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In the racing world, speed is everything, and the Formula Student cars are no different. As one of the key means to improve the speed of the car, lightweight plays an important role in the racing world. The weight reduction of unsprung metal parts can not only improve the driving speed, but also effectively optimize the dynamic of the car, so the lightweight design of unsprung parts has attracted much attention. In the traditional Formula Student racing car, the hub and spoke are two independent parts, they are fixed by four hub bolts or a central locking nut, the material of these fasteners is usually steel, so it brings a lot of weight burden. In order to achieve unsprung lightweight, a new type of wheel part design of Formula Student racing car is proposed in this paper. The hub and spoke are designed as integrated aluminum alloy parts, effectively eliminating the mass of hub bolts or central locking nuts.

Battery electric vehicles are quickly gaining momentum to improve vehicle fuel efficiency and emission reduction. However, they must be designed to provide adequate range on a single charge combined with good acceleration performance, top speed, gradeability, and fast charging times. The paper presents a model for sizing the power train of an electric vehicle, including the power electronic converter, electric motor, and battery pack. A major assumption is that an optimal wheel slip rate can be achieved by modern vehicles using slip control systems. MATLAB/Simulink was used to model the vehicle powertrain. Simulations were conducted based on different speed and acceleration profiles. The purpose of the study focused on the motor and power electronics sizing requirements to achieve optimal range and performance.

This paper presents a numerical methodology to generate lookup tables that provide d- and q-axis stator current references for the control of electric motors. The main novelty with respect to other literature references is the introduction of the iron power losses in the equivalent-circuit electric motor model implemented in the optimization routine. The lookup tables generation algorithm discretizes the motor operating domain and, given proper constraints on maximum stator current and magnetic flux, solves a numerical optimization problem for each possible operating point to determine the combination of d- and q- axis stator currents that minimizes the imposed objective function while generating the desired torque. To demonstrate the versatility of the proposed approach, two different variants of this numerical interpretation of the motor control problem are proposed: Maximum Torque Per Ampere and Minimum Electromagnetic Power Loss.

Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling.

This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

Vehicle yaw stability control (YSC) can actively adjust the working state of the chassis actuator to generate a certain additional yaw moment for the vehicle, which effectively helps the vehicle maintain good driving quality under strong transient conditions such as high-speed turning and continuous lane change. However, the traditional YSC pursues too much driving stability after activation, ignoring the difference of multi-objective requirements of yaw maneuverability, actuator energy consumption and other requirements in different vehicle stability states, resulting in the decline of vehicle driving quality. Therefore, a vehicle yaw stability model predictive control strategy for dynamic and multi-objective requirements is proposed in this paper. Firstly, the unstable characteristics of vehicle motion are analyzed, and the nonlinear two-degree-of-freedom vehicle dynamics models are established respectively.

Brake pulsation is a low frequency vibration phenomenon in brake judder. In this study, a simulation approach has been developed to understand the physics behind brake pulsation employing a full vehicle dynamics CAE model. The full vehicle dynamic model was further studied to understand the impact of suspension tuning variation to brake pulsation performance. Brake torque variation (BTV) due to brake thickness variation from uneven rotor wear was represented mathematically in a sinusoidal form. The wheel assembly vibration from the brake torque variation is transmitted to driver interface points such as the seat track and the steering wheel. The steering wheel lateral acceleration at the 12 o’clock position, driver seat acceleration, and spindle fore-aft acceleration were reviewed to explore the physics of brake pulsation. It was found that the phase angle between the left and right brake torque generated a huge variation in brake pulsation performance.

Human driving behavior's inherent variability, randomness, individual differences, and dynamic vehicle-road situations give human-machine cooperative (HMC) driving considerable uncertainty, which affects the applicability and effectiveness of HMC control in complex scenes. To overcome this challenge, we present a novel data-enabled game output regulation approach for HMC driving. Firstly, a global human-vehicle-road (HVR) model is established considering the varied driver's steering characteristic parameters, such as delay time, preview time, and steering gain, as well as the uncertainty of tire cornering stiffness and variable road curvature disturbance. The robust output regulation theory has been employed to ensure the global DVR system's closed-loop stability, asymptotic tracking, and disturbance rejection, even with an unknown driver's internal state. Secondly, an interactive shared steering controller has been designed to provide personalized driving assistance.

