Stranded Energy Management in Li-Ion Batteries During Transportation
Bibliographic record
Abstract
Stranded Energy (SE), i.e., residual electrical charge retained in lithium-ion batteries (LIBs) following a crash, poses significant risks during the post-incident handling and transportation of electric vehicles (EVs). Unlike typical high-voltage hazards, SE persists even when the vehicle is powered down, creating electrocution, thermal runaway, and toxic chemical exposure risks for first responders and technicians. This paper provides a structured assessment of SE hazards, classifying them into electrical, thermal, and chemical categories and analyzing the mechanisms by which each manifests. We examine real-time quantification methods, including smoke color observation, acoustic emission band analysis, gas venting signatures, and thermal imaging, to enable early detection and hazard grading. To guide risk mitigation, we propose a four-tier SE Management Framework: (i) no stranded energy removal; (ii) non-destructive stranded energy removal (level 1); (iii) non-destructive stranded energy removal (level 2); and (iv) destructive stranded energy removal. Each level corresponds to specific response actions based on battery condition and severity. Our framework consolidates current practices with emerging tools, offering a consistent decision-making model for safe SE removal. The approach supports improved responder safety, regulatory compliance, and battery transport protocols. Future work may extend this framework through automated battery management system (BMS) integration, chemistry-specific thresholds, and tool standardization.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".