Environmentally Sound Management of End-of-Life Batteries from Electric-Drive Vehicles in North America
Bibliographic record
Abstract
The market for electric-drive vehicles (EDVs), including hybrid electric vehicles (HEVs), plug-in hybrids (PHEVs), and electric, pure battery-powered vehicles (EVs), is expected to experience significant and rapid growth over the coming decades. In the year 2013 all EDVs together represent about 1.44 percent of annual vehicle sales in Canada, about 0.09 percent in Mexico and about 3.81 percent in the US, and numbers are expected to grow rapidly in the coming years. As the market for EDVs expands, there will be a vital opportunity to recapture and recycle the materials used in EDV batteries (nickel, cobalt, steel and other valuable components) once they reach end of life (EOL). This report characterizes the types, quantities, and composition of batteries used in EDVs in North America, and outlines best practices and technologies to support their environmentally sound management (ESM) at end of life. It is projected that about 276,000 EDV batteries will reach EOL in North America in 2015. Most of these batteries are likely to be nickel metal hydride (NiMH), which is the predominant battery chemistry used in HEVs. By 2030, almost 1.5 million EDV batteries will reach EOL. By that time, close to half the EOL EDV batteries will be lithium-based, with the remainder being NiMH batteries. The current domestic infrastructure to handle EOL EDV batteries is limited; however, it is expected to expand over time. A promising second-life use as energy storage units is currently being explored for EOL EDV batteries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".