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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

In this paper, the performance simulation model of a domestic self-dumping truck was established using AVL-Cruise software. Then its accuracy was checked by the power performance and fuel economy tests which were conducted on the proving ground. The power performance of the self-dumping truck was evaluated through standing start acceleration time from 0 to 70km/h, overtaking acceleration time from 60 to 70km/h, maximum speed and maximum gradeability, while the composite fuel consumption per hundred kilometers was taken as an evaluation index of fuel economy. A L9 orthogonal array was applied to investigate the effect of three matching factors including engine, transmission and final drive, which were considered at three levels, on the power performance and fuel economy of the self-dumping truck. Furthermore, the grey relational grade was proposed to assess the multiple performance responses according to the grey relational analysis.

To quickly and efficiently match the planetary gearset characteristic parameter of power-spilt hybrid vehicles so that their oil-saving potential can be maximized, this study proposes a parameter matching method that comprehensively considers energy management strategy and driving cycle based on an analysis of vehicle instantaneous efficiency. The method is used to match the planetary characteristic parameter of a power-split hybrid light truck. The relevant conclusions are compared with the influence of various planetary characteristic parameters on fuel consumption obtained through simulation under typical operating conditions. The simulation results show that the influence laws of the various planetary characteristic parameters on vehicle average efficiency are similar to those on fuel consumption. The proposed parameter-matching method based on vehicle efficiency analysis can effectively match the planetary characteristic parameter for power-split hybrid powertrains.

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model.

Based on the traditional heavy commercial vehicle, hydraulic hub-motor drive vehicle (HHMDV) is equipped with a hydraulic hub-motor auxiliary drive system, which makes the vehicle change from the rear-wheel drive to the four-wheel drive to improve the traction performance on low-adhesion road. In the typical operating mode of the vehicle, the leakage of the hydraulic system increases because of the oil temperature rising, this makes the control precision of the hydraulic system drop. Therefore, a temperature compensation control strategy for the assist mode is proposed in this paper. According to the principle of flow continuity, considering the loss of the system and the expected wheel speed, the control strategy of multifactor target pump displacement based on temperature compensation is derived. The control strategy is verified by the co-simulation platform of MATLAB/Simulink and AMESim.

As a novel assist actuator of brake system, the electromechanical brake (EMB) booster has played a significant role in the battery electric vehicles and automatic driving vehicles. It has advantages of independent to vacuum source, active braking, and tuning pedal feeling compared with conventional vacuum brake booster. In this article, a novel EMB booster system is proposed, which is consisted of a permanent magnet synchronous motor (PMSM), a two-stage reduction by gears and ball screw, a servo body, and a reaction disk. Together with the hydraulic control unit, it has two working modes: active braking for automatic drive and passive braking for driver intervention. The structure and work principle of the electric brake booster system is first introduced. The precise control from pedal force to hydraulic pressure is the key for such a power-assisted brake actuator. We translate the control problem of force feedback control to position tracking control.

With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. To seek for an optimized volumetric ratio for ABE-diesel blends, the previous work in our team has experimentally investigated and analyzed the combustion features of ABE-diesel blends with different volumetric ratio (A: B: E: 6:3:1; 3:6:1; 0:10:0, vol. %) in a constant volume chamber. It was found that an increased amount of acetone would lead to a significant advancement of combustion phasing whereas butanol would compensate the advancing effect. Both spray dynamic and chemistry reaction dynamic are of great importance in explaining the unique combustion characteristic of ABE-diesel blend. In this study, a semi-detailed chemical mechanism is constructed and used to model ABE-diesel spray combustion in a constant volume chamber.

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.

High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate.

This paper intends to present a novel optimal trajectory planning method for obstacle avoidance on highways. Firstly, a mapping from the road Cartesian coordinate system to the road Frenet-based coordinate system is built, and the path lateral offset in the road Frenet-based coordinate system is represented by a function of quintic polynomial respecting the traveled distance along the road centerline. With different terminal conditions regarding its position, heading and curvature of the endpoint, and together with initial conditions of the starting point, the path planner generates a bunch of candidate paths via solving nonlinear equation sets numerically. A path selecting mechanism is further built which considers a normalized weighted sum of the path length, curvature, consistency with the previous path, as well as the road hazard risk.

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.

Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation.

With more and more countries proposing timetables for stopping selling of fuel vehicles, China has also issued a “dual-slope” policy. As electric vehicles are the most promising new energy vehicle, which is worth researching. The integration and control of the motor and gearbox have gradually become a hot research topic due to low cost with better performance. This paper takes an electric vehicle equipped with permanent magnet synchronous motor and two-gear automatic transmission without synchronizer and clutch as the research object.

