Real-Time Multi-Ensemble Learning-Based Power Prediction for Energy Management of Dual-Source Electric Vehicles
Bibliographic record
Abstract
Electric vehicles (EVs) rely on batteries as their main energy source, so it is important to take steps to reduce their stress to extend their life and maintain their performance. Combining supercapacitors with batteries can be a promising way to reduce battery stress, while also improving global EV performance. This paper proposes a novel multi-ensemble learning (MEL)-based method for designing energy management strategies (called MEL-EMSs) for dual-source EVs (DSEVs), i.e., EVs using a battery and a supercapacitor. A designed MEL-EMS determines in real-time a performant power distribution. Such power distribution is determined solely based on the history of the speed and traction power, without requiring any prior knowledge of the complete driving cycle. MEL-EMS is evaluated at two levels, to assess power distribution and energy consumption respectively. The real-time simulation is carried out under unknown driving cycles based on a validated numerical EV model. Our results demonstrate that MEL-EMS outperforms state-of-the-art methods by enhancing energy efficiency, reducing battery stress with a 4.8% reduction in battery root-mean-square current and a 41.7% decrease in standard deviation of battery current, while ensuring accurate and low-cost real-time power distribution.
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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 | 0.001 | 0.001 |
| 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".