Experimental Characterization and Modeling of a YASA P400 Axial Flux PM Traction Machine for Performance Analysis of a Chevy Bolt EV
Bibliographic record
Abstract
This paper investigates off-the shelf performance traction machine, a yokeless and segmented armature (YASA) axial flux surface permanent magnet machine, model P400HC from YASA Motors. A series of manual measurements and automated dynamometer tests were performed at various conditions. From these tests parameters are determined including friction and windage torque, phase resistance, permanent magnet flux linkage, and inductance. The efficiency, phase current, phase voltage, and power factor of the machine was measured over a wide torque, speed, and dc bus voltage range up to around the maximum ratings given by the manufacturer. A range of d-q current values were tested, showing that the machine is slightly salient since maximum torque is achieved when including a small amount of d-axis current. A Chevrolet Bolt electric vehicle (EV) was modeled with the YASA Motors machine and the stock Bolt EV machine. Over four different vehicle drive cycles, the YASA Motors machine was shown to be considerably more lossy than the Bolt EV machine, thereby achieving 4% to 6% less range. The higher loss of the YASA Motors machine likely has several causes, including higher phase resistance, significant friction, windage, and no-load iron losses, and the fact that Bolt EV machine was heavily optimized for an EV application while the YASA Motors machine was optimized to be a highly power dense more general-purpose machine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".