Search Results

Lower engine emission is an important target in the evaluation of the control strategy of ECU. So the hardware in the loop simulation system (HILSS) including emission model is necessary. In this paper, a NOx emission neural network (NN) model is constructed based on the reflection relationship between the NOx formation and some direct influence factors such as concentration of oxygen, combustion temperature, combustion period. Combined with a nonlinear dynamic diesel engine model based on the filling and emptying methods, the NOx emission NN model can reach the trade-off between simulation accuracy and computational overhead. A new HILS platform based on Matlab/RTW and VxWorks real time operating system is introduced in the paper. The graphic programming and automatic code generating methods also developed to accelerate the development of HILSS.

In this work, a XD132 Road Roller from XCMG in China was employed as a research basis to study the heat exchange performance of the heat dissipation module under varied working conditions. The module in the XD132 consists of a cooling fan and three radiators. At first, the numerical investigation on the elementary units of radiators was performed to obtain Colburn j factor and Fanning friction f factor, which were used for the ε-NTU method to predict the radiator performance. The fan was numerically tested in a wind test tunnel to acquire the performance curve. The performance data from both investigations were transformed into the boundary conditions of the numerical vehicle model in a virtual tunnel. A field experiment was carried out to validate the simulation accuracy, and an entrance coefficient was proposed to discuss the performance regularity under four working conditions.

This paper focuses on dynamic analysis and frame optimization of a FSAE racing car frame. Firstly, a Multi-Body Dynamic (MBD) model of the racing car is established using ADAMS/Car. The forces and torques of the mechanical joints between the frame and suspensions are calculated in various extreme working conditions. Secondly, the strength, stiffness and free vibration modes of the frame are analyzed using Finite Element Analysis (FEA). The extracted forces and torques in the first step are used as boundary conditions in FEA. The FEA results suggest that the size of the frame may be not reasonable. Thirdly, the size of the frame is optimized to achieve minimized weight. Meanwhile the strength and stiffness of the frame are constrained. The optimization results reveal that the optimization methodology is powerful in lightweight design of the frame.

The road adhesion coefficient has a great impact on the performance of vehicle tires, which in turn affects vehicle safety and stability. A low coefficient of adhesion can significantly reduce the tire's traction limit. Therefore, the measurement of the coefficient is much helpful for automated vehicle control and stability control. Considering that the road adhesion coefficient is an inherent parameter of the road and it cannot be known directly from the information of the on-vehicle sensors. The novelty of this paper is to construct a road adhesion coefficient observer which considers the noise of sensors and measures the unknown state variable by the trained neural network. A Butterworth filter and Adaptive Neural Fuzzy Interference System (ANFIS) are combined to provide the lateral and longitudinal velocity which cannot be measured by regular sensors.

For automated driving vehicles, path planning and trajectory tracking are the core of achieving obstacle avoidance. Real-time external environment perception and vehicle state monitoring play the important role in the decision-making of vehicle operation. Sensor measuring is an important way to obtain vehicle state parameters, but some parameters cannot be measured due to sensor cost or technical reasons, such as vehicle lateral velocity and side-slip angle. This disadvantage will adversely affect the monitoring of vehicle self-condition and the control of vehicle running, even it will lead to erroneous decision-making of vehicles. Therefore, this paper proposes an automated driving path planning and trajectory tracking control method based on Kalman filter vehicle state observer. Some of vehicle state data can be measured accurately by sensors.

In autonomous driving, variations in tire vertical load, tire slip angle, road conditions, tire pressure and tire friction all contribute to uncertainty in tire cornering stiffness. Even the same tire may vary slightly during the manufacturing process. Therefore, the uncertainty of tire cornering stiffness has an important influence for autonomous driving path planning and control strategies. In this paper, the Chebyshev interval method is used to represent the uncertainty of tire cornering stiffness and is combined with a model predictive control algorithm to obtain the trajectory interval bands under local path planning and tracking control. The accuracy of the tire cornering stiffness model and the path tracking efficiency are verified by comparing with the path planning and control results without considering the corner stiffness uncertainties.

Suspension kinematics and compliance strongly influence the handling performance of the vehicle. The kinematics and compliance characteristics are determined by the suspension geometry and stiffness of suspension bodies and elastic components. However, it is usually inefficient to model all the joints, bushings, and linkage deformation in a full vehicle model. By transforming the complex modeling problem into a data-driven problem tends to be a good solution. In this research, the neural-network-based suspension kinematics and compliance model is built and implemented into a 17 DOF full vehicle model, which is a hybrid model with state variables expressed in the global coordinate system and vehicle coordinate system. The original kinematics and compliance characteristics are derived from multibody dynamics simulation of the suspension system level.

Trajectory and velocity tracking are currently one of the core issues in autonomous vehicle control. However, most studies deal with them separately which may cause vehicle instability under extreme conditions. In this paper, a coupled longitudinal and lateral control strategy of trajectory tracking for autonomous vehicles is presented. A lateral controller is implemented with a Linear Time-Varying MPC (LTV-MPC) to generate the front steering angle required for trajectory tracking. The side-slip angle is constrained within an interval to prevent tire saturation. Furthermore, a velocity regulation module in which the reference velocity is calculated considering the curvature of the trajectory and the lateral stability criteria is designed. A longitudinal controller is proposed to provide the traction torque with the obtained reference velocity to cope with the longitudinal velocity tracking problem.

In contemporary trajectory planning research, it is common to rely on point-mass model for trajectory planning. However, this often leads to the generation of trajectories that do not adhere to the vehicle dynamics, thereby increasing the complexity of trajectory tracking control. This paper proposes a local trajectory planning algorithm that combines sampling and sequential quadratic optimization, considering the vehicle dynamics model. Initially, the vehicle trajectory is characterized by utilizing vehicle dynamic control variables, including the front wheel angle and the longitudinal speed. Next, a cluster of sampling points for the anticipated point corresponding to the current vehicle position is obtained through a sampling algorithm based on the vehicle's current state. Then, the trajectory planning problem between these two points is modeled as a sequential quadratic optimization problem.

The present study introduces a novel approach for achieving path tracking of an unmanned bicycle in its local body-fixed coordinate frame. A bicycle is generally recognized as a multibody system consisting of four distinct rigid bodies, namely the front wheel, the front fork, the body frame, and the rear wheel. In contrast to most previous studies, the relationship between a tire and the road is now considered in terms of tire forces rather than nonholonomic constraints. The body frame has six degrees of freedom, while the rear wheel and front fork each have one degree of freedom relative to the body frame. The front wheel exhibits a single degree of freedom relative to the front fork. A bicycle has a total of nine degrees of freedom.