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Aiming at the problem of poor robustness after the combination of lateral kinematics control and lateral dynamics control when an autonomous vehicle decelerates and changes lanes to overtake at a certain distance. This paper proposes a trajectory determination and tracking control method based on a PI-MPC dual algorithm controller. To describe the longitudinal deceleration that satisfies the lateral acceleration limit during a certain distance of lane change, firstly, a fifth-order polynomial and a uniform deceleration motion formula are established to express the lateral and longitudinal displacements, and a model prediction controller (MPC) is used to output the front wheel rotation angle. Through the dynamic formula and the speed proportional-integral (PI) controller to control and adjust the brake pressure.

The vehicle axle gear whine noise and vibration are key issues for the automotive industry to design a quiet, reliable driveline system. The main source of excitation for this vibration energy comes from hypoid gear transmission error (TE). The vibration transmits through the flexible axle components, then radiates off from the surface of the housing structure. Thus, the design of hypoid gear pair with minimization of TE is one way to control the dynamic behavior of the vehicle axle system. In this paper, an approach to obtain minimum TE and improved dynamic response with optimal tooth profile modification parameters is discussed. A neural network algorithm, named Back Propagation (BP) algorithm, with improved Particle Swarm Optimization (PSO) is used to predict the TE if some tooth profile modification parameters are given to train the model.

High-pressure common rail (HPCR) fuel injection system is the most widely used fuel system in diesel engines. However, when multiple injection strategy is used, the pressure wave fluctuation is un-avoided due to the opening and closing of the needle valve which will affect the subsequent fuel injection and combustion characteristics. In this paper, several parameters: injection pressure, injection intervals, the main injection pulse widths are investigated on a common rail fuel injection test rig with two injection pulses to explore their effect on the fuel injection rate and fuel quantity. The result showed that the longer injection interval between the pilot and main injections will lead to a rail pressure drop at the beginning of the main injection so that a smaller fuel quantity will be delivered. The main injection pulse width also influences fuel injection rate and the main fuel quantity.

Multi-sensor fusion strategies have gradually become a consensus in autonomous driving research. Among them, radar-camera fusion has attracted wide attention for its improvement on the dimension and accuracy of perception at a lower cost, however, the processing and association of radar and camera data has become an obstacle to related research. Our approach is to build a concise framework for camera and radar detection and data association: for visual object detection, the state-of-the-art YOLOv5 algorithm is further improved and works as the image detector, and before the fusion process, the raw radar reflection data is projected onto image plane and hierarchically clustered, then the projected radar echoes and image detection results are matched based on the Hungarian algorithm. Thus, the category of objects and their corresponding distance and speed information can be obtained, providing reliable input for subsequent object tracking task.

With the rapid rise of intelligent and connected vehicles (CVs), the traffic flow becomes more complex, and the accurate description of the microscopic behavior of the vehicle is crucial for studying the mixed traffic flow. This study firstly analyzes and explores the vehicle’s front-wheel dynamics, and a front-wheel steering model (FWSM) is proposed to describe the vehicle’s lateral motion. In addition, a two-dimensional kinematic intelligent driver model (2D-KIDM) is developed to predict the vehicle’s two-dimensional movement considering the intelligent driver model (IDM) characteristics, vehicular dynamics, and the FWSM. The effectiveness of the proposed 2D-KIDM is evaluated with various simulations in realistic scenarios from the highD dataset. Dynamic time warping (DTW) and some common indexes are also used to analyze the error.

Proton exchange membrane fuel cell (PEMFC) provides a promising future low carbon automotive powertrain solution. The catalyst layer (CL) is its core component which directly influences the output performance. PEMFC performance can be greatly improved by the effective optimization of CL composition. This work demonstrates a deep optimization of CL composition for improving the PEMFC performance, including the platinum (Pt) loading, Pt percentage of carbon-supported Pt and ionomer to carbon ratio of the anode and the cathode,. The simulation results by a PEMFC three-dimensional (3D) computation fluid dynamics (CFD) model coupled with the CL agglomerate model is used to train the artificial neural network (ANN) which can efficiently predict the current density under different CL composition. Squared correlation coefficient (R-square) and mean percentage error in the training set and validation set are 0.9867, 0.2635% and 0.9543, 1.1275%, respectively.