Grid tied hybrid PV fuel cell system with energy storage and ANFIS based MPPT for smart EV charging
Bibliographic record
Abstract
This paper presents the comprehensive design, simulation, and experimental validation of a grid-tied hybrid renewable energy system tailored for electric vehicle (EV) charging applications. The proposed system integrates photovoltaic (PV) panels, a proton-exchange membrane fuel cell, battery storage, and a supercapacitor to ensure reliable and efficient power delivery. An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm is employed to enhance PV power extraction under dynamically varying environmental conditions. Simulation results demonstrate effective voltage boosting from 110 V to 150 V and a regulated output of approximately 1100 V at 30 A, with the PV-side current stabilized at 500 A. The fuel cell maintains a steady output of 110 V while its current decreases from 40 A to 25 A, and the battery retains a 60% state-of-charge (SOC) at 120 V output. The hardware prototype, developed using a DSPIC30F4011 microcontroller, achieves an MPPT efficiency of 98.7%, voltage regulation within ± 1.5%, and output power deviation under 2%. Grid voltage and current waveforms exhibit low total harmonic distortion (THD), in compliance with IEEE 519 standards, with measured values of 500 V and 13 A, respectively. The proposed architecture offers enhanced transient response, high energy efficiency, and superior power quality, positioning it as a promising solution for next-generation smart EV charging stations.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".