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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".