A hierarchical energy efficiency optimization control strategy for distributed drive electric vehicles
Bibliographic record
Abstract
Distributed drive electric vehicle with four in-wheel motors is widespread with various characteristics, such as performance potentials for independent wheel drive control and energy efficiency. However, in future, one of the biggest obstacles for its success in the automotive industry would be its limited energy storage. This paper proposes a hierarchical control method that involves a high-level motion controller that uses sliding mode control to calculate the total desired force and yaw moment and a low-level allocation controller in which an optimal energy-efficient control allocation scheme is presented to provide optimally distributed torques of four in-wheel motors in all the normal cases. A practicable motor energy efficiency model as a motor actuator is proposed by incorporating the electric motor efficiency map based on measured data into the motor efficiency experiment and a current closed-loop motor model. Moreover, both tracking performance and energy-saving are carried out in this research and evaluated via a co-simulation approach using MATLAB/Simulink and CarSim. A ramp maneuver at a constant speed and New European Driving Cycle and Urban Dynamometer Driving Schedule maneuvers have been conducted. To conclude, it is demonstrated that distributed drive electric vehicle with four in-wheel motors can reduce total power consumption and enhance tracking performance compared with a simple control allocation in which the torques are the fixed ratio distribution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".