Vehicle Dynamics and Control, 2nd Edition

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic “Vehicle Dynamics and Control (https://www.mdpi.com/topics/J12MF37TK9)”. A vehicle is a typical multi-system-coupled, complex nonlinear dynamic system that exhibits different dynamic characteristics in the lateral, longitudinal, and vertical directions; thus, the research and control objectives in different directions are diverse. Vehicle dynamics and control are based on the dynamic equations of the entire vehicle and each subsystem. The key to obtaining good dynamic performance, stability, smoothness, and safety of vehicles is to control the vehicle’s speed, yaw rate, tire slip ratio, body roll angle, and vibration acceleration by adopting appropriate control algorithms. In recent years, with the rapid development of microelectronics, sensing, and automation technologies, people’s requirements for vehicle efficiency, energy saving, and intelligence are increasing. The industry has ushered in the technological changes of electrification, intelligence, and networking, which have also brought new challenges to research on vehicle dynamics and control. To further improve the power, stability, ride comfort, and safety of vehicles, dynamics and control have become the focus of relevant research by scholars in recent years. We therefore invite papers on innovative technical developments in addition to reviews, case studies, and analytical and assessment papers from different disciplines that are relevant to the topic of vehicle dynamics and control. The main topics of the section include, but are not limited to, the following:

• Vehicle drive system and braking system control;
• Optimal design and control of vehicle suspension systems;
• Lateral and longitudinal vehicle dynamics;
• Dynamics modeling, simulation analysis, and control system design;
• Chassis active control of intelligent electric vehicles;
• Lateral control of autonomous vehicles.

Prof. Dr. Francis F. Assadian
Prof. Dr. Junnian Wang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Machines machines	2.1	3.0	2013	15.5 Days	CHF 2400	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Vehicles vehicles	2.4	4.1	2019	19.9 Days	CHF 1600	Submit
World Electric Vehicle Journal wevj	2.6	4.5	2007	16.2 Days	CHF 1400	Submit
Designs designs	-	3.9	2017	21.7 Days	CHF 1600	Submit

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Published Papers (4 papers)

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17 pages, 25472 KiB  

Open AccessArticle

Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles

by Zhihong Wang, Jiefeng Zhong, Jie Hu, Zhiling Zhang and Wenlong Zhao

Appl. Sci. 2025, 15(2), 953; https://doi.org/10.3390/app15020953 - 19 Jan 2025

Viewed by 385

Abstract

This paper addresses the significant variations in model parameters observed in autonomous commercial vehicles in comparison to passenger cars, with a disparity noted largely due to changes in load. Additionally, it tackles the issue of path tracking inaccuracy caused by external factors such [...] Read more.

This paper addresses the significant variations in model parameters observed in autonomous commercial vehicles in comparison to passenger cars, with a disparity noted largely due to changes in load. Additionally, it tackles the issue of path tracking inaccuracy caused by external factors such as delays in steering system execution. The proposed solution is a hierarchical control method, grounded in mass estimation and model predictive control(MPC). Initially, to counter the variation in model parameters, a mass estimator is developed. This estimator utilizes the recursive least squares method with a forgetting factor, coupled with M-estimation, thereby enhancing the robustness of the estimation and achieving model correction. Subsequently, an upper-level MPC controller is constructed based on the error model, thereby augmenting the precision of tracking control. To address the delay in the steering system execution common in autonomous commercial vehicles, a lower-level steering angle compensator is designed to expedite the response speed of the execution. The feasibility of the vehicle’s front wheel angle is constrained via the rollover index, thereby enhancing vehicle stability during operation. The efficacy of the proposed control strategy is demonstrated with joint simulations using TruckSim/Simulink and real vehicle tests. The results indicate that this strategy can effectively manage the model mismatch caused by load changes in commercial vehicles and the delay in steering system execution, thereby exhibiting commendable tracking accuracy, adaptability, and driving stability. Full article

(This article belongs to the Topic Vehicle Dynamics and Control, 2nd Edition)

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35 pages, 5504 KiB  

Open AccessArticle

From Polylithic to Monolithic: The Design of a Lightweight, Stiffened, Non-Rotational, Deep-Drawn Automotive Product

by Gibson P. Chirinda, Stephen Matope, Andreas Sterzing and Matthias Nagel

Designs 2024, 8(6), 123; https://doi.org/10.3390/designs8060123 - 21 Nov 2024

Viewed by 690

Abstract

The transition from polylithic (composed of many parts) to monolithic (one part) design in automotive components presents an opportunity for a reduction in part count, weight, processing routes, and production time without compromising performance. The traditional design approaches for rooftop tents assemble various [...] Read more.

