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Torque Vectoring Control for distributed drive electric vehicle is studied. A handling improvement algorithm for normal cornering maneuvers is proposed based on state variable feedback control: Yaw rate feedback together with steer angle feedforward is employed to improve transient response and steady gain of the yaw rate, respectively. According to the feedback coefficient's influence on the transient response, an optimization function is proposed to obtain optimum feedback coefficients under different speeds. After maximum feedforward coefficients under different speeds are obtained from the constraint of the motor exterior characteristic, final feedforward coefficients are calculated according to an optimal steering characteristic. A torque distribution algorithm is presented to help the driver to speed up during the direct yaw moment control.

Although Hydrostatic Transmission System (HTS) had been used in many places, such as machine tools, agriculture machinery, construction machinery, and vehicles, it had not been used in good performance. Twenty years ago many people began to design new hydrostatic transmission with higher efficiency. Hydrostatic Energy Storage Transmission System (HESTS) is one of new hydrostatic transmission system with higher efficiency. HESTS is more fit for being used in vehicle that is always running in undulating ground or starting and braking frequently. Construction of vehicular HESTS was analyzed, mathematical model of vehicular HESTS was established. The needed control strategies of vehicular HESTS were analyzed because there are many variables would be controlled in the new transmission system.

After obtaining the optimal trajectory through the lane change decision and trajectory planning, the last key technology for the automatic lane change assist system is to carry out the precise and rapid steering actuation according to the front wheel angle demand. Therefore, an automatic lane change system model including a BLDCM (brushless DC motor) model, a steering system model and a vehicle dynamics model is first established in this paper. Electromagnetic characteristics of the motor, the moment of the inertia and viscous friction etc. are considered in these models. Then, a SMC (Sliding Mode Control) algorithm for the steering system is designed to follow the steering angle input. The control torque of the steering motor is obtained through the system model according to steering angle demand. After that, the control current is calculated considering of electromagnetic characteristics of the BLDCM. Debugging and optimization of the control algorithm are done through simulations.

The automatic lane change assist system is an intelligent driving assistance technology oriented to traffic safety, which requires trajectory planning of the lane change maneuver based on the lane change decision. A typical scene of lane change for overtaking is selected, where the front vehicle in the same lane and the rear vehicle in the left lane are deemed to be potential dangerous vehicles through the lane change. Lane change trajectory equation is first established according to the general law of steering wheel angle through lane changes. Based on the relative position, velocity and acceleration information of the dangerous vehicles and the lane change vehicle, motions of these surrounding dangerous vehicles are predicted. At the same time, a multi-objective optimization function is established based on the relative longitudinal safety boundary. The objectives are the minimum safety distance, the lane change time and the front wheel angle.

Vehicle and pedestrian detection technology is the most important part of advanced driving assistance system (ADAS) and automatic driving. The fusion of millimeter wave radar and camera is an important trend to enhance the environmental perception performance. In this paper, we propose a method of vehicle and pedestrian detection based on millimeter wave radar and camera. Moreover, the proposed method complete the detection of vehicle and pedestrian based on dynamic region generated by the radar data and sliding window. First, the radar target information is mapped to the image by means of coordinate transformation. Then by analyzing the scene, we obtain the sliding windows. Next, the sliding windows are detected by HOG features and SVM classifier in a rough detect. Then using the match function to confirm the target. Finally detecting the windows in a precision detection and merging the detecting windows. The target detection process is carried out in the following three steps.

The weight of an exhaust system on a modern vehicle is increasing because of all kinds of reasons, like engine power's increasing, more catalysts for emission control and more NVH (Noise, Vibration and Harshness) performance requirements. After the engine starting, the exhaust system was not only bearing a cyclical load from the engine, which mainly causing the vibration of the exhaust system, but also the loads from the road, which was transferred through the wheels, the suspension system and the body. Because the exhaust system always worked in these bad conditions, its structural strength, durability and life-time were analyzed in the paper, by numerical simulation and physical correlation. By discretizing the exhaust system's CAD model, a finite element model was built. After restrict the finite element model as it in a real load condition, complete the structure stress analysis and Fatigue analysis of exhaust system's hanger with FEA analysis tools.

