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It is very significant for autonomous vehicles to have the ability to operate beyond the stable handling limits, which plays a vital role in vehicles’ active safety and enhances riding and driving pleasure. For traditional vehicles, it is rather difficult to control the longitudinal speed, sideslip angle and yaw rate simultaneously when drifting along a given trajectory because they are under-actuated. Nevertheless, for a 4-wheel-independent-drive (4WID) vehicle, it is possible and controllable thanks to its over-actuated characteristics. This article designs a trajectory following control strategy for automated drifting of 4WID vehicles. First, a double-track 7 degree of freedom (7DOF) vehicle dynamic model is established, which incorporates longitudinal and lateral load transfer and considers nonlinear tire models. The controller which proposes a hierarchical architecture is then designed.

The vehicle will produce certain shock and vibration during the braking process, which will affect the driving experience of the driver. Aiming at the problems of pitch vibration, longitudinal vibration and shock during the braking process, this paper proposes a planning and following control method for target longitudinal acceleration in post-braking phase, and designs control trigger strategies. Target longitudinal acceleration planning takes minimizing longitudinal shock as the design goal. The following control takes the brake pressure as the control object, and adopts the “feedforward +PID feedback” method to follow the target longitudinal acceleration. Besides, considering the safety of braking process, the trigger condition of control is designed which utilizes BP neural network method to judge whether the control has to be triggered. Based on Simulink software, the simulation model of straight-line braking is established.

A Vehicle Dynamics Analysis of an electric All-Terrain Vehicle (ATV) is conducted and presented in the following paper. Vehicle performance is analyzed, shortcomings are identified and solutions to optimize the vehicle design are implemented. These optimizations are tested and results are compared with the pre-existing models and validated by conducting physical trials on the actual model. The virtual tests are carried out using Multi-Body Dynamics (MBD) tool- MSC ADAMS. The results obtained from the tests have been put forth in theoretical as well as graphical manner to get a clearer view. This research involved a thorough study of Lateral and Longitudinal Dynamics of the ATV. Trends in dynamic parameters like the ride quality, pitch response, roll stability, yaw response, camber gain and other important parameters of the vehicle have been studied and its correlation with the feedback obtained from the driver is established.

This paper will describe the development of a load estimation algorithm that is used to estimate the load parameters necessary to detect a vehicle’s proximity to rollover. When operating a vehicle near its handling limits or with large loads, vehicle rollover must be considered for safe operation. Vehicle mass and center of gravity (CG) height play a large role in a vehicle’s rollover propensity. Cargo and passenger vehicles operate under a range of load configurations; therefore, changes in load should be estimated. Researchers have often developed load estimation and rollover detection algorithms separately. This paper will develop a load estimation algorithm and use the load estimates and vehicle states to detect rollover. The load estimation algorithm uses total least squares and is broken into two parts. First, mass is estimated based on a “full-car” dynamic ride model. Next, the CG height and inertia are estimated using the previously estimated mass and a dynamic roll model.

The accuracy and range of chassis control for a four in-wheel motor (IWM)-driven electric vehicles (EVs), especially in observer-based EVs control for improving road handling and ride comfort, is a challenging task for the IWM-driven vehicle system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely acquire movement state and chose the reasonable observer-based control algorithm for IWM-driven EVs become a hot topic in both academia and industry. Simultaneously, uncertainty is always existing, e.g., varying road excitation, variable system parameters or nonlinear structure. Meanwhile, the coupling effects between the non-ideal IWM actuator and vehicle are ignored under the assumption of an ideal actuator.

Robotic technology has begun to play an essential role in ground automotive applications. Utility trucks are among the first responders in extreme climate and severe weather conditions, comprised of two systems: a mobile platform and an articulated robotic morphing arm. The conventional industrial manipulators are mounted on stationary bases, while a mobile manipulator is dynamically coupled on a mobile platform. Such trucks with morphing manipulator can increase the possibility of road accidents in many ways and, additionally, create dangerous situations on the roads, and off-road conditions, while moving, and performing tasks. Large boom equipped trucks for reaching elevated heights can become unstable due to drastic variation of the boom equipment moment of inertia causing the extreme weight re-distribution among the wheels. The morphing capabilities of the utility trucks need to be investigated together with the vehicle-road forces in order to hold the truck safe on the roads.

The DrivAer realistic generic car model is now established as one of the benchmark geometries to assess the aerodynamic flow field characteristics associated with passenger vehicles. Since its introduction in 2012, the database of experimental studies has grown and provides excellent validation opportunities for analytical methods. This paper compares Computational Fluid Dynamics (CFD) simulations for integral forces, surface pressure distribution and velocity flow fields for the DrivAer model in the notchback configuration. Transient CFD data are obtained by employing hybrid Reynolds Averaged Navier-Stokes (RANS) and Large Eddy Simulation methods (Detached Eddy Simulation - DES) using the finite volume solvers Simcenter Star-CCM+ and the openFOAM based flow solver IconCFD. Computational results are calculated using Wall Resolved Meshes (WRM), where y+ < 1, and Wall Function Meshes (WFM), where 30 < y+ < 100.

