Design of smart battery charging circuit via photovoltaic for hybrid electric vehicle
Bibliographic record
Abstract
Abstract This paper proposes an intelligent battery charging scheme for hybrid electric vehicles (HEVs) with a fuel cell as the primary energy source and solar photovoltaic (PV) and battery as the auxiliary energy sources. While dealing with the PV, a minimized oscillation‐based improved perturb and observe (I‐P&O) maximum power point (MPP) tracking (MPPT) scheme is designed to mitigate the impact of oscillations around MPP and loss of tracking direction. The DC–DC boost and DC–DC buck power converters are connected in a cascade manner to harvest optimal power from PV and as a charging circuit for HEV, respectively. An intelligent fuzzy logic‐based proportional integral derivative (PID) (F‐PID) controller is employed for the buck converter to get the constant voltage and constant current for the effective charging of the battery. The two primary objectives of this work are (1) maximum utilization of the designed PV array via the I‐P&O MPPT scheme to enhance the system efficacy, reduce system cost, and reduce complexity. (2) To obtain minimum battery losses and an enhanced life cycle of HEV. The proposed MPPT scheme provides a maximum 99.80% tracking efficiency of the considered PV array at an insolation level of 1000 W/m 2 . Moreover, almost nominal voltage and current ripples have appeared in HEV's proposed intelligent battery charging circuit.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".