Fast Online-Implementable Sub-Optimal Energy Management of a Power-Split Hybrid Electric Vehicle
Bibliographic record
Abstract
The current focus of HEV controller design is on the development of real-time implementable energy management strategies that can approximate the global optimal solution closely. In this work, the Toyota Prius power-split hybrid powertrain is used as a case study for developing online energy management strategy for hybrid electric vehicle. The power-split hybrid powertrain combines the advantages of both the series and parallel hybrid powertrain and has been appealing to the auto-makers in the past years. The addition of two additional electric machines and a Planetary Gear Sets (PGS) allows more flexibility in terms of control at some cost of complexity. A forward-looking dynamic model of the power-split powertrain system is developed and implemented in Simulink first. An optimal control problem is formulated, which is further reduced to an optimal control problem with a single-variable objective function and a single-state subject to both dynamic constraint and boundary constraint. The reduced optimal control problem is then solved by an on-line (real-time) implementable approach based on Pontryagin’s Minimum Principal (PMP), where the costate p is adapted based on SOC feedback. Simulation results on standard driving cycles are compared using the proposed optimal control strategy and a rule-based control strategy. The results have shown significant improvement in fuel economy comparing to the baseline vehicle model, and the proposed online (real-time) PMP control algorithm with an adaptive costate p is very close to the optimal PMP solution with a constant costate. The proposed optimal control has a fast computation speed and calculates the optimal decision “dynamically” without the necessity of knowing future driving cycle information and can be practically implemented in real-time.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".