Tire forces and moments play an important role in vehicle dynamics and safety. X-by-wire chassis components including active suspension, electronic powered steering, by-wire braking, etc can take the tire forces as inputs to improve vehicle’s dynamic performance. In order to measure the accurate dynamic wheel load, most of the researches focused on the kinematic parameters such as body longitudinal and lateral acceleration, load transfer and etc. In this paper, the authors focus on the suspension system, avoiding the dependence on accurate mass and aerodynamics model of the whole vehicle. The geometry of the suspension is equated by the spatial parallel mechanism model (RSSR model), which improves the calculation speed while ensuring the accuracy. A suspension force observer is created, which contains parameters including spring damper compression length, push rod force, knuckle accelerations, etc., combing the kinematic and dynamic characteristic of the vehicle.

Under complex and extreme operating conditions, the road adhesion coefficient emerges as a critical state parameter for tire force analysis and vehicle dynamics control. In contrast to model-based estimation methods, intelligent tire technology enables the real-time feedback of tire-road interaction information to the vehicle control system. This paper proposes an approach that integrates intelligent tire systems with machine learning to acquire precise road adhesion coefficients for vehicles. Firstly, taking into account the driving conditions, sensor selection is conducted to develop an intelligent tire hardware acquisition system based on MEMS (Micro-Electro-Mechanical Systems) three-axis acceleration sensors, utilizing a simplified hardware structure and wireless transmission mode. Secondly, through the collection of real vehicle experiment data on different road surfaces, a dataset is gathered for machine learning training.

In order to study the tire friction characteristics under wet skid surface, the “pseudo” hydrodynamic pressure bearing effect is used to be equivalent to the hydrodynamics of water film, and an advanced Lugre tire hydroplaning dynamic model is developed by combining the arbitrary pressure distribution function. The water hydroplaning dynamic tests were carried out for 285/70R19.5 tire under wet of different water film thickness and dry conditions, and the parameters of the advanced Lugre tire dynamic model were identified. The results show that the tire water-skiing model proposed in this paper can effectively simulate the friction characteristics of tires under different water film thicknesses. Under dry conditions, 0.5mm water film and 1mm water film road conditions, the relative errors of the maximum tire friction coefficient between the tested and advanced Lugre tire model are 1.11%, 0.12% and 0.16%, respectively.

Off-roading is the scenario of driving a vehicle on unpaved surfaces such as sand, gravel, riverbeds, rocks, and other natural terrain. Vehicle designed for that purpose requires jumping from height due to uneven surfaces/patches. This also requires them to sustain a high amount of loads acting upon them on impact. Thus, off-roading vehicles should not only provide intended vehicle dynamics performance but at the same time should be durable as well. Drop test which is done in a controlled environment is a widely used method to validate the durability of vehicle in such scenarios wherein the vehicle is dropped from a certain predefined height. In Multibody dynamics simulation, drop test was replicated and acceleration data computed at different locations in the vehicle were correlated with actual physical test data. Correlation was done for different drop heights. This paper presents relevant details of the virtual vehicle modeling, loadcase, test data & subsequent correlation.

The soft and rough terrain on the planet's surface significantly affects the ride and safety of rovers during high-speed driving, which imposes high requirements for the control of the suspension system of planet rovers. To ensure good ride comfort of the planet rover during operation in the low-gravity environment of the planet's surface, this study develops an active suspension control strategy for torsion spring and torsional damper suspension systems for planet rovers. Firstly, an equivalent dynamic model of the suspension system is derived. Based on fractal principles, a road model of planetary surface is established. Then, a fuzzy-PID based control strategy aimed at improving ride comfort for the planet rover suspension is established and validated on both flat and rough terrains.

