Automotive energy systems
Bibliographic record
Abstract
Owing to recent well-known trends, renewable resources are becoming increasingly prominent in the complex energy market mosaic. As long as their penetration level is low, they can be handled easily by the current infrastructure, but at present incremental rates, this will not be the case in the future. The intermittent nature of solar and wind generation will require a far more flexible compensation mechanism than is currently available. Because of this, large battery banks that act as buffers between the generator and the grid invariably accompany today's renewable energy installations. Wind power, in particular, is not only intermittent but also it has no day-average predictability, as winds can differ hour-to-hour as easily at night as during the day, adding an extra amount of irregularity to an already varying load. This suggests that plug-in electric vehicles (PEVs) will be called on to perform, not only the more manageable regulation tasks, but also aid in providing peak power. As noted earlier, this might not find approval with PEV owners unless the pricing model is modified. Nevertheless, it is reasonable to ask whether a large PEV contracted fleet could perform this task on a national (US) level. Studies have shown that the answer is yes. With an overconfident 50% estimation for the market penetration of wind energy and 70 million PEVs available, peak power could be provided at the expense of approximately 7 kWh of battery energy per day or about 10%-20% of an average PEV reserve.
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.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.228 | 0.159 |
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".