Lithium-Ion Battery Second Life: Cell Performance Assessment for Stationary Energy Storage Applications
Bibliographic record
Abstract
Repurposing electric vehicle (EV) lithium-ion batteries (LIBs) for second-life applications in stationary energy storage has developed considerable interest. With EV sales continuously rising, some critical materials used in LIBs risk supply shortages, while the volume of spent batteries could begin to overwhelm the facilities capable of disposing or recycling them. EV batteries typically reach end-of-life (EOL) when the capacity fades by 20-30%; although this is EOL exclusively for automotive applications, there still remains plenty of residual energy storage available that can be further utilized for less demanding applications. If viable, this could have significant impact in satisfying economic and environmental concerns as the trend toward greener and more sustainable energy solutions gains momentum. Extending the lifespan of retired EV LIBs can potentially supplement and soften the demand for total product, relaxing the strain on newly manufactured product and changing the standard of practice in the value chain. This lifecycle expansion could also be beneficial with respect to environmental impact by reducing raw material extraction and processing, landfill disposal or recycling, and improving resource sustainability. Existing public work on the reuse of EV batteries has mostly been exploratory, including feasibility studies, techno-economic analyses, and promotional demonstrations. Although necessary as part of the framework to move forward, these investigations lack the data to demonstrate the reliability of EV batteries for second-life applications. The work presented herein utilizes the International Electrotechnical Commission (IEC) standard 62620:2014 to evaluate the compliance, in terms of both capability and longevity, of EOL EV cells for energy storage applications. Battery modules were obtained from consumer-utilized 2012 and 2014 Nissan Leafs and characterization tests were applied to determine the present state of health. Cells were electrically isolated and the IEC Standard’s electrical tests were applied using a de-rated capacity as the new nominal capacity, based on the characterization results. One of the seven IEC Standard tests included a 500-cycle endurance test in which a C/2 charge and discharge rate was applied. Electrochemical impedance spectroscopy was measured periodically throughout the endurance test. Results on the compliance of these cells for stationary energy storage applications will be presented and discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".