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Applied Sciences
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Trends and Prospects in Vehicle System Dynamics
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Trends and Prospects in Vehicle System Dynamics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 3844

Special Issue Editors

Dr. Luigi Romano

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Guest Editor
Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden
Interests: vehicle dynamics; vehicle control; automotive control; sustainable transportation; tire modeling
Prof. Dr. Basilio Lenzo

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
Interests: vehicle dynamics; vehicle control; vehicle state estimation; robotics; control theory
Special Issues, Collections and Topics in MDPI journals
Dr. Ricardo De Castro

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of California, Merced, CA 95343, USA
Interests: vehicle dynamics; vehicle control; electric vehicles; hybrid-electric vehicles; autonomous driving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The challenges that the automotive sector is facing concerning the implementation of partial and full-automated driving, in conjunction with the impellent need for electrification, require integrated solutions. To this end, vehicle-to-vehicle interaction and new generations of sensors and control strategies, including AI-based algorithms, should be explicitly considered. This Special Issue focuses on emerging technologies and approaches that will support the current trends and prospects in vehicle dynamics. Aspects of interest may include, but are not limited to, the following: novel modeling approaches of vehicular systems and subsystems, with an emphasis on handling and energy-efficiency applications; innovative control/estimation strategies for individual components, vehicles, and fleets; modeling and control of vehicle-to-vehicle interaction, based on future urban and extra-urban mobility scenarios; model-based and model-free path planning, force allocation strategies, and online energy optimization; and the development and integration of novel sensors and devices supporting vehicle dynamics functionalities. 

Dr. Luigi Romano
Prof. Dr. Basilio Lenzo
Dr. Ricardo De Castro
Guest Editors

Manuscript Submission Information

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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 modeling
  • vehicle dynamics
  • vehicle control
  • vehicle-to-vehicle interaction
  • fleet management
  • path planning
  • force allocation
  • energy optimization
  • next-generation vehicle sensors
  • AI-based vehicle control

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Published Papers (3 papers)

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Research

17 pages, 11075 KiB  
Article
Vehicle Motion Control for Overactuated Vehicles to Enhance Controllability and Path Tracking
by Philipp Mandl, Johannes Edelmann and Manfred Plöchl
Appl. Sci. 2024, 14(22), 10718; https://doi.org/10.3390/app142210718 - 19 Nov 2024
Viewed by 441
Abstract
The motion control of vehicles poses distinct challenges for both vehicle stability and path tracking, especially under critical environmental and driving conditions. Overactuated vehicles can effectively utilize the available tyre–road friction potential by leveraging additional actuators, thus enhancing their stability and controllability even [...] Read more.
The motion control of vehicles poses distinct challenges for both vehicle stability and path tracking, especially under critical environmental and driving conditions. Overactuated vehicles can effectively utilize the available tyre–road friction potential by leveraging additional actuators, thus enhancing their stability and controllability even in challenging scenarios. This paper introduces a novel modular upstream control architecture for overactuated vehicles, integrating a fast and robust linear time-varying model predictive path and speed tracking controller with a model following approach and nonlinear control allocation to form a holistic vehicle motion controller. The architecture decouples the path and speed tracking task from the actuator allocation, where torque vectoring and rear-wheel steering are applied to achieve linear understeer reference vehicle behavior. It allows for the use of a simpler path tracking controller, enabling long preview horizons and enhanced computational efficiency. Nonlinearities, such as the mutual influence of lateral and longitudinal tyre forces, are accounted for within the control allocation. The simulation results demonstrate that the proposed control architecture and overactuation improve vehicle stability in critical driving conditions and reduce path tracking errors compared to a dual-motor vehicle. Full article
(This article belongs to the Special Issue Trends and Prospects in Vehicle System Dynamics)
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34 pages, 7755 KiB  
Article
Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
by Johannes Ultsch, Andreas Pfeiffer, Julian Ruggaber, Tobias Kamp, Jonathan Brembeck and Jakub Tobolář
Appl. Sci. 2024, 14(16), 7066; https://doi.org/10.3390/app14167066 - 12 Aug 2024
Viewed by 1108
Abstract
In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives [...] Read more.
In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives of improving ride comfort and road-holding, and (3) the additionally coupled vertical dynamic system. This work presents the application of Reinforcement Learning to the vertical dynamics control problem of a real street vehicle to address these issues. We discuss the entire Reinforcement Learning-based controller design process, which started with deriving a sufficiently accurate training model representing the vehicle behavior. The obtained model was then used to train a Reinforcement Learning agent, which offered improved vehicle ride qualities. After that, we verified the trained agent in a full-vehicle simulation setup before the agent was deployed in the real vehicle. Quantitative and qualitative real-world tests highlight the increased performance of the trained agent in comparison to a benchmark controller. Tests on a real-world four-post test rig showed that the trained RL-based controller was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average. Full article
(This article belongs to the Special Issue Trends and Prospects in Vehicle System Dynamics)
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21 pages, 9989 KiB  
Article
Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
by Aashish Shaju, Steve Southward and Mehdi Ahmadian
Appl. Sci. 2024, 14(5), 1817; https://doi.org/10.3390/app14051817 - 22 Feb 2024
Cited by 1 | Viewed by 1316
Abstract
This study introduces an advanced lateral control strategy for autonomous vehicles using a clothoid-based approach integrated with an adaptive lookahead mechanism. The primary focus is on enhancing lateral stability and path-tracking accuracy through the application of Euler spirals for smooth curvature transitions, thereby [...] Read more.
This study introduces an advanced lateral control strategy for autonomous vehicles using a clothoid-based approach integrated with an adaptive lookahead mechanism. The primary focus is on enhancing lateral stability and path-tracking accuracy through the application of Euler spirals for smooth curvature transitions, thereby reducing passenger discomfort and the risk of vehicle rollover. An innovative aspect of our work is the adaptive adjustment of lookahead distance based on real-time vehicle dynamics and road geometry, which ensures optimal path following under varying conditions. A quasi-feedback control algorithm constructs optimal clothoids at each time step, generating the appropriate steering input. A lead filter compensates for the vehicle’s lateral dynamics lag, improving control responsiveness and stability. The effectiveness of the proposed controller is validated through a comprehensive co-simulation using TruckSim® and Simulink®, demonstrating significant improvements in lateral control performance across diverse driving scenarios. Future directions include scaling the controller for higher-speed applications and further optimization to minimize off-track errors, particularly for articulated vehicles. Full article
(This article belongs to the Special Issue Trends and Prospects in Vehicle System Dynamics)
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