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Function and performance test of automated vehicles in the closed field is a necessary way to verify their safety, intelligence and comfort. The design and number of test scenarios will influence if the automated vehicles can be tested and evaluated effectively and fast. Based on the interrelationship among the vehicle, driver’s (or control system) driving strategy and road, we use the permutation and combination method to compare the relative position and movement relations between an automated vehicle (vehicle under test) and the surrounding vehicles to generate a total possible test scenarios group.

UGV (Unmanned Ground Vehicle) is gaining increasing amounts of attention from both industry and academic communities in recent years. Local trajectory planning is one of the most important parts of designing a UGV. However, there has been little research into local trajectory planning and tracking, and current research has not considered the dynamic of the surrounding environment. Therefore, we propose a dynamic local trajectory planning and tracking method for UGV driving on the highway in this paper. The method proposed in this paper can make the UGV travel from the navigation starting point to the navigation end point without collision on both straight and curve road. The key technology for this method is trajectory planning, trajectory tracking and trajectory update signal generation. Trajectory planning algorithm calculates a reference trajectory satisfying the demands of safety, comfort and traffic efficiency.

Previous study suggested that female, thin, obese, and older occupants had a higher risk of death and serious injury in motor vehicle crashes. Human body finite element models were a valuable tool in the study of injury biomechanics. The mesh deformation method based on radial basis function(RBF) was an attractive alternative for morphing baseline model to target models. Generally, when a complex model contained many elements and nodes, it was impossible to use all surface nodes as landmarks in RBF interpolation process, due to its prohibitive computational cost. To improve the efficiency, the current technique was to averagely select a set of nodes as landmarks from all surface nodes. In fact, the location and the number of selected landmarks had an important effect on the accuracy of mesh deformation. Hence, how to select important nodes as landmarks was a significant issue. In the paper, an efficient peak point-selection RBF mesh deformation method was used to select landmarks.

Cycloid drives are widely used in the in-wheel motor for electric vehicles due to the advantages of large ratio, compact size and light weight. To improve the transmission efficiency and the load capability and reduce the manufacturing cost, a novel cycloid drive with non-pin design for the application in the in-wheel motor is proposed. Firstly, the generation of the gear pair is presented based on the gearing of theory. Secondly, the meshing characteristics, such as the contact zones, curvature difference, contact ratio and sliding coefficients are derived for performance evaluation. Then, the loaded tooth contact analysis (LTCA) is performed by establishing a mathematical model based on the Hertz contact theory to calculate the contact stress and deformation.

The control strategy of switched reluctance motor (SRM) in-wheel motor is investigated in order to reduce the influence of torque ripple of SRM on the ride comfort. The nonlinear model of switched reluctance motor (SRM) is established and the variable angle control strategy with optimal switch angles is applied to control SRM. However, the variable angle control strategy can not reduce the torque ripple of SRM significantly. Therefore, some advanced control strategies are developed to improve the ride comfort in electric vehicle. In this paper, the fuzzy proportional integration differential (PID) is developed to improve the torque ripple of SRM in which the fuzzy control idea is utilized to adjust the parameters of proportional integration differential (PID) control online and ensure the adaptive capabilities of the fuzzy proportional integration differential (PID) control to motor driving system.

Vehicle weight reduction has become one of the essential research areas in the automotive industry. It is important to perform design optimization of Body-in-White (BIW) at the concept design phase so that to reduce the development cost and shorten the time-to-market in later stages. Finite Element (FE) models are commonly used for vehicle design. However, even with increasing speed of computers, the simulation of FE models is still too time-consuming due to the increased complexity of models. This calls for the development of a systematic and efficient approach that can effectively perform vehicle weight reduction, while satisfying the stringent safety regulations and constraints of development time and cost. In this paper, an efficient BIW weight reduction approach is proposed with consideration of complex safety and stiffness performances. A parametric BIW FE model is first constructed, followed by the building of surrogate models for the responses of interest.

Wiper noise generated in the wiping process is one of the main influence factors affecting the driving comfort. Since the dynamic contact pressure of the contact between a blade and a windshield glass is difficult to be measured, it is also difficult to predict the degree of the wiper noise. In this paper, in the view of the reversal noise problem of a passenger-vehicle windscreen wiper system, the system dynamic models of the both wipers on the sides of the driver and copilot were built as considering the blade deformation and the elastic contact between the blades and the windscreen glass, including the crank pivot, the four linkage mechanism, the wiper blades, the wiper arms and the windscreen glass. The motion of the wiper system and the pressure distributions between the blades and the windscreen glass were analyzed under the half-dry condition.

The serpentine belt's multi-scale problems in geometric size, which gives rise to a very large number of element and deeply low calculating efficiency, always bring obstacles when predicting the dynamic response of a serpentine belt driving system using three-dimensional finite element model (FEM). In this paper, a simplified finite element model is built which can accurately present the original serpentine belt's geometric characteristics such as cross-area and moment of inertia, as well as material characteristics such as stiffness and damping, etc. This simplified model is then used in a three-dimensional belt-drive model to simulate the dynamic characteristics of the belt-drive system. The results show that the tension fluctuation for the original serpentine belt and the simplified belt are in good agreement with each other which confirms that the simplified belt model can be used to predict the engine front end accessory drive system (EFEADS)'s dynamic characteristics.

