A Fast Plant-Controller Optimization Process for Mild Hybrid Vehicles
Bibliographic record
Abstract
Hybrid vehicles are an important technology for reducing oil use and transportation-related emissions, and there has been recent renewed interest in mild hybrid powertrains due to their ability to provide moderate fuel savings at a relatively low cost. Simulation plays a major role in the design of hybrid vehicles, but slow simulation run times can sometimes be a limiting factor in the optimization process. This paper proposes a fast script-based optimization process that speeds up optimization iterations by 130 times compared to running a full Simulink model in rapid accelerator mode. This increase in speed can allow larger amounts of real-world data to be used in the design process. To investigate the use of real-world data in the design process, 5400 km of pick-truck driving data is used to optimize one plant and one controller parameter in a mild hybrid powertrain, and the results are compared to the optimal parameters found using three standard drive cycles. It was found that when testing on a 500-km validation dataset, the optimal designs from the UDDS, HWFET, and a created combination cycle led to 2.1%-3.8% higher fuel consumption than the optimal design from the large real-world dataset.
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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 | 0.000 | 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) | 0.000 | 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".