Repurposing Batteries of Plug-In Electric Vehicles to Support Renewable Energy Penetration in the Electric Grid
Bibliographic record
Abstract
After they reach their technical on-board end-of-life, plug-in electric vehicles batteries can provide opportunities for second life applications. Plug-in-hybrid and battery-only electric vehicles could provide utility-scale battery storage that could support grid applications, like for example, integration of intermittent renewable energy. For renewables like wind and solar intermittency acts as a major barrier to achieve high penetration scenarios. This paper examines how Li-ion batteries of plug-in electric vehicles reaching approximately 70% of their initial charging capacity can be repurposed and be used to integrate wind power to minimize grid impacts. As the cost can restrict the use of utility-scale use of batteries, repurposed batteries could provide an economical approach to integrate wind energy. We present a model that predicts the capacity of available kWh given the market share projections of plug-in electric vehicles for Canada through 2050. In addition, battery storage requirement to produce uniform wind power is determined by applying high-resolution wind data. The simulation model shows that by 2050, generated wind power supported by repurposed batteries could meet the load demand imposed by plug-in electric vehicles. Therefore, repurposing aftermarket batteries has the potential to maximize the renewable energy ratio by displacing gasoline with new sources of intermittent renewable wind energy with minimal grid impact.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".