Decoupling Control Strategy of IPT System Based on Optimal Efficiency Load Tracking
Bibliographic record
Abstract
The inductive power transmission system is applied to urban rail transit. Due to the limitations of the volume and coupling coefficient of the inductive coupling mechanism and the fact that the fluctuation of air gap in its movement will cause the fluctuation of mutual inductance value, DCDC booster link should be added to the side, rectifying side, to improve the output voltage level and stability. At present, most of the existing control strategies are based on the original side information communication. However, in the application of dynamic wireless charging in urban rail transit, the primary and secondary side coils are in the process of relative movement, so it is relatively difficult to establish reliable real-time communication, and it is easy to be interfered by electromagnetic transmission process, resulting in large errors. This paper analyzes the relationship between load and efficiency of IPT system applied to urban rail transit in detail and obtains the optimal load matching strategy of optimal efficiency. At the same time, an independent control strategy is proposed to eliminate the information communication of the primary and secondary sides and realize decoupling control. Finally, a simulation model is built to verify the effectiveness of the control strategy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".