Real-Time Torque-Distribution for Dual-Motor Off-Road Vehicle Using Machine Learning Approach
Bibliographic record
Abstract
Recently, demand for electric vehicles (EVs) has increased significantly as people are becoming more conscious of the environment and the need to reduce carbon emissions. The introduction of multi-motor systems in EVs has brought new challenges in terms of energy efficiency and performance. This paper presents a Multi-Ensemble Learning (MEL)-based approach to design an Energy Management Strategy (EMS) for a Dual Motor Electric Vehicle (DMEV) where MEL is a new powerful Machine Learning approach implemented using Python programming language. To make our study concrete, we studied a real DMEV that is modeled using Energetic Macroscopic Representation and whose control is simulated using Matlab/SimulinTM. The designed EMS aims to distribute the instant torque between the two electric motors in an efficient manner, with the objective of minimizing energy consumption as much as possible. Contrary to existing EMSs, an important advantage of our designed EMS is that it determines the instant torque distribution in real-time (while the vehicle is running), without knowing in advance how physical parameters (such as the speed and traction force) will evolve during the current trip. The real-time simulation is carried out under unknown driving cycles based on a validated numerical EV model with a significantly lower computational cost while achieving a high degree of accuracy in predicting and allocating torque, and a high degree of performance in terms of energy consumption.
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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) | 0.000 | 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) | 0.001 | 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".