Torque vectoring algorithm for distributed drive electric vehicle considering coordination of stability and economy
Bibliographic record
Abstract
Working of in-wheel motors (IWMs) in high-efficiency areas and minimum tire slip should be considered when driving distributed drive electric vehicles (DDEVs). Therefore, a novel torque vectoring control algorithm is proposed to lower energy dissipation and ensure lateral stability, which consists of a linear quadratic regulator and a proportion integration control module in upper controller to calculate desired additional yaw moment and total driving torque, respectively, for following desired yaw rate, side slip angle, and longitudinal velocity. In addition, the stability objective function considering tire working load and the economic objective function considering working efficiency of IWMs and tire slip energy are established separately in lower controller. The fitness function of coordinating lateral stability and economy is obtained by phase plane method. Particle swarm optimization (PSO) algorithm with a superior initial population (SIP-PSO) is proposed to solve torque distribution coefficients for torque distribution of DDEVs. Finally, simulation and hardware-in-the-loop test results under double lane change and snake lane change maneuvers on lower adhesion road indicate that the proposed algorithm can effectively lower the energy loss of IWM working and tire slip while ensuring lateral stability under different working conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".