Technology, economic, and environmental 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 increasing climate concerns has been to promote electric vehicles (EVs) as a replacement for traditional internal combustion engine (ICE) vehicles. Although the objective of EVs seems obvious, EVs come with an undeniable problem: battery decommission and disposal. EV batteries are required to deliver power so that the vehicle can accelerate quickly and drive extended distances; the battery has to be at a sufficient state of health (SOH) to deliver satisfactory results. Once a battery reduces to a SOH that is no longer adequate, it must be retired from the EV. The significant increase in the number of EVs and their forecasted exponential growth also comes with an accumulation of retired batteries, the handling of which raises serious concerns. However, research reveals promising repurposing that can give retired EV batteries another life as second-life batteries (SLBs). Research to address concerns about performance and cost compared to new batteries in various applications, under a variety of conditions, is ongoing. In addition, environmental assessments are being conducted to justify this innovative technology. This review paper outlines the current literature and most recent findings related to these topics and provides a brief comparison of SLBs to new batteries. • The technological, economic and environmental considerations for SLB analysis are presented. • The applicability of various batteries as SLBs is discussed. • Recent findings from various SLB application studies, including models and case studies, are interpreted. • Various optimization techniques to improve SLB performance are presented.
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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 | 0.003 | 0.004 |
| 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) | 0.004 | 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".