MPC-Based Energy Management Strategy for an Autonomous Hybrid Electric Vehicle
Bibliographic record
Abstract
Despite the current intense research on each of the subjects of electrification and autonomous driving, potential advantages as a result of the interaction of these two mainstreams in automotive have not been effectively studied yet. Autonomous vehicles generate an unprecedented amount of real-time data due to excessive use of perception sensors and processing units. In this article, we present a novel approach for improving the fuel economy of an autonomous hybrid electric vehicle by taking advantage of this qrydata. We introduce the term of autonomous-specific energy management strategy (ASEMS) and we present an example of such a strategy using model predictive control (MPC). Specifically, we show how a more fuel-optimal energy management strategy (EMS) can be achieved for the power-split powertrain of an autonomous hybrid electric vehicle using the motion planning data. We use an optimization-based motion planning approach and feed the resulting velocity profile up to the prediction horizon to the MPC-based EMS. The presented approach shows 2% to 12.81% less fuel consumption for the two extreme cases of 100 and 1000 meters as the prediction horizons, compared to a rule-based EMS. The presented EMS fuel-optimality for the 1000 meters is only 6.91% sub-optimal compared to the globally optimal results of dynamic programming.
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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) | 0.001 | 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) | 0.001 | 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".