Solar-Charged Electric Vehicles: A Comprehensive Analysis of Grid, Driver, and Environmental Benefits
Bibliographic record
Abstract
To date, solar-powered electric vehicles (EVs) have often been considered as niche projects or with small vehicle rooftop panels that can slightly extend the electric driving range. This article proposes a large-scale solar EV concept with low-cost, flexible, and thin-film solar cells integrated onto the steel of all upward-facing vehicle body panels as a viable solution to help mitigate EV charging and range concerns and the high cost and solar power intermittency of individual residential rooftop solar installations. Only by considering the full range of benefits, namely to the grid, driver, and environment, the value of solar-charged EVs can truly be appreciated. This article models the effect of panel tilt and partial shading on the solar energy capture of 150 drivers to analyze grid, driver, and environmental benefits in Los Angeles (LA) and Detroit over the course of a full year. The simulations predict net annual vehicle energy use reductions of 21.5% in LA and 17.5% in Detroit for average cloud conditions, compared to a nonsolar EV.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".