Technology and economic analysis of second-life batteries as stationary energy storage: A review
Bibliographic record
Abstract
With global warming on the rise, the push for zero emission transportation continues to grow. The transportation sector’s solution to these increasing concerns introduced society to electric vehicles (EVs) as a replacement for traditional internal combustion engine (ICE) vehicles. Although the objective of EVs seems obvious, the problem is more complex than it seems. EVs come with an undeniable problem, battery decommission and disposal. However, this possibly offers a unique opportunity if research continues in its current direction. An exclusive characteristic to EV batteries is their requirement to deliver power in such a way that the vehicle can accelerate quickly and drive extended distances. These demanding applications mean the battery has to be at a sufficient state of health (SOH) to deliver satisfactory results. Once a battery’s SOH reduces to a level that is no longer adequate, it must be retired from the EV. The EV population has grown significantly and is forecasted to continue growing exponentially, thus coming with the accumulation of retired batteries. Serious concerns are drawn to the handling of such batteries. However, research shows that there is promising repurposing that can give retired EV batteries a second life, referring to them as second life batteries (SLBs). Research in this area is ongoing to realize concerns about performance and cost compared to using new batteries in various applications, under a variety of conditions. This review paper outlines these topics, providing an brief, overall comparison of SLBs to new batteries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".