Parametric analysis of the potential of energy harvesting from commercial vehicle suspension system
Bibliographic record
Abstract
An accurate estimation of the harvestable energy from a vehicle suspension under typical operating conditions is vital for design and implementation of efficient energy harvesters in vehicles. In this study, a generic three-dimensional model of a commercial vehicle is formulated by integrating nonlinear models of suspension components and tires to determine the harvestable power considering the effects of suspension parameters and road characteristics. The component characteristics of the suspension system and tires are obtained through the reported laboratory-measured data acquired under an extensive range of loading conditions. The vehicle model is subsequently employed to investigate the harvestable energy potential considering variations in the driving speed, chassis load, road waviness and roughness, suspension and tire stiffness, compression mode damping ratio, and asymmetric suspension damping over the most possible ranges of running conditions. The results suggested significant influences of these parameters, while the driving speed, damping asymmetry factor, compression mode damping ratio, and road condition revealed the most pronounced effect on the harvestable power. The results obtained in terms of root mean square and power spectral density of harvestable power are also indicative that rough terrains yield incomparably larger magnitudes of energy dissipation than relatively smooth road classes defined in ISO 8608:1995, and thereby suggestive of the greater potential of energy recovery from commercial vehicles on off-road surfaces.
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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 | 0.000 |
| 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) | 0.001 | 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".