In partly autonomous driving such as level 2 or level 3 automatic driving from SAE international classification, the switching of the driving right between the human driver and the machine plays an important role in the driving process of vehicle [1]. In this paper, the data collected from vehicle states and the driving behavior of drivers is completed via a simulator and self-report questionnaires. A fuzzy analytic network process (Fuzzy-ANP) model is developed to evaluate the driving capability of the drivers in real time from vehicle states due to its direct inherent link to the change of the driving state of drivers Moreover, in this model, the idea of group decision and multi-index fusion is adopted. The questionnaire is required to identify the experimental results from the simulator. The results show that the proposed Fuzzy-ANP model can evaluate the driving capability of the participants in real time accurately.

Automotive powertrain mounting system (PMS) plays a key role in the vibration isolation and the comfort improvement in vehicle. So far, most of powertrain is modeled as a rigid body in 6 Degrees of Freedom (DOF) in research. Few comprehensive and overall optimization are considered which addresses the excitation of the powertrain, the vibration and noise inside the body and the transmission path of vibration together. In this paper, a 13-DOF model including automotive powertrain mounting system and the full vehicle is developed in order to achieve comprehensive and overall optimization for PMS. The minimum of vertical vibration at seat track and the noise at driver ear on the right side, the maximum of system's vibration isolation rate and the energy decoupling rate, the reasonable allocation of system natural frequencies are considered as the optimization targets. Genetic algorithm is used to solve the multi-objective optimization problem.

The research on coasting resistance is vital to electric vehicles, since the smaller the coasting resistance, the longer the coast-down distance. Vehicle coast resistance consists of rolling resistance, vehicle inner resistance and the aerodynamic drag. The vehicle inner resistance is mainly caused by driveline’s friction loss and oil splash loss. The rolling resistance is decided by tire resistance coefficient, which is influenced by tires and road conditions. And the aerodynamic drag is affected by vehicle’s shape and air. In this paper, four factors including tire pressure, road surface condition, atmosphere temperature, and recirculation on or off are examined. Experimental tests have been conducted on three different vehicles: one subcompact sedan, one compact sedan and one subcompact SUV. Then experimental results have been imported to simulation model to investigate the corresponding influence on NEDC range.

Automated driving is considered a key technology for reducing traffic accidents, improving road utilization, and enhancing transportation economy and thus has received extensive attention from academia and industry in recent years. Although recent improvements in artificial intelligence are beginning to be integrated into vehicles, current AD technology is still far from matching or exceeding the level of human driving ability. The key technologies that need to be developed include achieving a deep understanding and cognition of traffic scenarios and highly intelligent decision-making. Automated Vehicles, the Driving Brain, and Artificial Intelligenceaddresses brain-inspired driving and learning from the human brain's cognitive, thinking, reasoning, and memory abilities. This report presents a few unaddressed issues related to brain-inspired driving, including the cognitive mechanism, architecture implementation, scenario cognition, policy learning, testing, and validation.

Road load data is an essential input to evaluate vehicle durability and strength performances. Typically, load case of pothole impact constitutes the major part in the development of structural durability. Meanwhile, misuse conditions like driving over a curb are also indispensable scenarios to complement impact strength of vehicle structures. This paper presents a methodology of establishing Multi-body Dynamics (MBD) full vehicle model in Adams/Car to acquire the road load data for use in durability and strength analysis. Furthermore, load level between durability and misuse conditions of the same Impact road was also investigated to explore the impact due to different driving maneuvers.

Accurate estimation of battery state of health (SOH) has become indispensable in ensuring the predictive maintenance and safety of electric vehicles (EVs). While supervised machine learning excels in laboratory settings with adequate SOH labels, field-based SOH data collection for supervised learning is hindered by EVs' complex conditions and prohibitive data collection costs. To overcome this challenge, a battery SOH estimation method based on semi-supervised regression is proposed and validated using field data in this paper. Initially, the Ampere integral formula is employed to calculate SOH labels from charging data, and the error of labeled SOH is reduced by the open-circuit voltage correction strategy. The calculation error of the SOH label is confirmed to be less than 1.2%, as validated by the full-charge test of the battery packs.

In the field of automobile development, sufficient structure strength is the most basic objective to be accomplished. Typically, method of strength analysis could be divided into static strength and dynamic strength. Analysis of static strength constitutes the major part of the development, but the supplement of dynamic strength is also dispensable to assure structural integrity. This paper presents a methodology about analyzing the impact strength of body structure based on a Multi-body Dynamics (MBD) and Finite Element Analysis (FEA) combined method. Firstly, the full vehicle MBD model consists of Curved Regular Grid (CRG) road model, Flexible Ring Tire (FTire) model and dynamic deflection-force bump stop model was built in Adams/Car. Next, Damage Initiation and Evolution Model (DIEM) failure criteria was adopted to describe material failure behavior.