Bibliographic record
Abstract
This research investigates the characteristics of traction currents of a Citroën C-Zero. The battery pack consists of 88 cells connected in series with a nominal cell capacity of 50Ah. We characterized the amplitude variations, the frequency content, and temporal patterns of traction currents during six driving scenarios: 1. Parking mode: Vehicle stationary with auxiliary systems active 2. Acceleration: High acceleration from standstill to approximately 50km/h 3. Regenerative braking: Controlled deceleration from approximately 50km/h to standstill 4. Constant velocity: Steady--state driving at approximately 25km/h 5. Urban driving: Moderate acceleration and deceleration patterns 6. Dynamic driving: High-performance driving with rapid acceleration and deceleration A Hioki 3275 current clamp, featuring a bandwidth of 2MHz, was connected to the high-voltage cable near to the traction inverter input to record the current. The output signal from the current clamp was measured using a PicoScope 5444D oscilloscope at sample frequencies ranging from 50kHz to 20MHz. Positive currents indicate battery discharge (acceleration), while negative currents represent battery charge (recuperation). Battery-powered traction applications convert electrical energy into mechanical energy for propulsion, e.g. electric vehicles (EVs). A critical component of this systems is the traction inverter, which converts the dc battery voltage into an ac control signal, generating the rotating magnetic field into the electric motor. In EVs, traction inverters operate at switching frequencies ranging from 5 to 20kHz. The frequency of the electric motor control signal depends on the velocity of the EV. As the velocity increases, the motor's mechanical rotational frequency also increases, requiring a higher motor control frequency. Short-time Fourier transform (STFT) analysis revealed the temporal evolution of electrical frequencies below 1 kHz, corresponding to motor control signals that vary with vehicle velocity. The fundamental pulse width modulation (PWM) switching frequency was identified at 7.1 kHz, with associated harmonics and intermodulation products clearly visible in the frequency spectra. For detailed information about data structure, experimental protocols, and data processing methodologies, refer to the README.md file included with the dataset.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".