Torque Distribution Prediction for Dual-Motor Electric Vehicle Using Ensemble Learning Algorithms
Bibliographic record
Abstract
The increasing demand for Electric Vehicles (EV s) has made energy efficiency and performance crucial. This paper proposes a Multi-Ensemble Learning-based Energy Management Strategy (EMS) approach for a Dual-Motor Electric Vehicle (DM-EV) to address these challenges. Energetic Macroscopic Representation is used to model the DM-EV, and Matlab/Simulink™ is used to simulate the control. The proposed model is designed with Python programming language and aims to distribute the instant torque between the two electric motors efficiently, minimizing energy consumption in real-time, without prior knowledge of physical parameters. A real-time simulation under an unknown driving cycle was validated using a numerical EV model and achieved promising results while having a significantly lower computational cost compared to existing EMSs. The proposed model shows a high degree of efficiency in predicting and allocating torque, making it a promising solution for efficient energy management in multi-motor EVs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".