electricity)Range (aeronautics)Automotive engineeringComputer scienceIonElectrical engineeringEnvironmental scienceMaterials scienceEngineeringChemistryPhysicsPower (physics)Composite materialThermodynamics

Abstract

fetched live from OpenAlex

Fast-charging of Li-ion batteries has become a major interest, since it is seen as the strategy of choice to enable a wider adoption of electric vehicles (EVs) by bringing a realistic solution to long-range transit. However, very little is known about how repeated fast-charging affects the battery performances in the long term, and how winter temperatures, more extreme in northern countries such as Canada, may impact the fast charging ability of the battery. Optimization of both the chemistry and the manufacturing parameters are needed to develop EV batteries capable of sustaining repeated fast-charging. The work was carried out during a four years project at the National Research Council of Canada, with the overall objective to get a better understanding of the parameters affecting the ability of a Li-ion battery to charge at high rate and sustain repeated fast-charging, especially at low temperatures. A first set of experiments investigated the comparative fast charging performance of a number of 18650 format commercial cells at various temperatures. Figure 1 shows a table summarizing the ability of 11 of these cells to charge under different regimes at room temperature or -10 °C. Interestingly, the variability in the results seemed to be related to multiple parameters. Also, the capability rating for fast-charging was not the same at ambient, sub-ambient or sub-zero temperatures. Best contenders were repeatedly fast-charged for 300 cycles at various temperatures. Here again the results showed differences in how the cells are able to sustain repeated fast-charging. The cells were then disassembled for an in-depth post-mortem study of the failure modes due to fast-charging. A number of degradation mechanisms were revealed, including macroscopic deterioration of the electrodes at the core of the jelly-roll, aluminum current collector corrosion, exfoliation of graphite and formation of thick SEI films at the anode surface. Figure 2 shows example of results from the post-mortem analysis. In a second set of experiments, the fast-charging performance of selected components was comparatively investigated in laboratory cells, including various active materials, separators and electrolyte formulations. In an example of results to be presented, Figure 3 displays the fast-charging capability of several cathode materials in full cells at 23 and 0 °C. LFP and NCA seem to be the best choices for fast-charging Li-ion cells at ambient temperature whereas LCO shows the lowest performances for rates over C/3. However, LFP showed some limitations at 0 °C, whereas LCO happened to be the best performing cathode. These results clearly show that even if the graphite anode bears the most significant challenges relative to fast-charging, cathode materials should not be overlooked in the optimization of fast-charging capabilities. The third set of experiments, currently being carried out, studies the effects of electrode manufacturing parameters (loading, porosity, N/P ratio) using a pilot-scale pouch cell prototyping line, and supported by model simulations. The presentation will highlight the results of all three sets of experiments, including results from simulation, electrochemical testing and post-mortem analysis, in an attempt to give the audience a comprehensive guidance towards the chemistries and manufacturing parameters of Li-ion cells better suited for fast-charging. Figure 1

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.243
Teacher spread0.221 · 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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Citations0
Published2020
Admission routes2
Has abstractyes

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