Efficiency Analysis of Induction Motor Control Strategies Using a System-Level EV Model
Bibliographic record
Abstract
This paper presents a comparative study on the effect of two commonly used induction motor (IM) control strategies on motor and inverter efficiency of battery electric vehicles over standard drive cycles. An electric vehicle (EV) model is created for the 2015 Chevrolet Spark EV and verified using experimental data. After model verification, the Spark permanent magnet synchronous motor is replaced with a detailed IM and controller model. Simulation results for both Field Oriented Control (FOC) with constant rated flux and Maximum Torque Per Ampere (MTPA) control over a test drive cycle are given to validate the good tracking capability of IM current and speed controllers. The effect of control method and drive cycle on motor and inverter efficiency is illustrated by comparing efficiency calculation results for three standard drive cycles (UDDS, HWFET and US06).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".