As the automotive industry accelerates its virtual engineering capabilities, there is a growing requirement for increased accuracy across a broad range of vehicle simulations. Regarding control system development, utilizing vehicle simulations to conduct ‘pre-tuning’ activities can significantly reduce time and costs. However, achieving an accurate prediction of, e.g., stopping distance, requires accurate tire modeling. The Magic Formula tire model is often used to effectively model the tire response within vehicle dynamics simulations. However, such models often: i) represent the tire driving on sandpaper; and ii) do not accurately capture the transient response over a wide slip range. In this paper, a novel methodology is developed using the MF-Tyre/MF-Swift tire model to enhance the accuracy of ABS braking simulations.

When the aircraft towing operations are carried out in narrow areas such as the hangars or parking aprons, it has a high safety risk for aircraft that the wingtips may collide with the surrounding aircraft or the airport facility. A real-time trajectory prediction method for the towbarless aircraft taxiing system (TLATS) is proposed to evaluate the collision risk based on image recognition. The Yolov7 module is utilized to detect objects and extract the corresponding features. By obtaining information about the configuration of the airplane wing and obstacles in a narrow region, a Long Short-Term Memory (LSTM) encoder-decoder model is utilized to predict future motion trends. In addition, a video dataset containing the motions of various airplane wings in real traction scenarios is constructed for training and testing.

With the development of hardware and control theory, the application of quadcopters is constantly expanding. Quadcopters have emerged in many fields, including transportation, exploration, and object grabbing and placement. These application scenarios require accurate, stable, and rapid control, and a suitable dynamic model is one of the prerequisites. At present, many works are related to it, most of which are modeled using the Newton-Euler method. Some works have also adopted other methods, including the Lagrangian and Hamiltonian methods. This article proposes a new method that solves the Hamiltonian equation of a quadcopter expressed in quasi-coordinate. The external forces and motion of the body are expressed in the quasi-coordinate system of the body, and solved through the Hamiltonian equation. This method simplifies operations and improves computational efficiency. Additionally, a single pendulum is attached to the quadcopter to simulate application scenarios.

In recent years, the burgeoning applications of hydrogen fuel cells have ignited a growing trend in their integration within the transportation sector, with a particular focus on their potential use in multi-rotor drones. The heightened mass-based energy density of fuel cells positions them as promising alternatives to current lithium battery-powered drones, especially as the demand for extended flight durations increases. This article undertakes a comprehensive exploration, comparing the performance of lithium batteries against air-cooled fuel cells, specifically within the context of multi-rotor drones with a 3.5kW power requirement. The study reveals that, for the specified power demand, air-cooled fuel cells outperform lithium batteries, establishing them as a more efficient solution.

A redundant system refers to a system that operates identical unit systems simultaneously to enhance robustness to fault. In particular, considering system complexity, a redundant system consisting of two identical unit systems is widely used. However, dual-system redundancy can detect the presence of malfunction when the outputs of the two unit systems differ, but it is challenging to identify the normally functioning unit system. Therefore, the functionality can degrade or be interrupted even when a normally operating unit system is present. Hence, research is actively ongoing to address the challenge of identifying the normally functioning unit system. This study proposes an algorithm to identify the normally operating sensor in the event of a steering angle sensor fault in a redundant Electronic Power Steering (EPS) system. In this paper, an Extended Kalman Filter is designed based on the Bicycle model of vehicle dynamics to estimate the steering angle of the steering wheel.

Due to the high center of gravity of medium-duty vehicles, rollover accidents can easily occur during high-speed cornering and lane changes. In order to prevent the deformation of the body structure, which would restrict the survival space and cause compression injuries to occupants, it is necessary to investigate methods for mitigating these incidents. This paper establishes a numerical model of right-side rollover for a commercial medium-duty vehicle in accordance with ECE R66 regulations, and the accuracy of the model is verified by experiment. According to the results, the material and size parameters of the key components of the right side pillar are selected as design variables. The response result matrix was constructed using the orthogonal design method for total mass, energy absorption, maximum collision acceleration, and minimum distance from the survival space.