High-Accuracy Torque Control and Estimation for Interior Permanent Magnet Synchronous Machine Drives with Loss Minimization
Bibliographic record
Abstract
This thesis studies the high-accuracy torque control for the interior permanent magnet synchronous machine (IPMSM) drives with loss minimization. A nonlinear flux-linkage model for the IPMSM with twelve coefficients is proposed. It can generally be used to estimate the d-axis flux-linkage, q-axis flux-linkage, MTPA locus, and torque without the information of the machine known, such as the geometry and material of the permanent magnet. The new torque equation and MTPA condition are derived from the proposed flux-linkage model. An optimization problem is formulated to find the appropriate factors for the proposed model based on the measured flux-linkage data at only nine specific operating points. No selection of weight factors is required in the cost function. The desired copper-loss minimization control can be achieved and good torque estimate can be achieved in real-time. A novel model for IPMSM drives with all the losses considered is proposed. The model-based loss minimization control (LMC) algorithm with respect to motor current contributing to the flux-linkage generation is presented. The analytical solution to the optimization problem is provided. Based on the proposed IPMSM drive model, LMC with respect to the winding current in wide speed range is studied as well. The optimality is proved. The influences of the stator resistance, the equivalent inverter-loss and core-loss resistance in the proposed circuit are researched. Compared to maximum torque per ampere (MTPA) control, LMC introduces more efficient energy utilization. Due to the nonlinearities, the characteristics of the inverter loss, the core loss, the mechanical loss, the d- and q-axis flux-linkage profiles of the IPMSM drive system are researched. The process of the parameters’ characterization with respect to the speed, the d- and q-axis current is stated. The back-fitting based torque estimation technique is proposed, which eliminates the necessity of the manufacture of the dummy rotor. The separation of the core loss and mechanical loss is not required for the calculation. The accuracy of the prediction of the voltage limit ellipse based on the proposed model is enhanced compared to the conventional method. The torque control system for the IPMSM drives is designed, aiming at accurate motor torque control, high efficiency, and fast dynamic response performance. 2004 Prius IPMSM and one prototype motor are used to validate the proposed algorithms for the parameters’ characterization, torque estimation, and loss minimization control.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".