Parameter Identification for a Longitudinal Dynamics Model Based on Road Tests of an Electric Vehicle
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a method to estimate the parameters of a longitudinal dynamic model using on-road testing of an electric vehicle. Data acquisition was undertaken on our test vehicle, a Toyota Rav4EV 2012, by collating signals from three different sources: Vehicle Measurement System (VMS) (consisting of wheel force, torque, wheel spin, wheel speed and position sensors), Global Positioning System (GPS) and the Controller Area Network (CAN) of the vehicle. A MATLAB/Simulink based non-linear least square parameter estimation algorithm was used to identify the vehicle parameters including the mass, location of center of gravity, frontal area, coefficient of drag, wheel inertia and road load parameters of the vehicle. A 14 degrees of freedom (DOF), longitudinal dynamics model of the Rav4EV was developed in the MapleSim software using the estimated parameters. The accuracy of the identified parameters and the model was validated by comparing the model output against the experimental data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 it