Vehicle-Level Modeling and Analysis of Onboard Energy Harvesters and Their Impact on Dynamics
Bibliographic record
Abstract
Abstract Integrating energy harvesters into rail transit systems, such as vehicles, offers a sustainable power solution for onboard sensors, yet their impact on vehicle dynamics remains largely underexplored. This study proposes an approach that combines vehicle dynamics, energy harvesters, and interface circuits to thoroughly analyze bidirectional complex coupling effects between the subsystems. An equivalent circuit model (ECM) is developed by employing the mechanical-electrical analogy theory to simulate vehicle dynamics, and its accuracy is verified through comparison with a traditional dynamic model. Subsequently, electromagnetic and piezoelectric harvesters were integrated into the model, along with power-boosting interface circuits, to assess the combined effects. The energy-harvesting performance and the vehicle dynamic behaviors under harmonic and random excitations were investigated. Results indicated that the power-boosting interface circuits can significantly enhance energy harvesting efficiency and stabilize power output under different excitation conditions. However, the installation of energy harvesters deteriorates vehicle dynamics noticeably, and the power-boosting interface circuits further exacerbate these effects, particularly at low frequencies. In general, the proposed ECM presents a comprehensive method for the design and optimization of onboard energy harvesters and their interface circuits, offering insights from a system-level analysis for rail transit 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".