Shifting quality analysis of unmanned tractor equipped with series hydro-mechanical transmission
Bibliographic record
Abstract
The series hydro-mechanical transmission (HMT) can improve the power performance and fuel economy of unmanned tractors at a lower cost. However, its shifting impact problem needs to be solved before it can be applied in practice. In this study, the control parameters of the HMT power-shift system were optimized from the perspectives of energy loss and driving comfort to improve the shifting quality of the transmission. First, the powertrain of the HMT was introduced. Second, a shifting dynamics model of the HMT was established and experimentally validated. Third, based on the single-factor simulation results, the influence of each factor on the shifting quality of the tractor was analyzed. Finally, a matching strategy for shifting parameters was proposed, and related simulation analysis was conducted on the shifting quality under plowing conditions. Compared to the standard parameters, the results show that the optimized shift parameters reduce the sliding friction work of the clutch under 9 operating conditions as well as the peak acceleration of the tractor under 12 operating conditions. In summary, the parameter-matching method proposed in this study was effective and provided theoretical and methodological support for the development of HMT and its control system for unmanned tractors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".