Vehicle Dynamics Estimation and Fault Monitoring

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 978

Special Issue Editor


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Guest Editor
Mechanical Engineering Department, Advanced Vehicle Dynamics and Mechatronic Systems (VEDYMEC), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
Interests: intelligent transportation systems; vehicle dynamics; vehicle safety; vehicle control; road vehicles
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Special Issue Information

Dear Colleagues,

The increasing complexity of modern vehicles, driven by advancements in electrification, connectivity, and autonomy, demands robust and intelligent systems for understanding vehicle behavior and ensuring operational safety. At the core of this challenge lies the need for the accurate estimation of vehicle dynamics and real-time fault monitoring.

This Special Issue aims to gather the latest research focused on methodologies for estimating vehicle states and monitoring system faults. The accurate estimation of key parameters, such as lateral velocity, sideslip angle, roll angle, tire-road friction, etc., allows for improved vehicle control and driver decision-making. At the same time, effective fault detection, isolation, and estimation enhances system reliability by detecting anomalies in sensors, actuators, or control logic before they lead to failures.

We invite original contributions addressing both model-based and data-driven approaches with relevance for conventional, electric, and autonomous vehicles. Submissions may include theoretical advancements, algorithm developments, simulation studies, or experimental validations.

Topics of interest include (but are not limited to) the following:

  • Vehicle state and parameter estimation;
  • Tire–road friction and force estimation;
  • Sensor and actuator fault monitoring;
  • Sensor fusion for dynamics and health monitoring;
  • Data-driven vs. model-based monitoring approaches;
  • Fault-tolerant estimation and control;
  • Anomaly detection in connected and autonomous vehicles;
  • Validation techniques, datasets, and benchmarks.

Dr. Fernando Viadero-Monasterio
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vehicle state estimation 
  • fault monitoring 
  • health monitoring 
  • fault-tolerant control 
  • tire-road friction estimation 
  • data-driven approaches 
  • connected vehicles 
  • autonomous vehicles 
  • validation techniques

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Published Papers (1 paper)

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Research

13 pages, 1530 KB  
Article
Interval Observer for Vehicle Sideslip Angle Estimation Using Extended Kalman Filters
by Fernando Viadero-Monasterio, Miguel Meléndez-Useros, Basilio Lenzo and Beatriz López Boada
Machines 2025, 13(8), 707; https://doi.org/10.3390/machines13080707 - 9 Aug 2025
Viewed by 806
Abstract
Accurate estimation of the vehicle sideslip angle is critical for the effective operation of advanced driver assistance systems and active safety functions such as electronic stability control. However, direct measurement of sideslip angle is impractical in series-production vehicles due to high sensor cost. [...] Read more.
Accurate estimation of the vehicle sideslip angle is critical for the effective operation of advanced driver assistance systems and active safety functions such as electronic stability control. However, direct measurement of sideslip angle is impractical in series-production vehicles due to high sensor cost. Furthermore, existing estimation methods often neglect the impact of model uncertainties on estimation error, which can compromise estimation reliability and, consequently, vehicle stability. To address these limitations, this paper proposes an interval observer based on a Kalman filter that accounts explicitly for model uncertainties in the sideslip angle estimation process. The proposed method generates both upper and lower bounds of the estimated sideslip angle, providing a quantifiable measure of uncertainty that enhances the robustness of control systems that depend on this measurement. Given the limitations of simplified vehicle models, a combined vehicle roll and lateral dynamics model is utilized to improve estimation accuracy. The effectiveness of the proposed methodology is demonstrated through a series of simulation experiments conducted using CarSim. Full article
(This article belongs to the Special Issue Vehicle Dynamics Estimation and Fault Monitoring)
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