Torque Cancelation of Integrated Battery Charger Based on Six-Phase Permanent Magnet Synchronous Motor Drives for Electric Vehicles
Bibliographic record
Abstract
This paper proposes a torque cancellation strategy for a nonisolated three-phase integrated battery charger topology for light and medium duty electric vehicle drives based on six-phase permanent magnet (PM) synchronous machines. The charger requires a three-phase grid interface and utilizes the machine windings as the input filter inductances after minor reconfiguration. The drive power electronics are used to control the currents drawn from the three-phase grid. The current flowing through the machine windings induces a torque on the PM machine rotor which is eliminated by the proposed torque cancellation strategy in battery charging mode. The torque cancellation strategy is general and works with both symmetric and asymmetrically wound six-phase PM machines. The strategy is also capable of eliminating the torque when the rotor is displaced from the stator d-axis. The linear quadratic regulator with the integral action control scheme is used to accommodate for the asymmetry caused by the dependence of winding inductance on the rotor position. Co-simulation results with Finite-Element Analysis and hardware experiments show the topology based on six-phase PM machines can be used to charge or discharge the battery for grid support functions while the torque on the machine shaft remains canceled.
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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) | 0.001 | 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".