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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.

Electronic hydraulic control system adopted in the conventional vehicle continuously variable transmissions needs to consume energy incessantly for its oil pump driven by engine working continuously. As a result we can reduce the fuel consumption of the engine by removing the pump and replacing the control system with electro-mechanical one. The paper introduces two types of electro-mechanical controlled CVT (EM-CVT). One of them use gear train which is driven by a motor, and the other applies a planetary gear control device which uses spring to provide clamping force and electromagnetism clutches to control the planetary gears. The former is mainly applied to small torque CVT, for its clamping force brought from spring is limited. The latter can offer larger clamping force, and can be applied to prevalent CVT nowadays.

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.

Analysis of pressure transients in brake system is very important for calculating brake force development, especially for vehicles mounted on ABS (Antilock braking system). This paper introduces an analytical dynamic model of the air-over-hydraulic (AOH) brake system mounted on heavy-duty off-highway vehicle (HOV). The paper relies on physical arguments to develop the mathematic models for the brake system components. And then a generalized AOH brake system, based on the systems analysis level for the components, is formulated in detail. The foundation drum brake is presented with a novel modeling method for the interaction with the apply system. And the pipeline hysteresis and fluid fluctuation of the brake system are well researched. Experiments are preformed on a bench setup and a real vehicle of the AOH brake system and the experimental data is compared with the simulation results. Preliminary analysis shows that the simulation tracks the data closely.

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.

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.

The automatic emergency braking (AEB) system is an important part of automobile active safety, which can effectively reduce rear-end collision accidents and protect drivers' safety through active braking. AEB system has been included in many countries' new car assessment programme as the test content of active safety. In view of obviously deficiencies of the existing AEB control algorithm in avoiding longitudinal collision at high speed, it is proposed to an optimized model of the minimum safe distance for rear-end collision prevention on high-speed road in order to improve the accuracy of AEB system. Considering the influence of road adhesion coefficient and human comfort on the maximum braking deceleration, it is established to a more accurate and reasonable AEB system to avoid collision for expressway. The collision avoidance strategy is verified by simulation software.

Intelligent vehicle-to-everything connectivity is an important development trend in the automotive industry. Among various active safety systems, Autonomous Emergency Braking (AEB) has garnered widespread attention due to its outstanding performance in reducing traffic accidents. AEB effectively avoids or mitigates vehicle collisions through automatic braking, making it a crucial technology in autonomous driving. However, the majority of current AEB safety models exhibit limitations in braking modes and fail to fully consider the overall vehicle stability during braking. To address these issues, this paper proposes an improved AEB control system based on a risk factor (AERF). The upper-level controller introduces the risk factor (RF) and proposes a multi-stage warning/braking control strategy based on preceding vehicle dynamic characteristics, while also calculating the desired acceleration.

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.