Brake squeal shows a significant uncertainty characteristic. In this paper, a series of bench tests were carried out to study the uncertainty of brake squeal on a multi-function brake inertia dynamometer test bench. Then based on time-frequency analysis results, a creative squeal confirmation and determination method was presented, which can show the squeal variations in the domains of time, frequency and amplitude together. An uncertainty analysis method was also established, in which the statistical parameters of squeal frequency and sound pressure level (SPL), and probability density evaluation of frequency based on Quantile-Quantile Plot (QQ plot) were given. And a judgment method of the frequency doubling was devised based on numerical multiple and occurrence concurrence, as well as the uncertainty statistical analysis method considering frequency doubling. All the methods established were applied to the uncertainty analysis of brake squeal.

In this paper, the system identification theory and method using dynamic neural networks are presented, the multilayer feedforward networks employed, the backpropagation with adaptive learning rate algorithms proposed. Finally the comparision of network output with that of the hydrostatic drive system of secondary regulation is given, and output error, sum-squared error et al, or the results that embody the effect of system identification given sine input to it are provided.

With the development of autonomous vehicle technology, there is an increasing tendency toward the application of intelligent sensors in environment-perception system on autonomous vehicle to assist vehicle in intelligent decision making relevant to autonomous driving. As for environment-perception system, a good track management method serves as the foundation, while multi-target tracking and multi-sensor data fusion are recognized as the key. In this paper, a track management method is proposed to deal with multi-target tracking based on the target-level data of multisource environmental sensors for autonomous vehicle. The track management includes four procedures as following: track initiation; point-track association; track update; track deletion. A modified fast algorithm for data association is applied in the point-track association procedure. Afterwards Kalman filter is implemented to update the track information of target. The algorithm has got through a simulation test.

Millimeter wave automotive radar is one of the most important sensors in the Advanced Driver Assistance System (ADAS) and autonomous driving system, which detects the target vehicles around the ego vehicle via processing transmitted and echo signals. However, the sampling rate of classical radar signal processing methods based on Nyquist sampling theorem is too high and the resolution of range, velocity and azimuth can’t meet the requirement of highly autonomous driving, especially azimuth. In spatial domain, targets are sparse distribution in the detection range of automotive radar. To solve these problems, the algorithm for targets location based on compressed sensing for automotive radar is proposed in this paper. Besides, the feasibility of the algorithm is verified through the simulation experiments of traffic scene. The range-doppler-azimuth model can be used to estimate the distance, velocity and azimuth of the target accurately.

Target detection is essential to the advanced driving assistance system (ADAS) and automatic driving. And the data fusion of millimeter wave radar and camera could provide more accurate and complete information of targets and enhance the environmental perception performance. In this paper, a method of vehicle and pedestrian detection based on the data fusion of millimeter wave radar and camera is proposed to improve the target distance estimation accuracy. The first step is the targets data acquisition. A deep learning model called Single Shot MultiBox Detector (SSD) is utilized for targets detection in consecutive video frames captured by camera and further optimized for high real-time performance and accuracy. Secondly, the coordinate system of camera and radar are unified by coordinate transformation matrix. Then, the parallel Kalman filter is used to track the targets detected by radar and camera respectively.

In order to solve the problem that in the traditional trilateral positioning algorithm, the final positioning error is large when there is a certain error in the measured three-sided distance, a UWB positioning algorithm based on Back Propagation (BP) neural network is proposed. The algorithm utilizes the fast learning characteristic and the ability of approximating any non-linear mapping of neural network, and realizes the location of the mobile label through the TOA measurement value provided by the base station and the BP neural network. By comparing the traditional trilateral positioning algorithm, the BP neural network algorithm based on four distance inputs and the BP neural network algorithm based on four distance inputs with trilateral positioning coordinates, it can be seen that the positioning error of traditional trilateral positioning algorithm is 30 cm, and the positioning error of the positioning algorithm based on the BP neural network proposed in this paper is 10 cm.

