Energy harvesting from car suspension using a single magnet device
Bibliographic record
Abstract
The need for renewable energy sources and harvesting devices has increased over the years for environmental and economical reasons. Cars, for example, have gone through important transformations over the past decade, which led to the inception of the hybrid type. The idea is to harness some of the dissipated energy and reuse it to operate a vehicle. A lot of energy is dissipated into the environment from the suspension system; therefore, harnessing that energy could be very useful for powering up electrical systems in a car. A magnetic mass-spring system to harvest the vibrational energy dissipated from the car’s suspension system that maximizes electricity generation and minimizes ride discomfort is presented in this work. The comfort level for the passenger via three dimensionless indices, including a regenerated electricity transmissibility, displacement transmissibility performance, and ride comfort performance, is introduced. To maximize the regenerated electricity indicator and minimize the discomfort indices, a multi-objective function based on the above indices and three weighted factors in conjunction with the Simulated Annealing method is used to obtain the optimal physical parameters for the harvesting device. The theoretical developments are demonstrated under constant and varied driving speeds, and the simulation results show that the energy harvester is capable of producing reasonable amounts of electricity while maintaining a good comfort level. In fact, it was shown that the harvester can generate 0.045 V when the car travels at 60 km/h with an acceleration of 0.43 m/s2 and assumed base excitation amplitude of 0.05 m. Higher voltages were achievable with larger base excitation amplitudes and (or) accelerations.
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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".