Cadmium Telluride Solar cell: From Device modeling to electric vehicle battery management
Bibliographic record
Abstract
Battery charging in system level implementation for industrial and vehicular application charged by second generation thin film solar cells like CdTe can could have a promising future. These second-generation thin film solar cells are becoming popular for their cheaper production and better efficiency. Though the production of solar cells is still based mainly on silicon (Si), the market share of thin film solar has been increasing over the last few years [1]. The mathematical modeling of the voltage dependent current-voltage (I-V) characteristics of Cadmium Telluride (CdS/CdTe) Solar cell and utilizing that modeling mathematics in to circuit for electric vehicle standard battery charging have been analyzed in this paper. A single cell is developed based on the mathematical model and a solar module/network is constructed considering a series and parallel combinations of the single cell. The I-V characteristic of the cell is used as a source. Then the network response was analyzed under various operating conditions like intensity and temperate change. To extract the power from the solar cell, Perturb and Observe (P&O) Maximum power point technique has been used. Then a second converter driven with the developed charging algorithm is included. As the two control algorithms (MPP and battery charging) are working in the same system, mismatch between PV system and battery bank might happen [2]. The simple charging algorithm considering the both constant current and constant voltage mode and switching between these two modes when needed has been described.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".