Standalone DC level-1 EV Charging using pv/Grid infrastructure, MPPT algorithm and CHAdeMO protocol
Bibliographic record
Abstract
This paper presents an approach of DC level 1 charging of an electric vehicle (EV) or plug-in hybrid electric vehicle (PHEV) making use of standalone solar photovoltaic (PV) system. In this approach it is proposed to use the DC power generated by solar panels to directly charge EV traction battery pack using solar MPPT controller algorithm and CHAdeMO DC fast charging protocol. A supervisory control algorithm is developed to handle the standalone solar conditions. With the proposed solution, any EV users with CHAdeMO DC fast charging port can charge their vehicle. An example of commercially available EV `Nissan Leaf' is considered for explaining the solution with 1 kW solar PV system. An average daily commuter distance is considered along with the survey data of different demographic solar energy harvesting capability. Calculations are made to prove that this solution can charge EVs off-the-grid, hence zero running cost.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".