Modeling, simulation and control of a seamless two-speed automated transmission for electric vehicles
Bibliographic record
Abstract
Power transfer and gear shifting control are the main duties of the transmission in a vehicle. This paper focuses on the modeling, simulation and control of a two speed automated transmission for electric vehicles having a seamless gear shifting specification. The transmission incorporates two-stage planetary gear sets and two braking mechanisms to control the gear shifting. Controlling the input power of the electric motor and the embedded brakes provides seamless flow of power during a gear change. The dynamic model of the mechanism has been developed by using the power and the kinematic equations of the planetary gear trains and the free body diagram of the mechanism. The simulation model has been built up in MATLAB/Simulink®to investigate the performance of the proposed controller. The control algorithm is inspired by the two main control phases in Dual Clutch Transmissions (DCT), namely the torque phase and the inertia phase. An Input Output Feedback Linearization control technique with a PID controller are used for the torque phase and an optimal MIMO H∞controller is designed for the inertia phase. Simulation results show the ability of the proposed transmission with the control algorithm to have a smooth gear change without excessive oscillations in the output torque and speed.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".