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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.

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.

Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance.

In view of the vibration and noise problem in the electric drive system, the vibration characteristics of its high-speed reducer are analyzed and studied. Through the vibration and noise bench test of the integrated electric drive system, the contribution of high-speed reducer gear meshing order vibration noise to the vibration noise of the electric drive system was studied. A rigid-flexible coupling dynamic model of high-speed reducer was established, and the accuracy of the model was verified. At the same time, based on the gear modification theory, the effects of different gear modification parameters on the peak-to-peak value of high-speed reducer gear transmission error, the amplitude of each order harmonic of the transmission error, and the vibration acceleration response of the high-speed reducer shell surface were studied. Genetic algorithm was used to optimize the gear modification parameters, and the optimization method was simulated and verified.

The noise, vibration, and harshness (NVH), also known as noise and vibration (N&V), is a critical feature for customers to assess the performance and quality of vehicles. NVH characteristics are higher among factors that customers use to judge the vehicle's quality.This book sets out to introduce the basic concepts, principles, and applications of the NVH development and refi nement of Battery Electric Vehicles (BEV), Hybrid Electric Vehicles (HEV), and Fuel Cell Electric Vehicles. Each type comes with its own set of challenges.