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Record W4309819708 · doi:10.1149/ma2022-023356mtgabs

Understanding Li-Ion Cell Internal Short Circuit during Nail Penetration By Simultaneous in Situ Measurement of Local Current, Resistance and Temperature

2022· article· en· W4309819708 on OpenAlexaboutno aff
Siyi Liu, Shan Huang, Qian Zhou, Kent Snyder, Mary K. Long, Guangsheng Zhang

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
Fundersnot available
KeywordsThermocoupleMaterials sciencePenetration (warfare)Short circuitThermal runawayVoltageInternal resistanceCurrent (fluid)Penetration depthComposite materialThermal resistanceContact resistanceFOIL methodMechanicsNuclear engineeringElectrical engineeringPower (physics)OpticsHeat transferEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Internal short circuit (ISC) can be a main cause of Li-ion cell thermal runaway in field failures, but its mechanisms still require better understanding1 , 2. Due to the highly localized and electrochemical-thermal coupled nature of ISC, it is important to use in situ/operando measurement of critical parameters for insightful understanding of the mechanisms. We recently reported a method for in situ measurement of dynamic ISC resistance and ISC current during nail penetration as schematically shown in Figure 1(a)3. When an ISC is formed inside the small test cell by nail penetration, external current will flow from the large power supply cell to the test cell. The test cell capacity is much smaller than the large power supply cell, so the measured current (A) can be assumed to be equal to the ISC current. The ISC voltage (V) is measured directly. Then the ISC resistance can be obtained through dividing the ISC voltage by the ISC current. The local temperature at the ISC spot is directly measured by a thermocouple embedded at the tip of the smart nail4. The previous method enabled insightful understanding of ISC3. It was observed that the ISC resistance changed by several orders of magnitude during nail penetration and dramatically influenced heat generation and local temperature rise. In some cases, the local ISC temperature increased more than 500 ℃, and even caused melting of Al foil in contact with the nail and rapid recovery of ISC resistance. But the previous method has a shortcoming. As noted in Figure 1(a), the small test cell with ISC is chemically and thermally disconnected from the large power supply cell. Such disconnection makes the thermal behaviors of the cells different from a real-world large Li-ion cell in which the ISC location is electrochemically and thermally connected to the entire cell. In particular, it does not allow investigation of thermal runaway propagation from the ISC location to the large cell. Built on our previous work while addressing its shortcoming, here we report an improved method. As shown schematically in Figure 1(b), a small cell and a large cell are fabricated inside the same pouch, sharing the same electrolyte and the same separator. They are not only electrically connected, but also chemically and thermally connected. The measurement of ISC current, resistance and temperature is similar to our previous work. When the smart nail penetrates the small cell, the ISC current flows from the large cell to the small cell through the external wire and can be measured by a current sensor (A). The ISC resistance is obtained from directly measured ISC current and ISC voltage. The local temperature can be measured not only by the thermocouple embedded at the tip of the smart nail, but also by thermocouples embedded at different locations inside the large cell5. This new method will enable insightful understanding of the highly localized and electrochemical-thermal coupled ISC phenomena under conditions closer to field failures. The experimental data can also be used for validation and improvement of numerical models of ISC. References X. Lai, C. Jin, W. Yi, X. Han, X. Feng, Y. Zheng and M. Ouyang, Energy Storage Materials, 35, 470 (2021). G. Zhang, X. Wei, X. Tang, J. Zhu, S. Chen and H. Dai, Renewable and Sustainable Energy Reviews, 141 (2021). S. Liu, S. Huang, Q. Zhou, K. Snyder, M. Long and G. Zhang, 241st ECS Meeting, May 29- June 2, 2022, Vancouver, BC, Canada (2022). S. Huang, X. Du, M. Richter, J. Ford, G. M. Cavalheiro, Z. Du, R. T. White and G. Zhang, Journal of The Electrochemical Society, 167 (2020). S. Huang, Z. Du, Q. Zhou, K. Snyder, S. Liu and G. Zhang, Journal of The Electrochemical Society, 168 (2021). Figure 1

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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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.244
Teacher spread0.209 · 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
Published2022
Admission routes1
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