Design & Development of On-Board DC Fast Chargers for E-Rickshaw
Bibliographic record
Abstract
This paper proposes a single-phase 3.3 kW onboard battery charger for E-Rickshaw with a Single-stage Active Power Factor Correction (PFC) Buck-Boost Converter topology. Normally, E-Rickshaw battery charger is heavy and bulky in dimensions and comprises of a power-factor corrector (PFC) and a DC-DC converter. A traditional single-phase PFC converter has three sensors namely input voltage, input current, and output voltage for measurement. The proposed converter is more compact and more flexible in terms of control and conversion stages a with new Unity Power Factor alongside a combination of a full-bridge diode rectifier and a single switch Buck-Boost converter by just using one output voltage sensor is presented. The suggested control technique uses only one integrator to compensate for the steady-state error and is very simple to implement. The proposed battery charger accomplish Unity Power Factor at variable AC input with a small inductance. To verify the feasibility of the proposed scheme, the proposed battery charger topology is simulated in PSIM. The simulation result indicates a 95% peak efficiency at 220Vac and 93% at a variable input voltage from 170V-280V at 3.3kW. A scaled-down lab prototype of 1.5kW is built and experimental results are presented to verify the suitability of the proposed converter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".