An intelligent energy management strategy for an off‐road plug‐in hybrid electric tractor based on farm operation recognition
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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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".