Maximizing Photovoltaic Power Output in Partial Shading Conditions for Electric Vehicle Applications: A Comparative Study of Intelligent Control Strategies
Bibliographic record
Abstract
The increasing demand for sustainable transportation has positioned Electric Vehicles (EVs) as a key application area for photovoltaic (PV) systems.This study aims to enhance PV performance under Partial Shading Conditions (PSCs) by employing advanced Maximum Power Point Tracking (MPPT) algorithms.A comprehensive simulation model featuring a boost converter and battery integration is developed to evaluate and compare three MPPT techniques: the conventional Perturb and Observe (P&O) method, Grey Wolf Optimization (GWO), and Fuzzy Logic Control.Unlike previous studies, this work conducts a structured and comparative performance analysis across four distinct and progressively complex shading scenarios.Performance metrics include average power output, convergence time, and steady-state oscillations.Simulation results under shading patterns SP1 to SP4 indicate that the Fuzzy Logic approach achieves superior performance, with mean power outputs of 997.094W (SP1) and 410.081W (SP4), outperforming GWO by 1.7% and 0.7%, respectively.These findings offer quantitative evidence for the effectiveness of intelligent MPPT methods in enabling robust and efficient PV integration for EV applications.
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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 | 0.001 |
| 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) | 0.000 | 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 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".