5 belt wind tunnels are the most common facility to conduct the experimental aerodynamics development for production cars. Among aerodynamic properties, usually drag is the most important development target, but lift force and its front/rear balance is also important for vehicle dynamics. Related to the lift measurement, it is known that the “pad correction”, the correction in the lift measurement values for the undesirable aerodynamic force acting on wheel belt surface around the tire contact patch, must be accounted. Due to the pad correction measurement difficulties, it is common to simply subtract a fixed amount of lift values from measured lift force. However, this method is obviously not perfect as the pad corrections are different for differing vehicle body shapes, aerodynamic configurations, tire sizes and shapes.

The advent of autonomous driving features has brought about the rise of new automotive technologies such as drive-by-wire. But the implementation of these technologies on physical vehicles is more complex, creating a need for simulation, model-based development, and testing of these systems before they are implemented in an actual vehicle. The real-time simulations capabilities of MATLAB and Simulink provide a robust development platform for behavioral cloning. The most common high-fidelity model used for simulation of vehicle dynamics is the 14 DoF model. Ackerman steering geometry has been around for two centuries and it is the most commonly used steering geometry for passenger cars. Yet, 14 DoF vehicle simulation models in literature have been observed to be using parallel steering geometry due to their simplicity.

For the improvement of the vehicle yaw stability, this paper studies the control problem of the active front steering (AFS) system with actuator input delay. A novel sliding mode predictive control method to handle actuator input delay is proposed for the AFS system. Firstly, considering the nonlinearities of the vehicle system, a linear parameter varying vehicle system model with two-level structure is proposed to capture the vehicle dynamic behaviors. Secondly, to deal with the issues of actuator input delay and system constraints, a novel sliding mode predictive control method is put forward. In the process of controller design, a sliding mode control algorithm is employed for the improvement of the robustness of the control system, and then a model predictive control algorithm is employed to deal with system constraints.

With the rapid development of the logistics and transportation industry, heavy-duty trucks play an increasingly important role in social life. However, due to the characteristics of large cargo loads, high center of mass and relatively narrow wheelbase, the driving stability of heavy trucks are poor, and it is easy to cause rollover accidents under high-speed driving conditions, large angle steering and emergency obstacle avoidance. To improve the roll stability of heavy trucks, it is necessary to design an active anti-rollover control system, through the analysis of the yaw rate and the load transfer rate of the vehicle, driving states can be estimated during the driving process. Under the intervention of the control system, the lateral transfer rate of heavy trucks can be reduced to correct the driving posture of the vehicle body and reduce the possibility of rollover accidents.

As turbulence modeling has become an indispensable approach to perform flow simulation in a wide range of industrial applications, how to enhance the prediction accuracy has gained increasing attention during the past years. Of all the turbulence models, RANS is the most common choice for many OEMs due to its short turn-around time and strong robustness. However, the default setting of RANS is usually benchmarked through classical and well-studied engineering examples, not always suitable for resolving complex flows in specific circumstances. Many previous researches have suggested a small tuning in turbulence model coefficients could achieve higher accuracy on a variety of flow scenarios. Instead of adjusting parameters by trial and error from experience, this paper introduced a new data-driven method of turbulence model recalibration using adjoint solver, based on Generalized k-ω (GEKO) model, one variant of RANS.

A drag coefficient, which is representative of the drag of a car undergoing a particular drive cycle, known as the cycle-averaged-drag coefficient, has been previously developed. It was derived for different drive cycles using mean values for the natural wind. It assumed terrain dependent wind velocities based on the Weibull function, equi-probable wind direction and shear effects. It did not, however, include any effects of turbulence in the natural wind. Some recent research using active vanes in the wind tunnel to generate turbulence has suggested that the effect on drag can be evaluated from the quasi steady wind inputs. On this basis a simple quasi-steady theory for the effect of turbulence on car drag is developed and applied to predicting the cycle-averaged-drag coefficient for a range of cars of different types. The drag is always increased by the turbulence but in all cases is relatively small.

JUNO is an urban concept vehicle (developed at the Politecnico of Torino), equipped by an ethanol combustion engine, designed to obtain low consumptions and reduced environmental impact. For these goals the main requirements that were considered during the designing process were mass reduction and aerodynamic optimization, at first on the shape of the car body and then, thanks to add-on devices. JUNO’s aerodynamic development follows a defined workflow: geometry definition and modelling, CFD simulations and analysis, and finally geometry changes and CFD new verification. In this paper the results of the CFD simulations (using STARCCM+ and RANS k-ε) with a corresponding 1/1 scale wind tunnel tests made using the real vehicle. Particularly, the results in term of: total drag coefficient (Cx), total lift coefficient (Cz), the total pressure in the side and rear analyzing twenty different aerodynamics configurations made up of different combination of some aerodynamics add-on devices.

