A Single-Phase Integrated On-Board Charger With Minimal Current Ripple for Electric Vehicles Having at Least One Motor
Bibliographic record
Abstract
This paper presents a novel and improved integrated EV charger topology that combines motor control and battery charging into a single circuit to simplify the system’s architecture. Unlike the traditional EV chargers that use interleaved boost converters and inductors to perform the charging functionalities, this new approach uses the vehicle’s motor as a coupled inductor. In addition, the inverter used to control the motor during propulsion is also used as an interleaved power factor correction (PFC) boost converter to charge the EV battery. With little to no hardware modifications, dual functionality is made possible by the motor's built-in capabilities, which ensure optimal power flow during the battery charging stage. This paper thoroughly examines the modeling and operational modes of the proposed charger. Mathematical analysis and simulations are also done on the motor to ensure the charger generates zero torque during charging mode. Furthermore, the performance of the proposed charger is evaluated, a controller is developed, and detailed simulation and experimental results are provided on a 3-kW charger. The research findings proved that the proposed charger is robust with a 93.9% experimental efficiency and a THD of 2.23%.
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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) | 0.001 | 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 | 0.001 |
| 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".