The driver's face detection and tracking method important for Advanced Driver Assistance Systems (ADAS) and autonomous driving in various situations. The deep SORT algorithm has integrated appearance information, the motion model and the intersection-over-union (IOU) distance methods, and has been applied to face tracking, but it depends on detection information in every frame. Once the detection information lacks, the deep SORT algorithm will wait until the target detects bounding boxes appear again, even if the target didn’t disappear or shield. Hence, we propose to use a new tracker that not completely depend on the detection algorithm to cascade with the deep SORT algorithm to realize stable driver's face tracking. At first, the driver's face detection and tracking will be accomplished by the MTCNN-deep-SORT algorithm.

In this paper, a shock absorber physical model is developed. Firstly, a rebound valve model which is based on its structure parameters is built through using the large deflection theory. The von Karman equations are introduced to discover the physical relationships between the load and the deflection of valve discs. An analytical solution of the von Karman equations is then deducted via perturbation method. Secondly, the flow equations and the pressure equations of the shock absorber operating are investigated. The relationship between fluid flow rate and pressure drop of rebound valve is analyzed based on the analytical solution of valve discs deflection. Thirdly, an inter-iterative process of flow rate and pressure drop is employed in order to adequately consider the influence of fluid flow on damping force. Finally, the physical model is validated by comparing the experimental data with the simulation output.

The new control algorithm for parallel hybrid electric vehicle is presented systematically, in which engine operation points are limited within higher efficient area by the control algorithm and the state of charge (SOC) is limited in a range in order to enhance the batteries' charging and discharging efficiency. In order to determine the ideal operating point of the vehicle's engine, the control strategy uses a lookup table to determine the torque output of the engine. The off-line simulation model of parallel HEV power train is developed which includes the control system and controlled objective (such as engine, electric motor, battery pack and so on). The results show that the control algorithm can effectively limite engine and battery operation points and much more fuel economy can be achieved than that of conventional one.

Based on traditional heavy commercial vehicles, a hydraulic hub-motor drive vehicle (HHMDV) is equipped with a set of hydraulic hub-motor auxiliary system (HHMAS) to improve the traction performance and adaptability under complex conditions. In the case of low-speed operation or mechanical transmission failure, the creep mode (CM) can be used to drive the vehicle. Aiming at a common hydraulic system problem that flow loss increases due to temperature variation, a temperature compensation control strategy of the CM is proposed in this paper. By analyzing the speed regulation characteristics of the closed loop of the system in the CM, combined with the efficiency of the hydraulic variable pump (HP) and the hydraulic quantitative motor (HM), and aiming at adjusting the engine work in the optimal curve of the engine, the temperature compensation factor is introduced to control the HP displacement with hydraulic stepless speed regulation.

The motor errors of the four-wheel independent driving electric vehicles was studied, and classified as steady errors and dynamic errors. In the paper, the forward compensation control and the PID control strategy were respectively applied to compensate for the errors. The electric vehicles straight-line running stability caused by the motor errors was discussed, the PID control method based on BP neural net work was presented to co-ordinate torque of the wheels. The result of the simulation on the 7 degrees of freedom vehicle model showed that control method improved the straight-line running ability of the vehicle.

This paper describes an improved mathematical model to study the emission conversion effectiveness of a three-way catalytic converter, which employed detailed chemical reaction mechanism. The model also accounts for adsorption/release of oxygen in the catalyst monolith under non-stoichiometric A/F conditions. A commercial CFD code FLUENT was utilized to solve the governing equations for flow and pressure drop and to simulate the transient process in a three-way catalytic converter in a multi-dimensional manner. A comparison between simulation results and experimental data for a three-way catalyst was conducted and a good agreement was observed. Based on the improved model, some geometric parameters were studied for an elliptic monolith catalyst, which are widely used in today's converter systems because of its advantages in packaging.

A novel non-destroy repeatable-use impact theory based total cylinder sampling system has been established. This system is mainly composed of a knocking body and a sampling valve. The knocking body impacts the sampling valve with certain velocity resulting in huge force to open the sampling valve and most of the in-cylinder gas has been dumped to one sampling bag for after-treatment. The feasibility and sampling response characteristics of this impact theory based total cylinder sampling system were investigated by engine bench testing. Within 0 to 35°CA ATDC (Crank Angle After Top Dead Center) sample timing 50 percent to 80 percent of in-cylinder mass would be sampled, which was a little less compared with the traditional system. The half decay period of pressure drop was 10 to 20 degrees crank angle within 0 to 60°CA ATDC sample timing, which was about 2-3 times of the traditional system.