ENERGY HARVESTING AND CONTROL OF A REGENERATIVE SUSPENSION SYSTEM USING SWITCHED MODE CONVERTERS
Bibliographic record
Abstract
Harvesting road induced vibration energy through electromagnetic suspension allows extension of the travel range of hybrid and fully electrical powered vehicles while achieving passenger comfort. The core of this work is to investigate development of power converters for an electromagnetic suspension system which allows for regeneration of vibration energy and dynamics control of vehicle suspension. We present a variable electrical damper mechanism which can be controlled using unity power-factor AC/DC converter topologies. By controlling the synthesized electrical damper, the system is capable of providing variable damping forces, ranging from under-damped to over-damped cases, while regenerating mechanical vibration energy into electric charge stored in a battery. To demonstate the concept, the developed converter is attached to a small-scale one-degree-of-freedom suspension prototype which emulates a vehicle suspension mechanism. The energy regeneration mechanism consists of a mass-spring system and a ball-screw motion converter mechanism coupled to a DC machine, excited by a hydraulic shaker. The motion converter stage converts vibrational motion into a bi-directional rotatory motion, resulting in generation of back-emf in the rotary machine. We also introduce an optimized start/stop algorithm for the harvesting of energy using the proposed power converter. The algorithm allows for improvements in power conversion efficiency enhancement (≈ 14% under class C road profile) through turning the circuit on/off during its operation. The idea is to ensure that the converter only operates in the positive conversion efficiency region; meaning that when there is enough energy the converter starts the energy harvesting process. Furthermore, an estimation of range enhancement for a full-scale electric vehicle (EV) is furnished using regenerative suspension. It is estimated that for a full size EV (e.g., Tesla model S), a range extension of 10-30% is highly realistic, depending on the road conditions.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".