Advanced regenerative braking system for EVs: Leveraging BLDC‑supercapacitor technologies for optimized energy recovery, economic viability, and maintenance strategies
Bibliographic record
Abstract
Electric vehicles (EVs) offer a pathway to a cleaner and quieter future; however, a considerable portion of their braking energy is still dissipated as heat rather than being recuperated. To address this inefficiency, the present study proposes an advanced regenerative braking architecture that integrates high-power supercapacitors with precision-controlled Brushless DC (BLDC) motors. Employing adaptive control algorithms, the system captures up to 92.5% of kinetic energy during deceleration, directing it first to supercapacitors for rapid storage, then gradually to the primary battery. This dual-stage energy strategy reduces thermal losses, extends battery lifespan, and ensures fast, reliable braking response. The adaptability of the proposed system is validated under various real-world conditions, including urban traffic, highway speeds, and steep inclines. Statistical validation through confidence intervals and error bars reinforces the reliability of the results. A cost-benefit analysis confirms commercial feasibility, highlighting savings in energy consumption, brake wear, and battery replacement within standard service intervals. Additionally, robust safety and maintenance strategies are outlined to ensure operational safety and long-term reliability. By converting wasted kinetic energy into a practical resource, this work lays the foundation for smarter, safer, and more sustainable electric mobility, accelerating the shift toward a truly carbon-neutral transportation future.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".