Dynamical modeling and optimal state estimation using Kalman-Bucy filter for a seamless two-speed transmission for electric vehicles
Bibliographic record
Abstract
A seamless two-speed transmission that incorporates a two-stage planetary gear set with common sun and common ring gears and two braking mechanisms to control the flow of power is introduced. For an electric vehicle equipped with such a transmission, a detailed dynamical model of the driveline including the half shaft stiffness and longitudinal vehicle dynamics is derived by exploiting the torque balance and virtual work principle. A deterministic Luenberger observer and a stochastic Kalman-Bucy filter are designed to estimate the unmeasured states. These observers estimate the speed of the sun and ring gears and the input and output torques of the transmission based on the measured speeds of the electric motor and the vehicle. Due to nonlinearities in the system such as the longitudinal vehicle dynamics, nonlinear observer methods generally apply for the observer design. However, the nonlinearities are only function of measurable states. Hence, using linear output injection to design an observer results in linear error dynamics. Therefore, the nonlinear observer design problem is transformed into the design of an observer for a linear system. The simulation and experimental results are presented to verify and compare the performance of the deterministic Luenberger estimator with stochastic Kalman-Bucy filter when the system encounters noise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".