Real-Time Multiobjective Energy Management for Electrified Powertrains: A Convex Optimization-Driven Predictive Approach
Bibliographic record
Abstract
Energy management is an important technology for maximizing the energy efficiency of hybrid vehicles. In the process of developing cost-optimal powertrain control strategies, existing studies have explored the interactions between fuel economy and battery degradation to prolong the battery service period and reduce fuel consumption simultaneously. However, computational efficiency will be sacrificed in order to search optimal control sequences due to the nonlinear powertrain model and many optimization variables in the optimal control problem, which hinders the implementation in real-time applications. To this end, this article combines second-order cone programming and model predictive control algorithms to formulate a computationally efficient energy management strategy for a series hybrid electric vehicle. Specifically, three main contributions are made which distinguish our work from existing studies. First, based on the constructed convex powertrain model, two objectives, fuel economy and battery degradation, are optimized by the proposed hybrid algorithm. Second, a comparison study that compares the strategies with and without battery degradation optimization is presented to validate the effectiveness of the proposed control strategy. Finally, by changing the size of the prediction horizon, several simulation results are discussed to evaluate the computational efficiency of the devised method. Furthermore, the effects of different battery and fuel prices on optimized results are analyzed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".