Comparative Study of Embedded Energy Management Methods Based on Machine Learning for Dual-Source Electric Vehicles
Bibliographic record
Abstract
In the actual context of dual-source electric vehicles (DSEVs), efficient energy management strategies (EMSs) are essential to optimize energy distribution between batteries and supercapacitors. However, achieving real-time decision-making and adaptability in resource-constrained environments remains a significant challenge. This study addresses these challenges by comparing two advanced machine learning (ML)-based EMSs: Driving Mode Prediction-based EMS (DMP-EMS) and Multi-Ensemble Learning-based EMS (MEL-EMS). Using Signal Hardware-In-the-Loop simulation and a Raspberry Pi 4 platform, the two EMSs are evaluated based on various criteria, including performance accuracy, model complexity, training and prediction times, required computing resources, scalability, and energy consumption. The findings highlight the trade-offs between these EMSs, offering insights into designing tailored EMSs for DSEVs under varying operational demands. While MEL-EMS excels in environments where prediction accuracy is paramount since it offers superior predictive performance suitable for applications demanding high accuracy and scalability, DMP-EMS provides a more efficient and practical solution for real-time energy management in resource-constrained settings.
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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 | 0.002 |
| 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".