A new system configuration design and power management strategies for a multi-source hybrid truck
Bibliographic record
Abstract
In this article, a novel system configuration with multiple energy sources is proposed for a hybrid truck in order to reduce fuel consumption and overcome the drawbacks of using a single energy source. The energy-saving characteristics of the hybrid system can be displayed after analyzing its system structure and performances. In order to validate the advantages of this presented system, the dynamic models of the system components are established in a MATLAB/Simulink environment, and initial and improved power management strategies with rule-based algorithms are developed. Then, the hybrid system is simulated based on the models and control strategies over the urban dynamometer driving schedule driving cycle. The simulation results show that the fuel consumption employing the initial power management strategy is 12.49 L/100 km, and there is a significant decrease with around 13.6% based on the improved strategy. The results also verify that the better fuel economy can be achieved by the proposed multi-source system compared to the counterparts under the same operating conditions.
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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 | 0.000 | 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 | 0.000 |
| 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".