Performance Comparison of Permanent Magnet and Electrically Excited Motors for Electric Vehicles
Bibliographic record
Abstract
The objective of this paper is to compare and analyze two types of electric motors, an electrically excited synchronous motor (EESM) and an interior permanent magnet (IPM) motor for an electric vehicle (EV) application.In order to achieve the objective, a comparative analysis of the EESM and IPM motors is presented via the analytic model and finite element method to examine and simulate the torque and power performances as well as their efficiency maps of both motors based on a Volkswagen ID.3 2020 reference model.The analysis takes into account the same machine size and power inverter for both motors.The examination indicates that the EESM achieved better torque and power but lower efficiency, especially at high speeds.The EESM requires flux weakening for a wider constant power range.The EESM can be a low-cost alternative given its adjustable excitation.The EESM has achieved a wider speed range but lower peak efficiency than the IPM.Findings inform optimal propulsion system design for EVs.This research provides valuable insights for automakers seeking to optimize drivetrain performance by selecting suitable electric motor options.The findings contribute to the advancement of next-generation propulsion systems for electric mobility.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".