Self-Adaptive Fuzzy Logic Controlled Buck– Boost Converter for Efficient EV Battery Charging
Bibliographic record
Abstract
A Self-Adaptive Fuzzy Logic Controlled Buck–Boost Converter (SA-FLC BBC) designed to improve voltage regulation, transient response, and charging efficiency in electric vehicle (EV) battery charging systems. Conventional PI controllers and fixed fuzzy logic structures often fail to maintain stable operation under nonlinear load and variable input voltage conditions. To overcome these limitations, a Mamdani-type adaptive fuzzy logic controller is developed with a dynamic gain-tuning mechanism that automatically adjusts the output scaling factor based on real-time error and change in error signals. The control algorithm modifies the PWM duty cycle to maintain a constant charging voltage despite disturbances or sudden load variations. The proposed system is modeled and simulated in MATLAB/Simulink 2021a, and the results demonstrate substantial performance improvement over conventional control methods. The adaptive fuzzy controller achieved a settling time of 0.12 s, steady-state error of 0.8%, and maximum overshoot of 7.8%, showing 29.4% faster response and 34% ripple reduction compared to a PI-controlled converter. Furthermore, converter efficiency improved by approximately 4%, ensuring stable 48 V output under variable operating conditions. The proposed SA-FLC approach offers a simple, robust, and intelligent solution for next-generation smart and renewable-integrated EV charging systems.
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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) | 0.002 | 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".