Repurposing Batteries of Plug-In Electric Vehicles to Support Renewable Energy Penetration in the Electric Grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it