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With the development of the advanced driver assistance system and autonomous vehicle techniques, a precise description of the driver’s steering behavior with mathematical models has attracted a great attention. However, the driver’s steering maneuver demonstrates the stochastic characteristic due to a series of complex and uncertain factors, such as the weather, road, and driver’s physiological and psychological limits, generating negative effects on the performance of the vehicle or the driver assistance system. Hence, this paper explores the stochastic characteristic of driver’s steering behavior and a novel steering controller considering this stochastic characteristic is proposed based on stochastic model predictive control (SMPC). Firstly, a search algorithm is derived to describe the driver’s road preview behavior.

The wireless inter-vehicle communication provide a manner to achieve multi-vehicle cooperative driving, and the platoon of automotive vehicle can significantly improve traffic efficiency and ensure traffic safety. Previous researches mostly focus on the state of the proceeding vehicle, and transmit information from self to the succeeding vehicle. Nevertheless, this structure possesses high requirements for controller design and shows poor effect in system stability. In this paper, the state of vehicles is not only related to the information of neighbor vehicles, while V2V communication transmit information over a wide range of area. To begin with, the node dynamic model of vehicle is described by linear integrator with inertia delay and the space control strategy is proposed with different topological communication structures as BF, LBF, PBF, etc.

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly, the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough and in the meter-level, increasing the difficulty of precisely estimating the road grade.

Nowadays, SC is recognized as a key element of hybrid energy storage system in modern energy supply chain for electric vehicles (EVs). Co9S8 as a promising electrode material attracts much attention for supercapacitor owing to its superior electrochemical capacity. However, its poor stability and electronic conductivity, which result in inferior cycling performance and rate capability, have seriously limited the practical application of Co9O8 in supercapacitors. In this article, Co9S8 nanoparticles were embedded in reduced graphene oxide (rGO) via a simple anneal approach as high efficient and stable electrodes for SCs. The Co9S8/rGO composites were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The Co9S8 nanoparticles were inserted tightly between the rGO layers due to strong intermolecular forces, preventing the cluster in reduction process of rGO from graphene oxide (GO).

This research focuses on an integration of two optimal tracking controllers, the active suspension controller and the rear-wheel steering controller, with the objective of improving vehicle performances in terms of maneuverability and safety by enhancing road holding capability and lateral stability. The active suspension controller adjusts the vehicle roll angle and utilizes the vertical force at each active suspension to boost road holding capability. On the other hand, the rear-wheel steering controller adjusts rear steering angles to use lateral force at each ground-tire contact point and amplify the vehicle’s ability to follow the desired yaw rate and sideslip angle during cornering maneuvers. Though the active attitude motion and mass shifting of car body may seem to hold relationship with lateral stability, its ability to evenly distribute vertical tire forces benefits the rear-wheel steering controller by enhancing the road holding capability.

A vehicle-mounted anti-vibration system is designed to semi-actively reduce accelerations acting on vibration isolation platform under different road conditions. To provide the basis for optimal anti-vibration design, the kinematics and dynamics of the platform are analyzed to investigate the relationship between leg length, strength, the platform position and vibration properties. As the platform is fixed on vehicle, a combined vehicle-platform model is necessary for verifying the performance and applying some suitable control algorithms. Also, typical digital testing roads will be built using road load spectrum. To optimize the platform parameters, especially stiffness and damping, an active control system is designed at first. An anti-vibration system including a semi-active inerter is designed to match the control forces which are calculated from the above active system.

Lane-changing is a typical traffic scene effecting on road traffic with high request for reliability, robustness and driving comfort to improve the road safety and transportation efficiency. The development of connected autonomous vehicles with V2V communication provide more advanced control strategies to research of lane-changing. Meanwhile, four-wheel steering is an effective way to improve flexibility of vehicle. The front and rear wheels rotate in opposite direction to reduce the turning radius to improve the servo agility operation at the low speed while those rotate in same direction to reduce the probability of the slip accident to improve the stability at the high speed. Hence, this paper established Four-Wheel-Steering(4WS) vehicle dynamic model and quasi real lane-changing scenes to analyze the motion constraints of the vehicles.