Energy Harvesting From Pneumatic Tires Using Piezoelectric Transducers
Bibliographic record
Abstract
The concept of harvesting energy in our surrounding has recently drawn global attention. Harvesting the ambient energy of the deflected tire and convert it to electricity is discussed in this paper. An Elastic pneumatic tire deflects due to the load it carries. This deflection appears as a contact patch to the road surface. Initially, the concept of the tire deflection will be discussed. This deflection is then related to the wasted energy used for deflection. The dependency of this energy to some important parameters such as the tire air pressure, vehicle speed and tire geometry and forces are primarily discussed. To harvest the deflection energy different well established methods are exists. Due to the tire environment, piezoelectric transducers can serve as the best option. Those transducers are traditionally used to produce mechanical motion due to the applied electrical charges. This material is also capable of generating electrical charges by mechanical motion and deflections. For the tire energy harvesting application, the piezoelectric stacks can be mounted inside a tire structure such that electric charge is generated therein as the wheel assembly moves along a ground surface. For this application, lead-zirconate-titanate (PZT) is selected. The PZT inside the tire is modeled as a cantilever beam vibration in its first mode of vibration. The frequency of vibration is calculated based on the car speed, tire size, and PZT stack length. A mathematical model for this energy harvesting application is derived. Based on this model, the optimum load of the electrical circuit is also found. Finally the amount of energy harvested from tire using PZT is calculated. Although this energy is not significantly high, it will be enough to provide power for wireless sensors applications.
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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".