Artificial neural network based adaptive control for plug-in hybrid electric vehicles
Bibliographic record
Abstract
Plug-in hybrid electric vehicles (PHEV) have become vital for oil consumption reduction. They have not achieved their maximum potential due to control strategy limitations. Existing controllers are often tuned to achieve the best fuel economy for specific conditions. It is impractical to optimise a controller for every scenario. A control strategy for PHEVs using artificial neural networks (ANN) is presented. The advantages of implementing a controller using ANN include independence from drive cycle or user, precision and robustness, and updatable training set. Existing PHEV control strategies are used to model a base for city and highway driving. Simulation data was extracted to form an ANN training set, which was used to develop a new strategy that was better than existing ones. The controller was validated using different drive cycles. Furthermore, the sensitivity of ANN controllers is presented. The controller is also used to validate the charge depleting mode of PHEVs.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".