The transition from polylithic (composed of many parts) to monolithic (one part) design in automotive components presents an opportunity for a reduction in part count, weight, processing routes, and production time without compromising performance. The traditional design approaches for rooftop tents assemble various sheet metal and extrusions together using different joining processes such as welding, adhesive bonding, bolting, and riveting. This is often associated with disadvantages, such as increased weight, high production time, and leaking joints. This research, therefore, presents the development of a monolithic, lightweight, stiffened, non-rotational automotive rooftop tent that is manufactured via the deep-drawing process. An onsite company case study was conducted to analyze the polylithic product and its production process to determine its limitations. This was followed by the design of a lightweight, non-rotational monolithic product whose purpose is to eliminate the identified disadvantages. The stiffness geometries were developed to enhance the overall structural integrity without adding unnecessary weight. The Analytic Hierarchy Process (AHP) was used to analyze and evaluate alternative layouts against criteria such as complexity, tool design, symmetry, rigidity, and cost. Simulations conducted using NX 2024 software confirmed the effectiveness of this design. The results show that the monolithic rooftop tent has a comparable stiffness performance between the lightweight, monolithic rooftop tent and the heavy, polylithic rooftop tent. At the same time, the part count was reduced from twenty-three (23) single parts (polylithic) to a one (1) part (monolithic) rooftop tent, the weight was reduced by 15.6 kg, which translates to a 30% weight reduction without compromising the performance, processing routes were reduced from eight (8) to three (3), production time was reduced by 120 min, and leaking was eliminated. It can, therefore, be concluded that the design and manufacturing of monolithic rooftop tents leads to a lighter and stronger product. Full article

(This article belongs to the Topic Vehicle Dynamics and Control, 2nd Edition)

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20 pages, 6736 KiB  

Open AccessArticle

Enhanced Anti-Rollover Control for Commercial Vehicles Under Dynamic Lateral Interferences

by Jin Rong, Tong Wu, Junnian Wang, Jing Peng, Xiaojun Yang, Yang Meng and Liang Chu

Designs 2024, 8(6), 121; https://doi.org/10.3390/designs8060121 - 15 Nov 2024

Viewed by 709

Abstract

Commercial vehicles frequently experience lateral interferences, such as crosswinds or side slopes, during extreme maneuvers like emergency steering and high-speed driving due to their high centroid. These interferences reduce vehicle stability and increase the risk of rollover. Therefore, this study takes a bus [...] Read more.

Commercial vehicles frequently experience lateral interferences, such as crosswinds or side slopes, during extreme maneuvers like emergency steering and high-speed driving due to their high centroid. These interferences reduce vehicle stability and increase the risk of rollover. Therefore, this study takes a bus as the carrier and designs an anti-rollover control strategy based on mixed-sensitivity and robust $H_{\infty }$ controller. Specifically, a 7-DOF vehicle dynamics model is introduced, and the factors influencing vehicle rollover are analyzed. Based on this, to minimize excessive intervention in the vehicle’s dynamic characteristics, the lateral velocity, roll angle, and roll rate are recorded at the vehicle’s rollover threshold as desired values. The lateral load transfer rate (LTR) is chosen as the evaluation index, and the required additional yaw moment is determined and distributed to the wheels for anti-rollover control. Furthermore, to verify the effectiveness of the proposed anti-rollover control strategy, a co-simulation platform based on MATLAB/Simulink and TruckSim is developed. Various dynamic lateral interferences (side winds with different changing trends and wind speeds) are introduced, and the fishhook and J-turn maneuvers are selected to analyze and compare the proposed control strategy with a fuzzy logic algorithm. The results indicate that the maximum LTR of the vehicle is reduced by 0.11. Additionally, the lateral acceleration and yaw rate in the steady state are reduced by more than 1.8 m/s² and 15°, respectively, enhancing the vehicle’s lateral stability. Full article

(This article belongs to the Topic Vehicle Dynamics and Control, 2nd Edition)

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19 pages, 5136 KiB  

Open AccessArticle

Adaptive Energy Management Strategy for Hybrid Electric Vehicles in Dynamic Environments Based on Reinforcement Learning

by Shixin Song, Cewei Zhang, Chunyang Qi, Chuanxue Song, Feng Xiao, Liqiang Jin and Fei Teng

Designs 2024, 8(5), 102; https://doi.org/10.3390/designs8050102 - 12 Oct 2024

Viewed by 737

Abstract

Energy management strategies typically employ reinforcement learning algorithms in a static state. However, during vehicle operation, the environment is dynamic and laden with uncertainties and unforeseen disruptions. This study proposes an adaptive learning strategy in dynamic environments that adapts actions to changing circumstances, [...] Read more.

Energy management strategies typically employ reinforcement learning algorithms in a static state. However, during vehicle operation, the environment is dynamic and laden with uncertainties and unforeseen disruptions. This study proposes an adaptive learning strategy in dynamic environments that adapts actions to changing circumstances, drawing on past experience to enhance future real-world learning. We developed a memory library for dynamic environments, employed Dirichlet clustering for driving conditions, and incorporated the expectation maximization algorithm for timely model updating to fully absorb prior knowledge. The agent swiftly adapts to the dynamic environment and converges quickly, improving hybrid electric vehicle fuel economy by 5–10% while maintaining the final state of charge (SOC). Our algorithm’s engine operating point fluctuates less, and the working state is compact compared with Deep Q-Network (DQN) and Deterministic Policy Gradient (DDPG) algorithms. This study provides a solution for vehicle agents in dynamic environmental conditions, enabling them to logically evaluate past experiences and carry out situationally appropriate actions. Full article

(This article belongs to the Topic Vehicle Dynamics and Control, 2nd Edition)

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