A Predictive Energy Management Strategy for Hybrid Electric Powertrain With a Turbocharged Diesel Engine
Bibliographic record
Abstract
This paper proposes an energy management strategy for a hybrid electric vehicle (HEV) with a turbocharged diesel engine. By introducing turbocharger to the HEV powertrain, air path dynamics of engine becomes extremely complex and critical to engine torque response during transient processes. Traditional strategy that adopts steady-state-map based engine model may not work properly in this situation as a result of its incapability of accurately capturing torque response. Thus, in this paper, a physical-law based air path model is utilized to simulate turbo “lag” phenomenon and predict air charge in cylinder. Meanwhile, engine torque boundaries are obtained on the basis of predicted air charge. A receding horizon structure is then implemented in optimal supervisory controller to generate torque split strategy for the HEV. Simulations are conducted for three cases: the first one is rule-based torque-split energy management strategy without optimization; the second one is online optimal control strategy using map-based engine model; and the third one is online optimal control strategy combining air path loop model. The comparison of the results shows that the proposed third method has the best fuel economy of all and demonstrates considerable improvements of fuel saving on the other two methods.
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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) | 0.000 | 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".