An Optimal Permanent Magnet Motor for Pumps in Electric/Hybrid Vehicles: Design and Experimentation
Bibliographic record
Abstract
This paper presents the optimal design of a 1/2 HP Surface-Mounted Permanent Magnet Motor (SMPM). The motor complies with the latest department of Energy (DoE) standards for water pump applications, released in January 2020. A design procedure is performed to optimize the cogging torque and total harmonic distortion (THD), through parametric analysis. The design is executed using the ANSOFT Maxwell 3D software and proven using the lab pump test rig. The proposed design aims to produce a back-EMF close to a sinusoidal waveform, maximize the power density and reduce the cogging torque. To attain this sinusoidal back-EMF along with a reduced cogging torque, techniques like optimizing the skewing angle and modifying the windings configuration have been used. To decrease the harmonics in the back EMF, the pole pitch is calculated such that it eliminates the significant harmonics to achieve high power factor. The proposed SMPM based pump cost is${\$}$72, which is less than the current price of the induction machine-based pump of${\$}$113 for the same power rating. The test rig readings prove the proposed design has a better working efficiency of 2% compared to the best market pump circulators with an excellent power factor at its rated load.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".