Design Considerations for Lithium-Ion Battery Safety Used in Unique/Military Platforms
Bibliographic record
Abstract
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
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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.
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".