Adaptive Hierarchical Energy Management Design for a Plug-In Hybrid Electric Vehicle
Bibliographic record
Abstract
To promote the real-time application of the advanced energy management system in hybrid electric vehicles (HEVs), this paper proposes an adaptive hierarchical energy management strategy for a plug-in HEV. In this paper, deep learning (DL) and genetic algorithm (GA) are synthesized to derive the power split controls between the battery and internal combustion engine. First, the architecture of the multimode powertrain is founded, wherein the particular control actions, state variables, and optimization objective are explained. Then, the hierarchical framework for control actions generation is introduced. GA is utilized to search the global optimal controls based on the powertrain model provided in MATLAB/Simulink. DL is applied to train the neural network model that is connecting the inputs and control actions. Finally, the effectiveness of the presented integrated energy management strategy is validated via comparing with the original charge depleting/charge sustaining policy. Simulation results indicate that the proposed technique can highly improve the fuel economy. Furthermore, a hardware-in-the-loop is conducted to evaluate the adaptive and real-time characteristics of the designed energy management system.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".