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Record W4252826554 · doi:10.1149/ma2019-01/6/571

Design Considerations for Lithium-Ion Battery Safety Used in Unique/Military Platforms

2019· article· en· W4252826554 on OpenAlexaboutno aff
E. Andrukaitis

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsnot available
Fundersnot available
KeywordsBattery (electricity)Automotive engineeringShort circuitEngineeringNuclear engineeringComputer scienceElectrical engineeringForensic engineeringPower (physics)Voltage

Abstract

fetched live from OpenAlex

For current and future military and other unique applications, lithium-ion batteries (LIB) require several design considerations and improvements to meet safety requirements. An overview of the basic cell design for high power requirements and aspects of battery safety, such as, the nature of the combustion products are examined at Defence Research and Development Canada (DRDC) using a special facility for controlled burning of items and chemical analysis of the smoke and ash. Also, to pass requirements for a military environment, important extreme physical abuse tests, which are not part of the UN procedure are conducted to understand battery safety and failure modes. For example, nail penetration tests are relevant to EV market as well, where vehicle collisions can occur and a severe internal short-circuit may be induced. There are two versions of the nail penetration test that are typically conducted (slow and fast). The slow test uses an electric motor to push the nail into the battery and the fast test uses a nail gun. In both tests the nail is left imbedded in the cells, imposing a severe short-circuit and examples will be shown. Historically for some tests, pouch cell designs do not appear to be as safe as metal cylinder cells. This is in part because that cell design allows air to enter the cells more readily. However, it has been observed that the fire produced by a pouch cell is less intense than the flames emitted from cylindrical cells when they vent. Thus, cell/battery designs have a significant impact on a large format lithium-ion battery safety test performance. Impact tests are highly relevant to many lithium-ion battery applications, especially for future large format battery designs. The data shows that, although a battery may have passed a certain series of tests, it does not necessarily mean that it is safe to physical abuse when in the fully-charged state. It is important to understand what testing was carried out and the condition of the battery. For example, one severe test is bullet penetration, which can have a range of results depending on the cell materials and state of charge. An example of such a test at the cell level is shown in the figure below for the bullet penetration of a 4.5 Ah lithium cobalt oxide pouch cell and a 2.3 Ah lithium iron phosphate spirally wound cell. Both are fully charged lithium-ion cells that did not catch fire when penetrated, with only some frothing of the electrolyte seen in the spirally wound cell. However, when multiple cells are placed in a battery pack it will be discussed that in most circumstances they behave more hazardously. A brief history of design failures observed will also be presented. 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.006

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 head0.037
GPT teacher head0.257
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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