Tire tread pattern noise is a major source of road noise generated by motor vehicles. Recently, noise control technology has been developing, and low-noise motor vehicles, such as electric vehicles and hybrid vehicles, have been commercialized. The importance of low-noise tires has increased since regulations R117 for tire noise and R51.03 for motor vehicle noise have been strengthened. To evaluate the tire noise in the development stage of motor vehicles, finished products of tires are required; hence, financial and time costs should be invested. Therefore, it is highly useful to predict tire noise levels in the early stages. Recently, a technology to predict the tire pattern noise using a supervised training method of artificial neural network (ANN) has been developed. The tire tread depth is estimated using the shading of the full image of the actual tire, and the leading edge of the contact patch is calculated using tire contact patch images.

The difficulties of testing a bluff automotive body of sufficient scale to match the on-road vehicle Reynolds number in a closed wall wind tunnel has led to many approaches being taken to adjust the resulting data for the inherent interference effects. But it has been impractical if not impossible to experimentally analyze the effects that are occurring on and around the vehicle when these blockage interferences are taking place. The present study is an extension of earlier work by the author and similarly to that study uses the CFD (computational fluid dynamics) analysis of several bodies of differing configurations to examine the interference phenomena in solid wall wind tunnels and the effects that they have on the pressures, forces and force increments experienced by the vehicle model. This is accomplished by executing a series of CFD configurations with varying sized cross sections from 0.2% to 13% blockage enabling an approximation of free air conditions as a reference.

This paper presents calculations of external car aerodynamics by using the finite volume (FV) immersed-boundary method. The FV numerical codes primarily employ Reynolds-Averaged Navier-Stokes (RANS) models. In recent years, and due to possibility to run very large computational meshes, these models are usually used in conjunction with the advanced near-wall models. Moreover, it has been often demonstrated that the accuracy of RANS near-wall models relies on the mesh quality near the wall so by the rule, larger number of wall body-fitted cell-layers are employed. An immersed boundary (IB) method becomes an attractive alternative to the ‘standard’ FV approaches especially when applied to low quality CAD data. In general, the IB method is less investigated and validated for the car aerodynamics, particularly in conjunction with advanced near-wall turbulence models and an adaptive mesh refinement (AMR).

The automotive industry is facing new emission regulations, changing customer preferences and technology disruptions. All have in common, that external aerodynamics plays a crucial role to achieve emission limits, reduce fuel consumption and extend electric driving range. Probably the most challenging components in terms of numerical aerodynamic drag prediction are the wheels. Their contribution to the overall pressure distribution is significant, and the flow topology around the wheels is extremely complicated. Furthermore, deltas between different rim designs can be very small, normally in the range of only a few drag counts. Therefore, highly accurate numerical methods are needed to predict rim rankings and deltas. This paper presents experimental results of four different production rim designs, mounted to a modified production car. An accurate representation of the loaded, deformed tire geometry is used in all calculations for comparable conditions between wind tunnel and CFD.

With the evolution of telemetry technology in vehicles, Advanced Automatic Collision Notification (AACN), which detects occupants at risk of serious injury in the event of a crash and triages them to the trauma center quickly, may greatly improve their treatment. An Injury Severity Prediction (ISP) algorithm for AACN was developed using a logistic regression model to predict the probability of sustaining an Injury Severity Score (ISS) 15+ injury. National Automotive Sampling System Crashworthiness Data System (NASS-CDS: 1999-2015) and model year 2000 or later were filtered for new case selection criteria, based on vehicle body type, to match Subaru vehicle category. This new proposed algorithm uses crash direction, change in velocity, multiple impacts, seat belt use, vehicle type, presence of any older occupant, and presence of any female occupant.

The Global Positioning System (GPS) is a satellite-based radio navigation system, which provides geo location, navigation and real time tracking information. It is used during Non-Line of Sight (NLOS) situations, when the weak GPS signals get obstructed by mountains, etc. However, the functionality of GPS tracking devices has evolved since its early inception owing to its countless benefits. Instead of mainly focusing on tracking, they are used for various applications such as inhibiting car theft and support for road accidents. This development in the field of satellite communication has increased the demand for satellite services from all sectors especially the automotive industry. The GPS antenna is the backbone of the GPS system. Therefore, we require a commercially viable design that meets the size (0.105λo x 0.105λo x 0.02λo) requirement of the automobiles. In this paper, we discuss the substrate material solutions to design an affordable yet flexible GPS antenna.