An Open Access Journal
Highlights of Vehicles Editorial Office
Avenida Madrid, 189-195, 3-3
08014 Barcelona, Spain
08014 Barcelona, Spain
Jene Zhang
Managing Editor
Special Issue —
Feature Papers to the Inaugural Volume of Highlights of Vehicles
Deadline of submission 31 December 2023.
About this Special Issue
Nowadays, vehicle electrification has garnered significant attention in the automobile industry and research community. Electric vehicles, with their more flexible chassis structure and advanced performance, face significant challenges in terms of stability and safety. As a result, there is a pressing need for more advanced vehicle state estimation, motion control, and diagnosis technologies. Real-time estimation of the vehicle state is foundational to achieve effective vehicle control schemes. The accuracy of state parameter estimation directly influences the control performance and characteristics of the vehicle dynamics control system. Vehicle motion control, such as integrated chassis control, plays a crucial role as a component between planning and action tasks and can greatly improve the yaw and roll stability of the vehicle. Furthermore, diagnosis technologies are a promising tool for diagnosing road safety problems and proposing appropriate countermeasures. With the right tools and technologies, we can create a safer, more efficient, and more sustainable future for automobiles.
The scope of this special issue mainly covers the area of intelligent electric vehicle state estimation and control. We encourage the submissions which tackling the advanced state estimation and vehicle dynamics-based control strategy for electric vehicles. Furthermore, we especially welcome the submissions about the vehicle state estimation, motion control and diagnosis strategy for electric vehicles.
Potential topics include but are not limited to the following:
The scope of this special issue mainly covers the area of intelligent electric vehicle state estimation and control. We encourage the submissions which tackling the advanced state estimation and vehicle dynamics-based control strategy for electric vehicles. Furthermore, we especially welcome the submissions about the vehicle state estimation, motion control and diagnosis strategy for electric vehicles.
Potential topics include but are not limited to the following:
- vehicle state estimation
- integrated chassis control, such as torque vectoring or active suspension control
- human-machine shared control
- fault diagnosis and fault-tolerant control
- motion control algorithms, including advanced control and decision-making strategies for longitudinal, lateral and vertical vehicle dynamics
- diagnosis and fault estimation of safety-critical vehicular sub-systems
Special Issue Editors
School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Research Keywords: robust control theory and applications; vehicle system dynamics and control; smart materials and structures; active and semiactive vibration control; robotics and autonomous system
Submission Information
All the manuscripts submitted to this Special Issue must be within both the scope of this Special Issue and the journal.
Manuscripts should be submitted online (Click here to submit, registration and login required). All the manuscripts will undergo a rigorous single-blind peer-review process.
Please prepare your manuscript following the Instructions for Authors, and make sure it is submitted in gramatically correct English.
The Article Processing Charge (APC) will be fully waived. Please refer to the Editorial Process for more information about manuscript process.
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Published Articles (1)
Article 26 July 2023
Maksym Diachuk and Said M. Easa
This article is part of the Special Issue Feature Papers to the Inaugural Volume of Highlights of Vehicles.
Highlights of Vehicles
Volume 1 (2023), Issue 1, pp. 29–53
Volume 1 (2023), Issue 1, pp. 29–53
320 Views74 Downloads
Article 26 July 2023
Maksym Diachuk and Said M. Easa
The study aims at improving the technique of planning the autonomous vehicles’ (AV) speed mode based on a kinematic model with physical restrictions. A mathematical model relates the derivatives of kinematic parameters with ones of the
The study aims at improving the technique of planning the autonomous vehicles’ (AV) speed mode based on a kinematic model with physical restrictions. A mathematical model relates the derivatives of kinematic parameters with ones of the trajectory’s curvature. The inverse approach uses an expanded vehicle model considering the distribution of vertical reactions, wheels’ longitudinal reactions according to a drive type, and lateral forces ensuring motion stability. For analysis of the drive type, four options are proposed: front-wheel drive (FWD), rear-wheel drive (RWD), permanent engaged all-wheel drive (AWD), and 4-wheel drive with torque vectoring (4WD-TV). The optimization model is also built by the inverse scheme. The longitudinal speed’s higher derivatives are modeled by the finite element (FE) functions with nodal unknowns. The sequential integrations ensure the optimality and smoothness of the third derivative. The kinematic restrictions are supplemented by the tire-road critical slip states. Sequential quadratic programming (SQP) and the Gaussian N-point scheme for quadrature integration are used to minimize the objective function. The simulation results show a significant difference in the mode forecasts between four types of AV drives at the same initial conditions. This technique allows redistributing the traction forces strictly according to the wheels’ adhesion potentials and increases the optimization performance by about 40% compared to using the kinematic model based on the same technique without physical constrains.
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This article is part of the Special Issue Feature Papers to the Inaugural Volume of Highlights of Vehicles.
Highlights of Vehicles
Volume 1 (2023), Issue 1, pp. 29–53
Volume 1 (2023), Issue 1, pp. 29–53
320 Views74 Downloads
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Highlights of Vehicles Editorial Office
Avenida Madrid, 189-195, 3-3
08014 Barcelona, Spain
08014 Barcelona, Spain
Jene Zhang
Managing Editor
