Fuel-Optimal Energy Management Strategy for a Power-Split Powertrain via Convex Optimization
Bibliographic record
Abstract
Hybrid power-split powertrains are getting more attention as they present great efficiency and performance. These powertrains connect components by planetary gear sets. Energy management strategy of the powertrain determines the power flow through each component. This paper presents a framework to address the problem of the fuel-optimal control strategy for an energy management strategy of power-split powertrains using convex optimization. To do this, we present a novel model for the power unit and other components using convex equations and constraints. We then use numerical solvers for the convex problems to efficiently solve the optimal control problem. Convex solver shows better performance in considerably less amount of time for the tested driving cycles, compared to the dynamic programming. This makes it suitable for design cases which need to iteratively solve the optimal control problem for different initiations in the design space. Another application is using the presented optimization-based approach to obtain the control input of the powertrain in the real-time applications.
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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".