Direct voltage MTPA control of interior permanent magnet synchronous motor driven electric vehicles
Bibliographic record
Abstract
An efficient, straightforward, direct voltage maximum torque per ampere (MTPA) control schemefor interior permanent magnet synchronous motor (IPMSM) propelling an electric vehicle (EV) isproposed in this manuscript. The main feature of the proposed traction control scheme is that theMTPA is attained by directly varying the amplitude and the angle of the voltage vector, eliminatingthe need for current control loops and the associated regulators. Instead, a single-speed controller isadopted. Furthermore, an analytical formulation based on the motor voltage model is developed toextract the desired voltage’s magnitude and angle to run the motor within the MTPA operating points,disregarding numerical solutions, control law approximation, long-winded iterative calculations,or approximate representation of the IPMSM. Such a methodology significantly reduces controlscheme complexity, enhances computation efficiency, and mitigates the delays associated with thecascaded-based control systems. Additionally, it facilitates straightforward real-time implementation.The performance of the designed algorithm is experimentally validated using the commonly adopteddriving cycles, namely the Federal Test Procedure (US06) drive cycle and the New European DrivingCycle (NEDC). An intensive comparative evaluation against the MTPA field-oriented control (FOC)is established based on the driving cycles employed. Nevertheless, a quantitative assessment isconducted, revealing that the designed strategy achieved an average speed tracking improvement of14% and energy savings of 2.26% over the MTPA FOC. Also, the proposed method’s computationefficiency is investigated and compared to both the FOC and existing direct voltage approaches,demonstrating superior computational efficiency.
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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".