Comparative safety risk and the use of repurposed EV batteries for stationary energy storage
Bibliographic record
Abstract
Electrification of the vehicle market is aiding in increasing fuel efficiencies of vehicles while lowering emissions. However, eventually the vehicle battery will reach its End-of-Life (EOL) point, usually referred to as the point when the State-of-Health (SOH) of the battery is at 80% [1]. At this point, the battery can no longer be used in its original vehicle application, and must be removed for recycling. This has been shown to be uneconomical, since the vehicle batteries still have approximately 80% of their original capacity remaining [1]. Although no longer beneficial as a vehicle battery, they can be further utilized in a different application. Repurposing battery packs, however, can be quite the undertaking with many barriers limiting their adoption. This work seeks to understand the limitations and current codes and standards that affect repurposed battery pack designs. Utilizing these requirements, a bench test setup was designed, built, and tested to determine feasibility.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".