Search Results

During the launch of a car, severe torsional vibration sometimes may occur in its driveline due to somewhat the slipping of the clutch, its intuitive sense for an occupant is the longitudinal vibration of the vehicle, referred to as the launch shudder whose characteristic frequency is from 5 to 25 Hz generally. As the main vibration sources of the driveline and its crucial nonlinear components, the variable stiffness and backlash of the gear meshing are considered, their impacts on the launch shudder are analyzed in this paper. Conformal mapping, finite element method and regression method etc. are the main approaches to calculate the variable meshing stiffness of a gear pair. If this stiffness is get, it can usually be substituted for its approximate analytical expression, just with finite harmonic terms, in Fourier Series form into Ordinary Differential Equations(ODEs) to calculate the vehicle responses with its nonlinearity considered.

Environmental perception is a crucial subsystem in autonomous vehicles. In order to build safe and efficient traffic transportation, several researches have been proposed to build accurate, robust and real-time perception systems. Camera and LiDAR are widely equipped on autonomous self-driving cars and developed with many algorithms in recent years. The fusion system of camera and LiDAR provides state-of the-art methods for environmental perception due to the defects of single vehicular sensor. Extrinsic parameter calibration is able to align the coordinate systems of sensors and has been drawing enormous attention. However, differ from spatial alignment of two sensors’ data, joint calibration of multi-sensors (more than two sensors) should balance the degree of alignment between each two sensors.

Lithium-ion batteries (LIBs) with relatively high energy, power density and eco-friendly characteristic are considered as a vital energy source in consumer market of portable electronics and transportation sector especially in electric vehicles (EVs). To meet the higher capacity requirements, the nickel-rich LIBs with higher capacity has been used as the commercial power batteries. However, the battery with higher energy density is more destructive, which could result in thermal runaway accidents and make the battery safety issues become more and more prominent. Thermal runaway of LIBs is one of the key scientific problems in safety issues. Until now, the inducement of thermal runaway process is complicated which perplex researchers and industry a lot. On the one hand, the internal mechanism about thermal runaway should be deeply studied. On the other hand, in-situ monitoring should be developed to supply the mechanism study and early warning.

The vibration characteristics of hybrid vehicles are very different from that of traditional fuel vehicles. In this paper, the active and passive control schemes are used to inhibit the vibration issues in vehicle hybrid powertrain system. Firstly the torsional vibration mechanical model including engine, motor and planetary gear subsystems is established. Then the transient vibration responses of typical working condition are analyzed through power control strategy. Consequently the active and passive control of torsional vibration in hybrid powertrain system is proposed. The active control of the motor and generator torque is designed and the vehicle longitudinal vibration is reduced. The vibration of the planetary gear system is ameliorated with passive control method by adding torsional vibration absorbers to power units. The vibration characteristics in vehicle hybrid powertrain system are effectively improved through the active and passive control.

Millions of configurations of power-split hybrid powertrain can be generated due to variation in number of planetary-gear sets (PG), difference in number, type and installation location of shift actuators (clutches or brakes), and difference in connection positions of power components. Considering the large number of configurations, complex structures and control modes, it is vital to construct an appropriate multi-index system evaluation method, which directly affects the requirement fulfillment, the time and cost of 2-PG system configuration design. Considering one-sidedness (dynamics and economic performance), simplicity (linear combination of indicators) and subjectivity (relying on expert experience) of previous system evaluation method of 2-PG system design, a more systematic evaluation method is proposed in this paper. The proposed evaluation system consists of five aspects, involving dynamic, economy, comfort, reliability and cost, and more than 20 indexes.

In order to explore approaches for improving object detection accuracy in intelligent vehicle system, we exploit super-resolution techniques. A novel method is proposed to confirm the conjecture whether some popular super-resolution networks used for environmental perception of intelligent vehicles and robots can indeed improve the detection accuracy. COCO dataset which contains images from complex ordinary environment is utilized for the verification experiment, due to it can adequately verify the generalization of each algorithm and the consistency of experimental results. Using two representative object detection networks to produce the detection results, namely Faster R-CNN and YOLOv3, we devise to reduce the impact of resizing operation. The two networks allow us to compare the performance of object detection between using original and super-resolved images. We quantify the effect of each super-resolution techniques as well.

