Investigation of a Six-Phase Interior Permanent Magnet Synchronous Machine for Integrated Charging and Propulsion in EVs
Bibliographic record
Abstract
Merits such as reduced weight, overall and operational costs of the electric vehicle (EV) while providing level 3 charging capability, are propelling research on integrated charging (IC) technology for EVs. Since the same interior permanent magnet synchronous machine (IPMSM) is used during IC and traction conditions, it is important to understand the behavior of the machine during these conditions and optimally design the machine. Hence, firstly, this paper presents a case study on performance of a laboratory 3-phase IPMSM under IC and traction conditions. Thereafter, understanding the challenges such as low magnet operating point, losses and torque oscillation in 3-phase IPMSM during IC, a 6-phase IPMSM with an unconventional configuration is investigated to yield traction characteristics like that of the 3-phase IPMSM and mitigate challenges during IC. In the process, mathematical model of the 6-phase IPMSM is developed employing the dq-axis theory. The developed model is then employed to exclusively derive the relation between various per-unitized machine parameters to obtain optimal performance under IC and traction conditions. Thereafter, a novel bottom-up machine design methodology based on maximum-torque per ampere control technique is proposed to design a 6-phase IPMSM that will yield desired performance under traction and IC conditions. Finally, a 6-phase IPMSM has been designed using the proposed design methodology and its performance has been investigated under traction and IC conditions through a developed prototype and electromagnetic model of the machine in conjunction with finite element analysis.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".