Electric Multi-Motor Drives with Improved Induction Machine for Agricultural Wide-Span Implement Carrier (WSIC)
Bibliographic record
Abstract
Abstract. Agricultural wide-span implement carriers (WSIC) are machines specially adapted to the controlled-traffic farming field management system. The agricultural WSIC requires many main and auxiliary drives that can be controlled separately. Electric drives are highly efficient, cleaner and more environmentally friendly for actuation compared to other power sources such as hydraulics especially with the recent improvement in power to weight ratio. This paper reviews the relative merits of electric motor and drives systems currently in use in electric vehicle systems and evaluates the possibility of employing induction machines for agricultural WSIC (tractors and implements). The electric components for the implement part include two induction electric motors, a Permanent Magnet (PM) electric motor, and an electric stepper motor with a closed loop speed, and torque control. The electric components for the tractors include a generator and its controllers, a rectifier, inverters, and an appropriate power interface. To enhance the performance of the employed induction machine, a MATLAB simulation and a typical experimental test have been carried out to compare an off-the-shelf Squirrel Cage Induction (SCI) machine with another one of the same type but employing an auxiliary winding. The new SCI machine will be called “modified” in the rest of the paper. The results show significant improvement in the performance of the modified machine with a power factor of almost 0.99, a decrease in losses of 27% and a noticeable reduction of in-rush current.
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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) | 0.004 | 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".