An intelligent energy management strategy for an off‐road plug‐in hybrid electric tractor based on farm operation recognition
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Due to the growing emergence of vehicle electrification, agricultural tractor developers are launching hybrid powertrains in which energy management strategy (EMS) assumes a prominent role. This work mainly aims at developing an EMS for a plug‐in hybrid electric tractor (PHET) to minimise fuel consumption and increase the operating range. The developed off‐road PHET power sources are composed of a biogas‐fuelled Internal Combustion Engine Generator (Bio‐Gen), a photovoltaic system, and a battery pack. To control the power flow among different sources, a two‐layer EMS is formulated. In this regard, initially, the farm operating mode is recognised by means of classification of a working cycle's features. Then, a control strategy based on a multi‐mode fuzzy logic controller (MFLC) is employed to manage the power flow. At each sequence, the classifier identifies the farm operation condition and accordingly activates the relative mode of the MFLC to meet the requested power from the Bio‐Gen. The performance of the proposed EMS has been evaluated based on three real‐world typical agricultural working cycles. The results demonstrate the successful performance of the proposed intelligent EMS under farm conditions by maintaining the energy sources' operation in a high‐efficiency zone which can lead to the extension of the working range and decrease fuel consumption.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it