Optimization of PV Interconnections for a Solar-charged Electric Vehicle to Maximize On-board Solar Generation
Bibliographic record
Abstract
Recent advancements in high-efficiency flexible thin-film solar cells make it possible for solar-charged electric vehicles to obtain ample solar energy from on-board photovoltaic (PV) cells. To maximize the on-board solar energy generation while driving and parked even under partial shading conditions, this paper demonstrates the necessity of the optimization of the PV interconnections and proposes an optimization method using Genetic Algorithm. Using solar radiation data, modeled partial shading data, and temperature profiles of Los Angeles (LA) to run simulations in MATLAB/Simulink, this research shows the monthly and annual accumulated on-board solar energy comparison between the optimized PV interconnection and an unoptimized design, and demonstrates that an annual 3% more on-board solar energy could be accumulated in LA with the proposed PV interconnection optimization method.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".