Estimation of the State Variables and Unknown Input of a Two-Speed Electric Vehicle Driveline Using Fading-Memory Kalman Filter
Bibliographic record
Abstract
This paper studies the stochastic estimation of unavailable state variables and the unknown input of an electric vehicle (EV) driveline equipped with a novel seamless clutchless two-speed transmission. The proposed transmission is explained and the kinematics and dynamics of the driveline, which constitute the basis for the observer design, are presented. For identical inputs, the outputs of the dynamical model are compared to those of the experimental test rig and the simulation model created in the MATLAB/Simulink. The method of modeling the unknown input as a fictitious state variable is combined with the fading-memory Kalman filter (FMKF) in order to provide a robust concurrent estimation of unavailable states and the unknown input. The observer estimates angular velocities of the off-going and on-coming gears and consequently the gear ratio, the input and output torques of the transmission, and the unknown torque exerted on the vehicle based on the speed measurements of the electric motor and wheels. The observability of the states and unknown input of the augmented system is analyzed and the performance of the proposed observer is experimentally assessed for upshift and downshift scenarios. The estimation results are compared with the conventional KF and the deterministic Luenberger observer (DLO).
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".