Idealized Vehicle Crash Test Pulses for Advanced Batteries
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper reports a study undertaken by the Crash Safety Working Group (CSWG) of the United States Council for Automotive Research (USCAR) to determine generic acceleration pulses for testing and evaluating advanced batteries subjected to inertial loading for application in electric passenger vehicles. These pulses were based on characterizing vehicle acceleration time histories from standard laboratory vehicle crash tests. Crash tested passenger vehicles in the United States vehicle fleet of the model years 2005-2009 were used in this study.</div><div class="htmlview paragraph">Crash test data, in terms of acceleration time histories, were collected from various crash modes conducted by the National Highway Traffic Safety Administration (NHTSA) during their New Car Assessment Program (NCAP) and Federal Motor Vehicle Safety Standards (FMVSS) evaluations, and the Insurance Institute for Highway Safety (IIHS). These crash modes included: Frontal rigid flat barrier test at 35 mph (NHTSA NCAP), 40% offset frontal deformable barrier test at 40 mph (IIHS), Side moving deformable barrier test at 38 mph (NHTSA side NCAP), Side oblique pole test at 20 mph (US FMVSS 214/NHTSA side NCAP), and Rear 70% offset moving deformable barrier impact at 50 mph (US FMVSS 301).</div><div class="htmlview paragraph">The accelerometers used were located in the vehicle where deformation is minimal or non-existent, so that the acceleration represents the “rigid-body” motion of the vehicle. The wide range of variability from vehicle platforms was evident for each of the test modes. The test data were summarized using idealized step-ramp pulses obtained through parametric fit. Two-step Longitudinal and one-step Transverse acceleration test pulses were created based on the raw test data. These idealized vehicle crash test pulses may be used for evaluating the crashworthiness of advanced batteries for passenger vehicle applications.</div></div>
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it