Real-Time Modeling of Electric Machines for Hardware-in-the-Loop Environment: A State-of-the-Art Review
Bibliographic record
Abstract
Hardware-in-the-loop (HIL) simulation enables real-time closed-loop testing of electric drive systems by interfacing machine models with physical controllers. This paper presents a review of real-time modeling approaches for electrical machines in HIL environments. Nonlinearities such as magnetic saturation, cogging torque, spatial harmonics, and thermal effects are examined in the context of analytical, finite element analysis (FEA) based methods, and lookup table driven models. The reviewalso evaluates real-time hardware platforms in terms of execution latency, model compatibility, and integration workflows. A comparative analysis highlights that while induction machine and permanent magnet synchronous machine models are extensively validated, switched reluctance machine (SRM) modeling efforts remain limited, particularly in capturing multiphysics behavior. The reviewexplores the current limitations inSRMmodeling and motivates the development of high-fidelity, multiphysics approaches compatible with real-time HIL simulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".