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Record W4410113380 · doi:10.4271/10-09-02-0016

On-the-Market Air Suspension Systems for Passenger Cars

2025· article· en· W4410113380 on OpenAlexaff
Chaochao Ma, Yukun Lu, Ran Zhen, Yegang Liu, Amir Khajepour

Bibliographic record

VenueSAE International journal of vehicle dynamics, stability, and NVH · 2025
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSuspension (topology)Automotive engineeringAir suspensionBusinessEngineeringStructural engineeringMathematicsAxle

Abstract

fetched live from OpenAlex

This article conducts a thorough review of contemporary air suspension systems on the market for passenger cars. The evolution of suspension structures and control methodologies are briefly discussed. The layout of air suspension systems is introduced in detail, with each component receiving a comprehensive description and analysis. The open-loop and closed-loop arrangements are explained. Various types of air springs are discussed and compared. The sensory system, special working conditions, and failure analysis are also elaborated. In the case studies, some example models are listed to show a complete guide of how air suspension is implemented on passenger cars, which includes functionalities, air spring configurations, control methods, signal flow, service modes, and diagnostic messages. The major sources are OEMs’ official websites and previously released documents, such as user manuals and maintenance manuals, which are valid up to April 2023. Finally, the article concludes with a forward-looking discussion on the future application of air suspension.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0120.004

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.

Opus teacher head0.011
GPT teacher head0.262
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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