An Integrated Modeling Framework for Motion Control and Energy Management in Multi-Motor Electric Vehicles
Bibliographic record
Abstract
Multi-motor electric vehicles (MMEVs) present complex challenges for control and optimization due to the distribution of control actions and state variables across multiple subsystems and hierarchical levels. Although electric vehicle (EV) modeling has been widely studied, accurately capturing and optimizing the longitudinal energy efficiency and dynamic performance of MMEVs remains a significant challenge. This complexity is further increased by the presence of different motor types, such as induction motors (IMs) and permanent magnet synchronous motors (PMSMs), and various mechanical configurations in all-wheel drive systems. To address these issues, this paper proposes a global-local modeling framework that extends the Energetic Macroscopic Representation (EMR) methodology. The framework integrates detailed models of the electrical drive system with comprehensive mechanical subsystem modeling, including gearbox, differential, half-shafts, wheels, and tires. A global input power model links local control actions and state variables to overall energy flow, supporting a unified approach to longitudinal motion control and energy optimization. In contrast to conventional EMR-based models, the proposed framework explicitly incorporates driveline and tire dynamics, which significantly affect energy consumption due to drivetrain losses and tire slip. The model is evaluated through two scenarios that assess the effects of drivetrain modeling and force distribution strategies. The results show improved control system performance and enhanced energy efficiency, supporting future advancements in longitudinal dynamics modeling for MMEV.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".