Connected and autonomous vehicles are different from traditional vehicles. The communication between vehicles (V2V) or between vehicles and infrastructures (V2I) renders it possible to convey traffic information (e.g. signal timing or speed advisory) from signal controllers to vehicles as well as vehicles to vehicles in real time. Taking this advantage, this paper aims to developing an algorithm which enables the interconnected autonomous vehicles running efficiently on arterials. A set of driving rules determining random behavior and swarm behavior of autonomous vehicles is developed based on swarm intelligence theory. Under control of these rules, each autonomous vehicle follows the same rules, which make it select target vehicle from all the optimal individuals in detection zone according to characteristics of itself, then approach to the target by changing lane, following former car, or accelerating.

Disc brake squeal has always been a great challenge to the automotive industry. Based on the pin-on-disc system, a series of frictional squeal bench tests are carried out, which show significant time-varying characteristics on occurrence, sound pressure and frequency of frictional squeal. To investigate the generation mechanism of time-varying characteristics of frictional squeal, a four-degree-of-freedom (4DOF) lumped parameter model considering the time-varying tangential contact stiffness, the normal contact stiffness and the friction coefficient is established in this paper. Through both the system stability analysis and the transient response analysis, the time-varying frictional squeal is predicted successfully, and the generation mechanism and the key impact factors are also investigated in depth.

Because of the vehicle market competitive and of the raise of customers' demanding, NVH performance became an important job, especially for new energy vehicles. As the electric vehicle moving into the direction of high speed and large torque, electric vehicle vibration and noise problems highlighted gradually. In recent years, CAE has played an increasing role in the design, development and optimization of powertrain NVH at component and system levels. The subject of this paper was the numerical and experimental evaluation of the electromagnetic and vibro-acoustic behavior of an electric powertrain. For this purpose, a coupled and fully flexible dynamics model of the electric powertrain was developed. Then electromagnetic forces including both radial and tangential force and gear mesh excitations including time-varying meshing stiffness, meshing error and meshing impact were computed, which were used to perform forced response analysis on the full FE mesh of the powertrain housing.

From traditional vehicle to pure electric vehicle and then to range-extended electric vehicle (ReEV), the electrification of vehicle has caused a great change in NVH characteristics. ReEV has the most complex noise and vibration problems because it behaves both like traditional vehicle and pure electric vehicle. In addition, it has its own specific NVH characteristics. Firstly, internal combustion engine drives the generator rather than drives the vehicle. Due to the structure and load change, auxiliary power unit (APU) performs specific NVH characteristics. In addition, the pure electric mode will have the high frequency noise generated from the driveline because the noise couldn't be masked by the engine. In system level, unexpected and confusing sound in passenger compartment may affect the driver's comfort and judgments. Therefore, more and more attentions on these issues should be paid. NVH study and improvement methods on ReEV are emphasized.

In order to reduce high-frequency harmonic noise produced by the blower in the auxiliary system of a fuel cell vehicle (FCV), a narrowband active noise control (ANC) method instead of conventional passive mufflers is adopted since the blower demands clean air condition and expects good acoustic performance. However, in ANC practical applications, the frequency difference between reference signal and actual primary signal, i.e., frequency mismatch (FM), can significantly degrade the high-frequency performance of narrowband ANC system. In this paper, a new narrowband ANC system is proposed to compensate for the performance degeneration due to the existence of FM and improve noise reduction at high frequencies. The proposed system consists of two parts: the Filtered Error Least Mean Square (FELMS) algorithm filtering the primary signals at wide frequency range other than those at the targeted frequencies, and the FM removal algorithm proposed by Yegui Xiao.