Mechanism and Control Strategies of Lithium‐Ion Battery Safety: A Review
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) are extensively used everywhere today due to their prominent advantages. However, the safety issues of LIBs such as fire and explosion have been a serious concern. It is important to focus on the root causes of safety accidents in LIBs and the mechanisms of their development. This will enable the reasonable control of battery risk factors and the minimization of the probability of safety accidents. Especially, the chemical crosstalk between two electrodes and the internal short circuit (ISC) generated by various triggers are the main reasons for the abnormal rise in temperature, which eventually leads to thermal runaway (TR) and safety accidents. Herein, this review paper concentrates on the advances of the mechanism of TR in two main paths: chemical crosstalk and ISC. It analyses the origin of each type of path, illustrates the evolution of TR, and then outlines the progress of safety control strategies in recent years. Moreover, the review offers a forward-looking perspective on the evolution of safety technologies. This work aims to enhance the battery community's comprehension of TR behavior in LIBs by categorizing and examining the pathways induced by TR. This work will contribute to the effective reduction of safety accidents of LIBs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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.
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".