Cause and Mitigation of Lithium-Ion Battery Failure—A Review
Bibliographic record
Abstract
Lithium-ion batteries (LiBs) are seen as a viable option to meet the rising demand for energy storage. To meet this requirement, substantial research is being accomplished in battery materials as well as operational safety. LiBs are delicate and may fail if not handled properly. The failure modes and mechanisms for any system can be derived using different methodologies like failure mode effects analysis (FMEA) and failure mode methods effects analysis (FMMEA). FMMEA is used in this paper as it helps to identify the reliability of a system at the component level focusing on the physics causing the observed failures and should thus be superior to the more data-driven FMEA approach. Mitigation strategies in LiBs to overcome the failure modes can be categorized as intrinsic safety, additional protection devices, and fire inhibition and ventilation. Intrinsic safety involves modifications of materials in anode, cathode, and electrolyte. Additives added to the electrolyte enhance the properties assisting in the improvement of solid-electrolyte interphase and stability. Protection devices include vents, circuit breakers, fuses, current interrupt devices, and positive temperature coefficient devices. Battery thermal management is also a protection method to maintain the temperature below the threshold level, it includes air, liquid, and phase change material-based cooling. Fire identification at the preliminary stage and introducing fire suppressive additives is very critical. This review paper provides a brief overview of advancements in battery chemistries, relevant modes, methods, and mechanisms of potential failures, and finally the required mitigation strategies to overcome these failures.
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 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 | 0.003 | 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) | 0.003 | 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".