Fuzzy‐based blended control for the energy management of a parallel plug‐in hybrid electric vehicle
Bibliographic record
Abstract
The growing interest in reducing fuel consumption and gas emissions provides an incentive for the automotive industry to innovate in the field of hybrid electric vehicles (HEV) and plug‐in hybrid electric vehicles (PHEV). The two embedded power sources in these vehicles require an intelligent controller in order to make the best decision on the power distribution. Actually these controllers, often called energy management systems, are very important and greatly influence the achievable fuel economy. Compared with an HEV, a PHEV allows battery discharge over a complete trip. As a consequence the optimal control of a PHEV implies a stronger dependence on the total driving cycle. Many authors have studied the possibility of fuzzy‐based systems for both HEV and PHEV as they have proved to be robust, reliable and simple. However, classical fuzzy rule‐based strategies demonstrate a lack of optimality because their design is focused on the actual vehicle state rather than the driving conditions. This study proposes a blended control strategy based on fuzzy logic for a PHEV. The proposed controller is fed with driving condition information in order to increase the controller effectiveness in every situation. The efficiency of the proposed controller is demonstrated through simulations.
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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 | 0.001 | 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".