A Chemical, Mechanical and System-Level Approach to Lithium-Ion Cell and Battery Safety
Bibliographic record
Abstract
Lithium-ion cell and battery safety, or limitations thereof, have been hot-button topics for everyone in the energy storage industry for many years. Safety concerns have limited Lithium-ion adaptation in many large-format applications, most naval and undersea applications and several applications where the high energy density, long life characteristics, and many other advantages of the technology would have been mission-enabling. Safety is not a single-point problem, and addressing safety of electrical, chemical and mechanical systems such as batteries requires a semi-holistic approach to provide a meaningful advancement of system-level safety. In many applications, it is not a question of IF an abuse condition will occur, or IF a safety issue will arise, but WHEN; and thus, the need to know how to detect, manage and mitigate the effects of an uncontrolled release of energy in a highly energy dense system. The Yardney Division of EaglePicher Technologies has looked at safety from multiple sides to address Lithium-ion safety. Recent work on improvements in the chemical safety, utilizing reduced flammability electrolytes, ionic liquid based electrolytes, and stable active materials will be presented; as well as test data in small and large format cells to demonstrate the effectivity of these enhancements in real-world applications. Mechanical and Thermal design aspects of both cells and batteries have been evaluated to develop designs that can manage the thermal load from a single cell thermal runaway event and prevent propagation to nearest neighbors in a battery pack. Similarly, system-level safety designs and approaches have been developed to mitigate the effects of an abuse event within the battery and control impact to exterior systems and personnel. All of these design enhancements have been tested in large-format cells and batteries, and results of these tests will be discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".