Enhancement of Vehicle Ride Quality Through Semi-Active Suspension: A Full-Scale Quarter-Car Test Rig Evaluation
Bibliographic record
Abstract
In the pursuit of optimizing automotive suspension systems, a semi-active suspension system (SASS) utilizing continuous skyhook control has been developed to enhance vehicle ride comfort and handling. This system is specifically engineered to mitigate vibrations stemming from high-frequency road excitations. Central to this advancement is the introduction of an electrohydraulic (EH) damper, which is uniquely characterized by solenoid valves capable of adjusting the orifice size to modify damping characteristics. By tuning the damping ratio, the system effectively minimizes the positional oscillations of the sprung mass in response to road irregularities. The dynamic behavior of this damper is comprehensively modeled through a boundary model approach, ensuring precise simulation and prediction of performance. A full-scale quarter-car test platform was constructed to evaluate the dynamic response and the efficacy of various control strategies implemented within the SASS. The performance assessments were conducted using MATLAB Simulink to simulate the behavior of the system under skyhook control algorithms, which aim to maintain the chassis's vertical stability during disturbances. Comparative tests involving a single EH damper have demonstrated a high level of correlation with the simulated models, achieving a 95% agreement level. These findings underscore the capability of the SASS to surpass traditional hydraulic dampers in terms of performance, cost-efficiency, and versatility in testing applications. The insights garnered from this study not only validate the functionality of the proposed system but also contribute significantly to the body of knowledge in vehicle dynamics and control. This research provides a foundational framework for future exploration and potential implementation of advanced damping systems in the automotive industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".