Wall temperature in GDI engine is influenced by both water jacket and gas heat source. In turn, wall temperature affects evaporation and mixing characteristics of impingement spray as well as combustion process and emissions. Therefore, in order to accurately simulate combustion process, accurate wall temperature is essential, which can be obtained by conjugate heat transfer (CHT) and piston heat transfer (PHT) models based on mapping combustion results. This CHT model considers temporal interaction between solid parts and cooling water. This paper presents an integrated methodology to reliably predict in-cylinder combustion process and temperature field of a 2.0L GDI engine which includes engine head/block/gasket and water jacket components. A two-way coupling numerical procedure on the basis of this integrated methodology is as follows.

Noise signals of the driver’s right ear include those of engine, environment, chassis dynamometer, loaded gears and unloaded gears when they are recorded in full vehicle on chassis dynamometer in semi-anechoic room. Gear rattle noise signals of the driver’s right ear caused by unloaded gear pairs can’t be identified or quantified directly. To solve the problems, relative approaches are used to identify and quantify the gear rattle noise signals. Firstly, the rattle noise signals of the driver’s right ear are filtered by human ear characteristic functions and steady noise signals are extracted by regression and smoothing processes. The noise signals are regressed at 200ms interval in the hearing critical frequency bands and smoothed in the flanking frequencies. Then, the noise relative approaches are obtained by subtracting the steady noise signals from the filtered noise signals, which are the transient noise signals of the unloaded gear pairs inducing the rattle noise.

Generally, the gear rattle noise prediction models are composed of the mass and stiffness elements. The proposals are about the gear inertia or backlash and the shaft inertia or stiffness, but there are many detailed designs in the same inertia, stiffness or backlash conditions. Therefore, these proposals can’t guide detailed designs. These models only investigate the rattle in the rotating degree, and ignore rattle contribution in the radical and axial directions. Those prediction models only consider one or several factors which affect the rattle noise performance. It is difficult to predict the influence of individual factor and multi-factors coupling on the gear rattle noise in a rattle simulation model.

For sensing system, the trustworthiness of the variant sensors is the crucial point when dealing with advanced driving assistant system application. In this paper, an approach to a hybrid camera-radar application of vehicle tracking is presented, able to meet the requirement of such demand. Most of the time, different types of commercial sensors available nowadays specialize in different situations, such as the ability of offering a wealth of detailed information about the scene for the camera or the powerful resistance to the severe weather for the millimeter-wave (MMW) radar. The detection and tracking in different sensors are usually independent. Thus, the work here that combines the variant information provided by different sensors is indispensable and worthwhile. For the real-time requirement of merging the measurement of automotive MMW radar in high speed, this paper first proposes a fast vehicle tracking algorithm based on image perceptual hash encoding.

A specially designed generator has been developed to produce poly-disperse droplet streams: A liquid fuel (n-heptane) is metered to an ultrasonic atomizer to produce droplets, which are then carried and accelerated vertically upwards through a nozzle tube by carrier-air flow. Conditions of the streams at the nozzle exit are modulated by varying the length of nozzle tubes, the fuel and carrier-air flow rate. Optical measurement techniques such as direct photography method, schlieren photography and particle image velocimetry (PIV) are employed to characterize its performance characteristics. Effects of the nozzle tube length, the carrier-air and fuel flow rate are investigated to evaluate the performance of the generator. Longer nozzle tubes provide a better flow guidance for the carrier-air, and tend to generate streams with less and smaller droplets due to the transporting losses.

In this study, effects of altitude on free diesel spray morphology, macroscopic spray characteristics and air-fuel mixing process were investigated. The diesel spray visualization experiment using high-speed photography was performed in a constant volume chamber which reproduced the injection diesel-like thermodynamic conditions of a heavy-duty turbocharged diesel engine operating at sea level and 1000 m, 2000 m, 3000 m and 4500 m above sea level. The results showed that the spray morphology became narrower and longer at higher altitude, and small vortex-like structures were observed on the downstream spray periphery. Spray penetration increased and spray angle decreased with increasing altitude. At altitudes of 0 m, 1000 m, 2000 m, 3000 m and 4500 m, the spray penetration at 1.45 ms after start of injection (ASOI) were 79.54 mm, 80.51 mm, 81.49 mm, 83.29 mm and 88.92 mm respectively, and the spray angle were 10.9°, 10.8°, 10.7°, 10.4°and 9.8° respectively.

Automotive embedded DCS is widely used to solve automotive control problems. This paper presents a model-driven development technology for such systems. Models of automotive embedded DCS are built up strictly complying with the four-layer-model architecture, which is presented by Model-Driven Architecture (MDA). Three kinds of models are used to describe the protocol data structure, the algorithm process and visualization aspects of automotive embedded DCS. Corresponding XML databases are created based upon these models. As a single data source, these databases play key roles in further development phases, including generating the protocol specification, MC&D systems and embedded programming, etc. Some demonstrative applications are presented in this paper.

Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information.

In order to research the vibration and noise reduction in pure electric vehicle power-train, a comprehensive work is to simulate the power-train incentive of a high-speed pure electric vehicle, and indicates significant impact of gear mesh system on the power-train NVH performance. Therefore, it is necessary to further study the impact of meshing gear system on electric vehicle power-train vibration and noise performance and seek reasonable methods to reduce the vibration and noise. In this paper, a typical pure electric vehicle's powertrain was used to conduct vibration and noise dynamic simulation. Firstly, the power train model was established considering the gear meshing stiffness, transmission errors, bearing factors and shell flexible, then the vibration and sound radiation dynamic response of power-train was simulated. Based on the accuracy of prediction model, a gear tooth modification was carried out for vibration and noise optimization.

Centrifugal Pendulum Vibration Absorber (CPVA for short) is used to absorb torsional vibrations caused by the shifting motion of the engine. It is increasingly used in modern powertrains. In the research of the dynamic characteristics of the CPVA, it is necessary to obtain the real motion of the pendulum to compensate the fitting performance of mathematical model. The usual method is to install an angle sensor to measure the movement of the pendulum. On the one hand, the installation of the sensor will affect its movement to a certain extent, so that the measurement results do not match the actual motion. On the other hand, the motion of the pendulum is not only the rotational motion around the rotational axis of the CPVA rotor, but also has translation relative to it. As a result, it is difficult to obtain accurate motion only by the angle sensor. We proposed a non-contact centrifugal pendulum motion measurement method.

High-speed vehicles in low illumination environments severely blur the images used in object detectors, which poses a potential threat to object detector-based advanced driver assistance systems (ADAS) and autonomous driving systems. Augmenting the training images for object detectors is an efficient way to mitigate the threat from motion blur. However, little attention has been paid to the motion of the vehicle and the position of objects in the traffic scene, which limits the consistence between the resulting augmented images and traffic scenes. In this paper, we present a vehicle kinematics-based image augmentation algorithm by modeling and analyzing the traffic scenes to generate more realistic augmented images and achieve higher robustness improvement on object detectors against motion blur. Firstly, we propose a traffic scene model considering vehicle motion and the relationship between the vehicle and the object in the traffic scene.

Image corruptions due to noise, blur, contrast change, etc., could lead to a significant performance decline of Deep Neural Networks (DNN), which poses a potential threat to DNN-based autonomous vehicles. Previous works attempted to explain corruption from a Fourier perspective. By comparing the absolute Fourier spectrum difference between corrupted images and clean images in the RGB color space, they regard the noise from some corruptions (Gaussian noise, defocus blur, etc.) as concentrating on the high-frequency components while others (contrast, fog, etc.) concentrate on the low-frequency components. In this work, we present a new perspective that unifies corruptions as noise from high frequency and thus propose an image augmentation algorithm to achieve a more robust performance against common corruptions. First, we notice the 1/fα statistical rule of the natural image's spectrum and the channels-wise differential sensitivity on the YCbCr color space of the Human Visual System.

Powertrain system of series-parallel hybrid vehicle contains multiple excitation sources like engine, motor and generator. The reduction of noise and vibration is quite difficult during multiplex working modes or the switch of modes. Aiming at Series-parallel hybrid powertrain system which contains engine, motor and planetary gear subsystems, this paper considered a typical working condition which is based on the power control strategy and established the torsional vibration mechanical model of hybrid powertrain system. The inherent characteristics and transient vibration response of the electric mode, hybrid mode and parking charging mode were studied and it was discovered that the repetitive frequency of the powertrain system under the three working modes is the same which is only related to inertia and meshing stiffness of planetary gear system. The non-repetitive frequency and corresponding vibration modes under the electric mode and parking charging mode are both close.

The camera-based advanced driver assistance systems (ADAS) like lane departure warning system (LDWS) and lane keeping assist (LKA) can make vehicles safer and driving easier. Lane detection is indispensable for these lane-based systems for achieving vehicle local localization and behavior prediction. Since the vision is vulnerable to the variable environment conditions such as bad weather, occlusions and illumination, the robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend the previously lane detection and tracking process from the space domain to the temporal-spatial domain by using a more robust and general multi-lane model. First, new slice images containing temporal information are generated from image sequences. Instead of binarization process, we use a more general detector for extracting the lane marker candidates with prior knowledge to generate the binary slice image.